Learning Philosophy:
- The Power of Tiny Gains
- Master Adjacent Disciplines
- T-shaped skills
- Data Scientists Should Be More End-to-End
- Just in Time Learning
- Have basic business understanding
- Be able to frame an ML problem
- Be familiar with data ethics
- Be able to import data from multiple sources
- Be able to annotate data efficiently
- Be able to manipulate data with Numpy
- Be able to manipulate data with Pandas
- Be able to manipulate data in spreadsheets
- Be able to manipulate data in databases
- Be able to use the command line
- Be able to perform feature engineering
- Be able to experiment in a notebook
- Be able to visualize data
- Be able to to read research papers
- Be able to model problems mathematically
- Be able to structure machine learning projects
- Be able to utilize version control
- Be able to use data version control
- Be familiar with fundamental of ML and DL
- Be able to implement models in scikit-learn
- Be able to implement models in Tensorflow and Keras
- Be able to implement models in PyTorch
- Be able to implement models using cloud services
- Be able to apply unsupervised learning algorithms
- Be able to implement NLP models
- Be familiar with Recommendation Systems
- Be able to implement computer vision models
- Be able to model graphs and network data
- Be able to implement models for timeseries and forecasting
- Be familiar with Reinforcement Learning
- Be able to optimize performance metric
- Be familiar with literature on model interpretability
- Be able to optimize model size for production
- Be able to write unit tests
- Be able to serve models as REST APIs
- Be able to build interactive UI for models
- Be able to deploy model to production
- Be able to perform load testing
- Be able to perform A/B testing
- Be proficient in Python
- Be familiar with compiled languages
- Have a general understanding of other parts of the stack
- Be familiar with fundamental Computer Science concepts
- Be able to apply proper software engineering process
- Be able to efficiently use a text editor
- Be able to communicate and collaborate well
- Be familiar with the hiring pipeline
- Broaden Perspective
- Book: Delivering Happiness
- Book: Good to Great: Why Some Companies Make the Leap...And Others Don't
- Book: Hello, Startup: A Programmer's Guide to Building Products, Technologies, and Teams
- Book: How Google Works
- Book: Learn to Earn: A Beginner's Guide to the Basics of Investing and Business
- Book: Rework
- Book: The Airbnb Story
- Book: The Personal MBA
- Facebook: Digital marketing: get started
- Facebook: Digital marketing: go further
- Google Analytics for Beginners
- Google: Fundamentals of Digital Marketing
- Moz: The Beginner's Guide to SEO
- Smartly: Marketing Fundamentals
- Treehouse: SEO Basics
- Udacity: App Monetization
- Udacity: App Marketing
- Udacity: Get Your Startup Started
- Udacity: How to Build a Startup
- Youtube: SEO Unlocked
- Welcome to the SEO Unlocked
0:10:09
- Introduction to SEO and Why It's Important
0:10:29
- Keyword Research Part 1
0:19:20
- Keyword Research Part 2
0:09:56
- On-page and technical SEO Part 1
0:22:58
- On-page and technical SEO Part 2
0:12:16
- Mastering Technical SEO Audits
0:16:35
- Content Marketing Part 1
0:24:09
- Advanced Content Marketing Tactics
0:09:54
- The 10 Commandments of Content Marketing
0:19:01
- How to Edit Your Content For SEO
0:10:59
- Discover Your Competitive Strategy
0:09:12
- Over 4 Million Backlinks Built With This Simple Process
0:11:09
- How to Get POWERFUL Backlinks for Faster Rankings
0:09:40
- Get THOUSANDS of Backlinks On Semi-Autopilot
0:06:32
- How To Get The Most Out Of Google Analytics
0:07:45
- How to Setup Google Search Console
0:09:21
- How to Use Advanced Features in Google Analytics
0:10:52
- A Deep Dive Into Branding, Data & Experience
0:14:03
- How To Create A Compelling Brand
0:05:52
- Designing Your Customer Experience & Case Studies
0:07:32
- Welcome to the SEO Unlocked
- AWS: Types of Machine Learning Solutions
- Article: Apply Machine Learning to your Business
- Article: Resilience and Vibrancy: The 2020 Data & AI Landscape
- Article: Software 2.0
- Article: How Facebook uses super-efficient AI models to detect hate speech
- Article: Highlights from ICML 2020
- Article: Recent Advances in Google Translate
- Article: A Peek at Trends in Machine Learning
- Article: How to deliver on Machine Learning projects
- Article: Data Science as a Product
- Article: Customer service is full of machine learning problems
- Article: Cannes: How ML saves us $1.7M a year on document previews
- Article: Machine Learning @ Monzo in 2020
- Book: AI Superpowers: China, Silicon Valley, and the New World Order
- Book: A Human's Guide to Machine Intelligence
- Book: The Future Computed
- Book: Machine Learning Yearning by Andrew Ng
- Book: Prediction Machines: The Simple Economics of Artificial Intelligence
- Book: Building Machine Learning Powered Applications: Going from Idea to Product
- Coursera: AI For Everyone
- Datacamp: Case Studies in Statistical Thinking
- Datacamp: Data Science for Everyone
- Datacamp: Machine Learning with the Experts: School Budgets
- Datacamp: Machine Learning for Everyone
- Datacamp: Analyzing Police Activity with pandas
- Datacamp: HR Analytics in Python: Predicting Employee Churn
- Datacamp: Predicting Customer Churn in Python
- Datacamp: Data Science for Managers
- Facebook: Field Guide to Machine Learning
- Google: Introduction to Machine Learning Problem Framing
- Microsoft: Define an AI strategy to create business value
- Microsoft: Discover ways to foster an AI-ready culture in your business
- Microsoft: Identify guiding principles for responsible AI in your business
- Microsoft: Introduction to AI technology for business leaders
- Pluralsight: How to Think About Machine Learning Algorithms
- State of AI Report 2020
- Udacity: Problem Solving with Advanced Analytics
- Youtube: Vincent Warmerdam: The profession of solving (the wrong problem) | PyData Amsterdam 2019
- Youtube: Making Money from AI by Predicting Sales - Jay's Intro to AI Part 2
- Youtube: How does YouTube recommend videos? - AI EXPLAINED!
0:33:53
- Youtube: How does Google Translate's AI work?
0:15:02
- Youtube: Data Science in Finance
0:17:52
- Youtube: The Age of AI
- How Far is Too Far? | The Age of A.I.
0:34:39
- Healed through A.I. | The Age of A.I.
0:39:55
- Using A.I. to build a better human | The Age of A.I.
0:44:27
- Love, art and stories: decoded | The Age of A.I.
0:38:57
- The 'Space Architects' of Mars | The Age of A.I.
0:30:10
- Will a robot take my job? | The Age of A.I.
0:36:14
- Saving the world one algorithm at a time | The Age of A.I.
0:46:37
- How A.I. is searching for Aliens | The Age of A.I.
0:36:12
- How Far is Too Far? | The Age of A.I.
- Youtube: Gradient Dissent Podcast
- DeepChem creator Bharath Ramsundar on using deep learning for molecules and medicine discovery
0:55:11
- ML Research and Production Pipelines with Chip Huyen
0:43:07
- Product Management for AI with Peter Skomoroch
1:28:14
- Slow down and change one thing at a time - Advancing AI research with Josh Tobin
0:48:19
- Societal Impacts of Artificial Intelligence with Miles Brundage
1:02:25
- Deep Reinforcement Learning and Robotics with Peter Welinder
0:54:22
- Machine learning across industries with Vicki Boykis
0:34:02
- Designing ML models for millions of consumer robots - Angela Bassa and Danielle Dean
0:52:38
- Building trustworthy AI systems and combating potential malicious use – A conversation w/ Jack Clark
0:55:56
- Rachael Tatman - Conversational A.I. and Linguistics
0:36:51
- Nicolas Koumchatzky - Machine Learning in Production for Self Driving Cars
0:44:56
- Brandon Rohrer - Machine Learning in Production for Robots
0:34:31
- DeepChem creator Bharath Ramsundar on using deep learning for molecules and medicine discovery
- Youtube: Accuracy as a Failure
- Youtube: Using Intent Data to Optimize the Self-Solve Experience
- Youtube: Trillions of Questions, No Easy Answers: A (home) movie about how Google Search works
- Youtube: Hugging Face, Transformers | NLP Research and Open Source | Interview with Julien Chaumond
- Youtube: Vincent Warmerdam - Playing by the Rules-Based-Systems | PyData Eindhoven 2020
- Article: How to Detect Bias in AI
- Practical Data Ethics
- Lesson 1: Disinformation
- Lesson 2: Bias & Fairness
- Lesson 3: Ethical Foundations & Practical Tools
- Lesson 4: Privacy and surveillance
- Lesson 4 continued: Privacy and surveillance
- Lesson 5.1: The problem with metrics
- Lesson 5.2: Our Ecosystem, Venture Capital, & Hypergrowth
- Lesson 5.3: Losing the Forest for the Trees, guest lecture by Ali Alkhatib
- Lesson 6: Algorithmic Colonialism, and Next Steps
- Docs: Beautiful Soup Documentation
- Datacamp: Importing Data in Python (Part 2)
- Datacamp: Web Scraping in Python
- Article: Create A Synthetic Image Dataset — The “What”, The “Why” and The “How”
- Article: We need Synthetic Data
- Article: Weak Supervision for Online Discussions
- Youtube: Snorkel: Dark Data and Machine Learning - Christopher Ré
- Youtube: Training a NER Model with Prodigy and Transfer Learning
- Youtube: Training a New Entity Type with Prodigy – annotation powered by active learning
- Youtube: ECCV 2020 WSL tutorial: 4. Human-in-the-loop annotations
- Article: A Visual Intro to NumPy and Data Representation
- Article: NumPy Illustrated: The Visual Guide to NumPy
- Article: NumPy Fundamentals for Data Science and Machine Learning
- Datacamp: Intro to Python for Data Science
- Pluralsight: Working with Multidimensional Data Using NumPy
- Article: Visualizing Pandas' Pivoting and Reshaping Functions
- Article: A Gentle Visual Intro to Data Analysis in Python Using Pandas
- Article: Comprehensive Guide to Grouping and Aggregating with Pandas
- Article: 8 Python Pandas Value_counts() tricks that make your work more efficient
- Datacamp: pandas Foundations
- Datacamp: Pandas Joins for Spreadsheet Users
- Datacamp: Manipulating DataFrames with pandas
- Datacamp: Merging DataFrames with pandas
- Datacamp: Data Manipulation with pandas
- Datacamp: Optimizing Python Code with pandas
- Datacamp: Streamlined Data Ingestion with pandas
- Datacamp: Analyzing Marketing Campaigns with pandas
- edX: Implementing Predictive Analytics with Spark in Azure HDInsight
- Article: Modern Pandas
- Datacamp: Spreadsheet basics
- Datacamp: Data Analysis with Spreadsheets
- Datacamp: Intermediate Spreadsheets for Data Science
- Datacamp: Pivot Tables with Spreadsheets
- Datacamp: Data Visualization in Spreadsheets
- Datacamp: Introduction to Statistics in Spreadsheets
- Datacamp: Conditional Formatting in Spreadsheets
- Datacamp: Marketing Analytics in Spreadsheets
- Datacamp: Error and Uncertainty in Spreadsheets
- edX: Analyzing and Visualizing Data with Excel
- Codecademy: SQL Track
- Datacamp: Intro to SQL for Data Science
- Datacamp: Introduction to MongoDB in Python
- Datacamp: Intermediate SQL
- Datacamp: Exploratory Data Analysis in SQL
- Datacamp: Joining Data in PostgreSQL
- Datacamp: Querying with TransactSQL
- Datacamp: Introduction to Databases in Python
- Datacamp: Reporting in SQL
- Datacamp: Applying SQL to Real-World Problems
- Datacamp: Analyzing Business Data in SQL
- Datacamp: Data-Driven Decision Making in SQL
- Datacamp: Database Design
- Udacity: SQL for Data Analysis
- Udacity: Intro to relational database
- Udacity: Database Systems Concepts & Design
- Article: Streamline your projects using Makefile
- Calmcode: makefiles
- Calmcode: entr
- Codecademy: Learn the Command Line
- Datacamp: Introduction to Shell for Data Science
- Datacamp: Introduction to Bash Scripting
- Datacamp: Data Processing in Shell
- LaunchSchool: Introduction to Commandline
- Learn Enough Command Line to be dangerous
- Thoughtbot: Mastering the Shell
- Thoughtbot: tmux
- Udacity: Linux Command Line Basics
- Udacity: Shell Workshop
- Web Bos: Command Line Power User
- Article: Tips for Advanced Feature Engineering
- Article: Preparing data for a machine learning model
- Article: Feature selection for a machine learning model
- Article: Learning from imbalanced data
- Article: Hacker's Guide to Data Preparation for Machine Learning
- Article: Practical Guide to Handling Imbalanced Datasets
- Datacamp: Analyzing Social Media Data in Python
- Datacamp: Dimensionality Reduction in Python
- Datacamp: Preprocessing for Machine Learning in Python
- Datacamp: Data Types for Data Science
- Datacamp: Cleaning Data in Python
- Datacamp: Feature Engineering for Machine Learning in Python
- Datacamp: Importing & Managing Financial Data in Python
- Datacamp: Manipulating Time Series Data in Python
- Datacamp: Working with Geospatial Data in Python
- Datacamp: Analyzing IoT Data in Python
- Datacamp: Dealing with Missing Data in Python
- Datacamp: Exploratory Data Analysis in Python
- edX: Data Science Essentials
- Google: Feature Engineering
- Udacity: Creating an Analytical Dataset
- Article: Securely storing configuration credentials in a Jupyter Notebook
- Article: Automatically Reload Modules with %autoreload
- Calmcode: ipywidgets
- Documentation: Jupyter Lab
- Pluralsight: Getting Started with Jupyter Notebook and Python
- Youtube: William Horton - A Brief History of Jupyter Notebooks
- Youtube: I Like Notebooks
- Youtube: I don't like notebooks.- Joel Grus (Allen Institute for Artificial Intelligence)
- Youtube: Ryan Herr - After model.fit, before you deploy| JupyterCon 2020
- Youtube: nbdev live coding with Hamel Husain
- Youtube: How to Use JupyterLab
- Article: Creating a Catchier Word Cloud Presentation
- Article: Effectively Using Matplotlib
- Datacamp: Introduction to Data Visualization with Python
- Datacamp: Introduction to Seaborn
- Datacamp: Introduction to Matplotlib
- Datacamp: Intermediate Data Visualization with Seaborn
- Datacamp: Visualizing Time Series Data in Python
- Datacamp: Improving Your Data Visualizations in Python
- Datacamp: Visualizing Geospatial Data in Python
- Datacamp: Interactive Data Visualization with Bokeh
- Udacity: Data Visualization in Tableau
- Youtube: Jake VanderPlas - Exploratory Data Visualization with Vega, Vega-Lite, and Altair - PyCon 2018
- UWData: Data Visualization Curriculum
- Paper: A Neural Probabilistic Language Model
- Paper: Efficient Estimation of Word Representations in Vector Space
- Paper: Sequence to Sequence Learning with Neural Networks
- Paper: Neural Machine Translation by Jointly Learning to Align and Translate
- Paper: Attention Is All You Need
- Paper: BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
- Paper: XLNet: Generalized Autoregressive Pretraining for Language Understanding
- Paper: Synonyms Based Term Weighting Scheme: An Extension to TF.IDF
- Paper: RoBERTa: A Robustly Optimized BERT Pretraining Approach
- Paper: GLUE: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding
- Paper: Amazon.com Recommendations Item-to-Item Collaborative Filtering
- Paper: Collaborative Filtering for Implicit Feedback Datasets
- Paper: BPR: Bayesian Personalized Ranking from Implicit Feedback
- Paper: Factorization Machines
- Paper: Wide & Deep Learning for Recommender Systems
- Paper: Neural Factorization Machines for Sparse Predictive Analytics
- Paper: Multiword Expressions: A Pain in the Neck for NLP
- Paper: PyTorch: An Imperative Style, High-Performance Deep Learning Library
- Paper: ALBERT: A LITE BERT FOR SELF-SUPERVISED LEARNING OF LANGUAGE REPRESENTATIONS
- Paper: Self-supervised Visual Feature Learning with Deep Neural Networks: A Survey
- Paper: A Simple Framework for Contrastive Learning of Visual Representations
- Paper: Self-Supervised Learning of Pretext-Invariant Representations
- Paper: FixMatch: Simplifying Semi-Supervised Learning with Consistency and Confidence
- Paper: Self-Labelling via Simultalaneous Clustering and Representation Learning
- Paper: A Survey on Contextual Embeddings
- Paper: A survey on Semi-, Self- and Unsupervised Techniques in Image Classification
- Paper: Shortcut Learning in Deep Neural Networks
- Paper: Multi-document Summarization by using TextRank and Maximal Marginal Relevance for Text in Bahasa Indonesia
- Paper: Train Once, Test Anywhere: Zero-Shot Learning for Text Classification
- Paper: Zero-shot Text Classification With Generative Language Models
- Paper: How to Fine-Tune BERT for Text Classification?
- Paper: Universal Sentence Encoder
- Paper: Enriching Word Vectors with Subword Information
- Paper: Deep Learning Based Text Classification: A Comprehensive Review
- Paper: Beyond Accuracy: Behavioral Testing of NLP models with CheckList
- Paper: Pseudo-Label : The Simple and Efficient Semi-Supervised Learning Method for Deep Neural Networks
- Paper: Temporal Ensembling for Semi-Supervised Learning
- Paper: Boosting Self-Supervised Learning via Knowledge Transfer
- Paper: Follow-up Question Generation
- Paper: The Hardware Lottery
- Paper: Question Generation via Overgenerating Transformations and Ranking
- Paper: Good Question! Statistical Ranking for Question Generation
- Paper: Towards ML Engineering: A Brief History Of TensorFlow Extended (TFX)
- Paper: Interpolation Consistency Training for Semi-Supervised Learning
- Paper: Neural Text Generation: A Practical Guide
- Paper: Pest Management In Cotton Farms: An AI-System Case Study from the Global South
- Paper: BERT2DNN: BERT Distillation with Massive Unlabeled Data for Online E-Commerce Search
- Paper: On the surprising similarities between supervised and self-supervised models
- Paper: All-but-the-Top: Simple and Effective Postprocessing for Word Representations
- Paper: Simple and Effective Dimensionality Reduction for Word Embeddings
- Paper: A Survey on Visual Transformer
- Youtube: mixup: Beyond Empirical Risk Minimization (Paper Explained)
- 3Blue1Brown: Essence of Calculus
- The Essence of Calculus, Chapter 1
0:17:04
- The paradox of the derivative | Essence of calculus, chapter 2
0:17:57
- Derivative formulas through geometry | Essence of calculus, chapter 3
0:18:43
- Visualizing the chain rule and product rule | Essence of calculus, chapter 4
0:16:52
- What's so special about Euler's number e? | Essence of calculus, chapter 5
0:13:50
- Implicit differentiation, what's going on here? | Essence of calculus, chapter 6
0:15:33
- Limits, L'Hôpital's rule, and epsilon delta definitions | Essence of calculus, chapter 7
0:18:26
- Integration and the fundamental theorem of calculus | Essence of calculus, chapter 8
0:20:46
- What does area have to do with slope? | Essence of calculus, chapter 9
0:12:39
- Higher order derivatives | Essence of calculus, chapter 10
0:05:38
- Taylor series | Essence of calculus, chapter 11
0:22:19
- What they won't teach you in calculus
0:16:22
- The Essence of Calculus, Chapter 1
- 3Blue1Brown: Essence of linear algebra
- Vectors, what even are they? | Essence of linear algebra, chapter 1
0:09:52
- Linear combinations, span, and basis vectors | Essence of linear algebra, chapter 2
0:09:59
- Linear transformations and matrices | Essence of linear algebra, chapter 3
0:10:58
- Matrix multiplication as composition | Essence of linear algebra, chapter 4
0:10:03
- Three-dimensional linear transformations | Essence of linear algebra, chapter 5
0:04:46
- The determinant | Essence of linear algebra, chapter 6
0:10:03
- Inverse matrices, column space and null space | Essence of linear algebra, chapter 7
0:12:08
- Nonsquare matrices as transformations between dimensions | Essence of linear algebra, chapter 8
0:04:27
- Dot products and duality | Essence of linear algebra, chapter 9
0:14:11
- Cross products | Essence of linear algebra, Chapter 10
0:08:53
- Cross products in the light of linear transformations | Essence of linear algebra chapter 11
0:13:10
- Cramer's rule, explained geometrically | Essence of linear algebra, chapter 12
0:12:12
- Change of basis | Essence of linear algebra, chapter 13
0:12:50
- Eigenvectors and eigenvalues | Essence of linear algebra, chapter 14
0:17:15
- Abstract vector spaces | Essence of linear algebra, chapter 15
0:16:46
- Vectors, what even are they? | Essence of linear algebra, chapter 1
- 3Blue1Brown: Neural networks
- Article: A Visual Tour of Backpropagation
- Article: Entropy, Cross Entropy, and KL Divergence
- Article: Introduction to Linear Algebra for Applied Machine Learning with Python
- Article: Entropy of a probability distribution — in layman’s terms
- Article: KL Divergence — in layman’s terms
- Article: Probability Distributions
- Article: Relearning Matrices as Linear Functions
- Article: You Could Have Come Up With Eigenvectors - Here's How
- Article: PageRank - How Eigenvectors Power the Algorithm Behind Google Search
- Article: Interactive Visualization of Why Eigenvectors Matter
- Article: Cross-Entropy and KL Divergence
- Article: Why Randomness Is Information?
- Article: Basic Probability Theory
- Book: Basics of Linear Algebra for Machine Learning
- Datacamp: Introduction to Statistics in Python
- Datacamp: Foundations of Probability in Python
- Datacamp: Statistical Thinking in Python (Part 1)
- Datacamp: Statistical Thinking in Python (Part 2)
- Datacamp: Statistical Simulation in Python
- edX: Essential Statistics for Data Analysis using Excel
- Computational Linear Algebra for Coders
- Khan Academy: Precalculus
- Khan Academy: Probability
- Khan Academy: Differential Calculus
- Khan Academy: Multivariable Calculus
- Khan Academy: Linear Algebra
- MIT: 18.06 Linear Algebra (Professor Strang)
- 1. The Geometry of Linear Equations
0:39:49
- 2. Elimination with Matrices.
0:47:41
- 3. Multiplication and Inverse Matrices
0:46:48
- 4. Factorization into A = LU
0:48:05
- 5. Transposes, Permutations, Spaces R^n
0:47:41
- 6. Column Space and Nullspace
0:46:01
- 9. Independence, Basis, and Dimension
0:50:14
- 10. The Four Fundamental Subspaces
0:49:20
- 11. Matrix Spaces; Rank 1; Small World Graphs
0:45:55
- 14. Orthogonal Vectors and Subspaces
0:49:47
- 15. Projections onto Subspaces
0:48:51
- 16. Projection Matrices and Least Squares
0:48:05
- 17. Orthogonal Matrices and Gram-Schmidt
0:49:09
- 21. Eigenvalues and Eigenvectors
0:51:22
- 22. Diagonalization and Powers of A
0:51:50
- 24. Markov Matrices; Fourier Series
0:51:11
- 25. Symmetric Matrices and Positive Definiteness
0:43:52
- 27. Positive Definite Matrices and Minima
0:50:40
- 29. Singular Value Decomposition
0:40:28
- 30. Linear Transformations and Their Matrices
0:49:27
- 31. Change of Basis; Image Compression
0:50:13
- 33. Left and Right Inverses; Pseudoinverse
0:41:52
- 1. The Geometry of Linear Equations
- StatQuest: Statistics Fundamentals
- StatQuest: Histograms, Clearly Explained
0:03:42
- StatQuest: What is a statistical distribution?
0:05:14
- StatQuest: The Normal Distribution, Clearly Explained!!!
0:05:12
- Statistics Fundamentals: Population Parameters
0:14:31
- Statistics Fundamentals: The Mean, Variance and Standard Deviation
0:14:22
- StatQuest: What is a statistical model?
0:03:45
- StatQuest: Sampling A Distribution
0:03:48
- Hypothesis Testing and The Null Hypothesis
0:14:40
- Alternative Hypotheses: Main Ideas!!!
0:09:49
- p-values: What they are and how to interpret them
0:11:22
- How to calculate p-values
0:25:15
- p-hacking: What it is and how to avoid it!
0:13:44
- Statistical Power, Clearly Explained!!!
0:08:19
- Power Analysis, Clearly Explained!!!
0:16:44
- Covariance and Correlation Part 1: Covariance
0:22:23
- Covariance and Correlation Part 2: Pearson's Correlation
0:19:13
- StatQuest: R-squared explained
0:11:01
- The Central Limit Theorem
0:07:35
- StatQuickie: Standard Deviation vs Standard Error
0:02:52
- StatQuest: The standard error
0:11:43
- StatQuest: Technical and Biological Replicates
0:05:27
- StatQuest - Sample Size and Effective Sample Size, Clearly Explained
0:06:32
- Bar Charts Are Better than Pie Charts
0:01:45
- StatQuest: Boxplots, Clearly Explained
0:02:33
- StatQuest: Logs (logarithms), clearly explained
0:15:37
- StatQuest: Confidence Intervals
0:06:41
- StatQuickie: Thresholds for Significance
0:06:40
- StatQuickie: Which t test to use
0:05:10
- StatQuest: One or Two Tailed P-Values
0:07:05
- The Binomial Distribution and Test, Clearly Explained!!!
0:15:46
- StatQuest: Quantiles and Percentiles, Clearly Explained!!!
0:06:30
- StatQuest: Quantile-Quantile Plots (QQ plots), Clearly Explained
0:06:55
- StatQuest: Quantile Normalization
0:04:51
- StatQuest: Probability vs Likelihood
0:05:01
- StatQuest: Maximum Likelihood, clearly explained!!!
0:06:12
- Maximum Likelihood for the Exponential Distribution, Clearly Explained! V2.0
0:09:39
- Why Dividing By N Underestimates the Variance
0:17:14
- Maximum Likelihood for the Binomial Distribution, Clearly Explained!!!
0:11:24
- Maximum Likelihood For the Normal Distribution, step-by-step!
0:19:50
- StatQuest: Odds and Log(Odds), Clearly Explained!!!
0:11:30
- StatQuest: Odds Ratios and Log(Odds Ratios), Clearly Explained!!!
0:16:20
- Live 2020-04-20!!! Expected Values
0:33:00
- StatQuest: Histograms, Clearly Explained
- Udacity: Algebra Review
- Udacity: Differential Equations in Action
- Udacity: Eigenvectors and Eigenvalues
- Udacity: Linear Algebra Refresher
- Udacity: Statistics
- Udacity: Intro to Descriptive Statistics
- Udacity: Intro to Inferential Statistics
- Youtube: Principal Component Analysis (PCA) - THE MATH YOU SHOULD KNOW!
0:10:06
- Youtube: Support Vector Machines - THE MATH YOU SHOULD KNOW
0:11:21
- Youtube: The Kernel Trick - THE MATH YOU SHOULD KNOW!
0:07:29
- Youtube: Logistic Regression - THE MATH YOU SHOULD KNOW!
0:09:14
- Youtube: But what is a Neural Network? - THE MATH YOU SHOULD KNOW!
0:19:07
- Youtube: Visualizing Deep Learning
- Article: I trained a model. What is next?
- Article: pydantic
- Article: Always start with a stupid model, no exceptions.
- Article: Organizing machine learning projects: project management guidelines
- Article: Most impactful AI trends of 2018: the rise of ML Engineering
- Article: Building machine learning products: a problem well-defined is a problem half-solved.
- Article: Simple considerations for simple people building fancy neural networks
- Article: Logging and Debugging in Machine Learning - How to use Python debugger and the logging module to find errors in your AI application
- Article: How to Unit Test Deep Learning: Tests in TensorFlow, mocking and test coverage
- Article: Best practices to write Deep Learning code: Project structure, OOP, Type checking and documentation
- Article: Deep Learning in Production: Laptop set up and system design
- Article: Configuring Google Colab Like A Pro
- Article: Stop using print, start using loguru in Python
- Coursera: Structuring Machine Learning Projects
- Datacamp: Conda Essentials
- Datacamp: Conda for Building & Distributing Packages
- Datacamp: Creating Robust Python Workflows
- Datacamp: Software Engineering for Data Scientists in Python
- Datacamp: Designing Machine Learning Workflows in Python
- Datacamp: Object-Oriented Programming in Python
- Datacamp: Command Line Automation in Python
- Datacamp: Introduction to Data Engineering
- Datacamp: Experimental Design in Python
- Full Stack Deep Learning Bootcamp: March 2019
- Lecture 1: Introduction to Deep Learning
- Lecture 2: Setting Up Machine Learning Projects
- Lecture 3: Introduction to the Text Recognizer Project
- Lecture 4: Infrastructure and Tooling
- Lecture 5: Tracking Experiments
- Lecture 6: Data Management
- Lecture 7: Machine Learning Teams
- Lecture 9: Lukas Biewald
- Lecture 10: Troubleshooting Deep Neural Networks
- Lecture 11: Labs 6-9: Detection, Data Labeling, Testing and Deployment
- Lecture 12: Testing and Deployment
- Lecture 13: Research Directions
- Lecture 14: Jeremy Howard
- Lecture 15: Richard Socher
- Guest Lecture - Chip Huyen - Machine Learning Interviews - Full Stack Deep Learning
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0:48:16
- Lecture 2: Shell Tools and Scripting (2020)
0:48:55
- Lecture 3: Editors (vim) (2020)
0:48:26
- Lecture 4: Data Wrangling (2020)
0:50:03
- Lecture 5: Command-line Environment (2020)
0:56:06
- Lecture 6: Version Control (git) (2020)
1:24:59
- Lecture 7: Debugging and Profiling (2020)
0:54:13
- Lecture 8: Metaprogramming (2020)
0:49:52
- Lecture 9: Security and Cryptography (2020)
1:00:59
- Lecture 10: Potpourri (2020)
0:57:54
- Lecture 11: Q&A (2020)
0:53:52
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- Udacity: Writing READMEs
- Youtube: Weights and Biases Tutorial
- Youtube: Integrate Weights & Biases with PyTorch
- Youtube: Log (Almost) Anything with Weights & Biases
- Youtube: MLOps Tutorials
- Youtube: Data Engineering + ML + Software Engineering // Satish Chandra Gupta // MLOps Coffee Sessions #16
- Youtube: OO Design and Testing Patterns for Machine Learning with Chris Gerpheide
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0:11:49
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1:00:38
- Stanford MLSys Seminar Episode 2: Matei Zaharia
0:59:44
- Stanford MLSys Seminar Episode 3: Virginia Smith
1:00:55
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1:13:34
- Stanford MLSys Seminar Episode 5: Chip Huyen
1:06:44
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- Codecademy: Learn Git
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- Datacamp: Introduction to Git for Data Science
- Learn enough git to be dangerous
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- Thoughtbot: Mastering Git
- Udacity: GitHub & Collaboration
- Udacity: How to Use Git and GitHub
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- Youtube: 045 Introduction to Git LFS
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- Article: Naive Bayes classification
- Article: Linear regression
- Article: Polynomial regression
- Article: Logistic regression
- Article: Decision trees
- Article: K-nearest neighbors
- Article: Support Vector Machines
- Article: Random forests
- Article: Boosted trees
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- Article: Neural networks: training with backpropagation
- Article: Gradient descent
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- Article: Neural Network from scratch-part 1
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- Article: Perceptron to Deep-Neural-Network
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- AWS: The Elements of Data Science
- AWS: Understanding Neural Networks
- Book: Approaching (Almost) Any Machine Learning Problem
- Book: Pattern Recognition and Machine Learning
- Book: Grokking Deep Learning
- Book: Make Your Own Neural Network
- Coursera: Neural Networks and Deep Learning
- Datacamp: AI Fundamentals
- Datacamp: Extreme Gradient Boosting with XGBoost
- Datacamp: Ensemble Methods in Python
- Datacamp: Foundations of Predictive Analytics in Python (Part 1)
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- DeepMind: DeepMind x UCL | Deep Learning Lecture Series 2020
- DeepMind x UCL | Deep Learning Lectures | 1/12 | Intro to Machine Learning & AI
1:25:17
- DeepMind x UCL | Deep Learning Lectures | 2/12 | Neural Networks Foundations
1:24:12
- DeepMind x UCL | Deep Learning Lectures | 3/12 | Convolutional Neural Networks for Image Recognition
1:20:19
- DeepMind x UCL | Deep Learning Lectures | 4/12 | Advanced Models for Computer Vision
1:33:37
- DeepMind x UCL | Deep Learning Lectures | 5/12 | Optimization for Machine Learning
1:30:21
- DeepMind x UCL | Deep Learning Lectures | 6/12 | Sequences and Recurrent Networks
1:20:27
- DeepMind x UCL | Deep Learning Lectures | 7/12 | Deep Learning for Natural Language Processing
1:32:29
- DeepMind x UCL | Deep Learning Lectures | 8/12 | Attention and Memory in Deep Learning
1:36:04
- DeepMind x UCL | Deep Learning Lectures | 9/12 | Generative Adversarial Networks
1:42:26
- DeepMind x UCL | Deep Learning Lectures | 10/12 | Unsupervised Representation Learning
1:44:40
- DeepMind x UCL | Deep Learning Lectures | 11/12 | Modern Latent Variable Models
1:28:26
- DeepMind x UCL | Deep Learning Lectures | 12/12 | Responsible Innovation
1:02:28
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- Elements of AI
- edX: Principles of Machine Learning
- edX: Data Science Essentials
- Fast.ai: Deep Learning for Coder (2020)
- MIT: 6.S191: Introduction to Deep Learning
- MIT Introduction to Deep Learning | 6.S191
0:52:51
- Recurrent Neural Networks | MIT 6.S191
0:45:28
- Convolutional Neural Networks | MIT 6.S191
0:37:20
- Deep Generative Modeling | MIT 6.S191
0:40:39
- Reinforcement Learning | MIT 6.S191
0:44:11
- Deep Learning New Frontiers | MIT 6.S191
0:38:10
- Neurosymbolic AI | MIT 6.S191
0:41:10
- Generalizable Autonomy for Robot Manipulation | MIT 6.S191
0:47:00
- Neural Rendering | MIT 6.S191
0:36:44
- Machine Learning for Scent | MIT 6.S191
0:38:51
- MIT Introduction to Deep Learning | 6.S191
- Pluralsight: Deep Learning: The Big Picture
- StatQuest: Machine Learning
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0:12:45
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0:06:04
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0:07:12
- Machine Learning Fundamentals: Sensitivity and Specificity
0:11:46
- Machine Learning Fundamentals: Bias and Variance
0:06:36
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0:16:26
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0:09:21
- StatQuest: Linear Models Pt.1 - Linear Regression
0:27:26
- StatQuest: Odds and Log(Odds), Clearly Explained!!!
0:11:30
- StatQuest: Odds Ratios and Log(Odds Ratios), Clearly Explained!!!
0:16:20
- StatQuest: Logistic Regression
0:08:47
- Logistic Regression Details Pt1: Coefficients
0:19:02
- Logistic Regression Details Pt 2: Maximum Likelihood
0:10:23
- Logistic Regression Details Pt 3: R-squared and p-value
0:15:25
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0:18:39
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0:06:18
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0:20:26
- Regularization Part 2: Lasso (L1) Regression
0:08:19
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0:09:05
- Regularization Part 3: Elastic Net Regression
0:05:19
- StatQuest: Principal Component Analysis (PCA), Step-by-Step
0:21:57
- StatQuest: PCA main ideas in only 5 minutes!!!
0:06:04
- StatQuest: PCA - Practical Tips
0:08:19
- StatQuest: PCA in Python
0:11:37
- StatQuest: Linear Discriminant Analysis (LDA) clearly explained.
0:15:12
- StatQuest: MDS and PCoA
0:08:18
- StatQuest: t-SNE, Clearly Explained
0:11:47
- StatQuest: Hierarchical Clustering
0:11:19
- StatQuest: K-means clustering
0:08:57
- StatQuest: K-nearest neighbors, Clearly Explained
0:05:30
- Naive Bayes, Clearly Explained!!!
0:15:12
- Gaussian Naive Bayes, Clearly Explained!!!
0:09:41
- StatQuest: Decision Trees
0:17:22
- StatQuest: Decision Trees, Part 2 - Feature Selection and Missing Data
0:05:16
- Regression Trees, Clearly Explained!!!
0:22:33
- How to Prune Regression Trees, Clearly Explained!!!
0:16:15
- StatQuest: Random Forests Part 1 - Building, Using and Evaluating
0:09:54
- StatQuest: Random Forests Part 2: Missing data and clustering
0:11:53
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0:18:23
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0:23:54
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0:10:53
- AdaBoost, Clearly Explained
0:20:54
- Gradient Boost Part 1: Regression Main Ideas
0:15:52
- Gradient Boost Part 2: Regression Details
0:26:45
- Gradient Boost Part 3: Classification
0:17:02
- Gradient Boost Part 4: Classification Details
0:36:59
- Bam!!! Clearly Explained!!!
0:02:49
- Support Vector Machines, Clearly Explained!!!
0:20:32
- Support Vector Machines Part 2: The Polynomial Kernel
0:07:15
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0:15:52
- XGBoost Part 1: Regression
0:25:46
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0:25:17
- XGBoost Part 3: Mathematical Details
0:27:24
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0:24:27
- StatQuest: Fiitting a curve to data, aka lowess, aka loess
0:10:10
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0:14:31
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0:20:16
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1:06:23
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- Udacity: Deep Learning
- Udacity: A Friendly Introduction to Machine Learning
- Udacity: Intro to Data Analysis
- Udacity: Intro to Data Science
- Udacity: Intro to Machine Learning
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- Youtube: Neural Networks from Scratch in Python
- Neural Networks from Scratch - P.1 Intro and Neuron Code
0:16:59
- Neural Networks from Scratch - P.2 Coding a Layer
0:15:06
- Neural Networks from Scratch - P.3 The Dot Product
0:25:17
- Neural Networks from Scratch - P.4 Batches, Layers, and Objects
0:33:46
- Neural Networks from Scratch - P.5 Hidden Layer Activation Functions
0:40:05
- Neural Networks from Scratch - P.1 Intro and Neuron Code
- Youtube: Visualizing Deep Learning
- Youtube: Deep Double Descent
- Youtube: How do we check if a neural network has learned a specific phenomenon?
- Article: Label Smoothing Explained using Microsoft Excel
- Youtube: What is Adversarial Machine Learning and what to do about it? – Adversarial example compilation
- Article: Stacking made easy with Sklearn
- Calmcode: human-learn
- Datacamp: Supervised Learning with scikit-learn
- Datacamp: Machine Learning with Tree-Based Models in Python
- Datacamp: Introduction to Linear Modeling in Python
- Datacamp: Linear Classifiers in Python
- Datacamp: Generalized Linear Models in Python
- Pluralsight: Building Machine Learning Models in Python with scikit-learn
- Video: human learn
- Youtube: Applied Machine Learning 2020
- Channel Intro - Applied Machine Learning
0:01:28
- Applied ML 2020 - 01 Introduction
1:16:01
- Applied ML 2020 - 02 Visualization and matplotlib
1:07:30
- Applied ML 2020 - 03 Supervised learning and model validation
1:12:00
- Applied ML 2020 - 04 - Preprocessing
1:07:40
- Applied ML 2020 - 05 - Linear Models for Regression
1:06:54
- Applied ML 2020 - 06 - Linear Models for Classification
1:07:50
- Applied ML 2020 - 07 - Decision Trees and Random Forests
1:07:58
- Applied ML 2020 - 08 - Gradient Boosting
1:02:12
- Applied ML 2020 - 09 - Model Evaluation and Metrics
1:18:23
- Applied ML 2020 - 10 - Calibration, Imbalanced data
1:16:14
- Applied ML 2020 - 11 - Model Inspection and Feature Selection
1:15:15
- Applied ML 2020 - 12 - AutoML (plus some feature selection)
1:25:38
- Applied ML 2020 - 13 - Dimensionality reduction
1:30:34
- Applied ML 2020 - 14 - Clustering and Mixture Models
1:26:33
- Applied ML 2020 - 15 - Working with Text Data
1:27:08
- Applied ML 2020 - 16 - Topic models for text data
1:18:34
- Applied ML 2020 - 17 - Word vectors and document embeddings
1:03:04
- Applied ML 2020 - 18 - Neural Networks
1:19:36
- Applied ML 2020 - 19 - Keras and Convolutional neural nets
1:16:01
- Applied ML 2020 - 20 - Advanced neural networks
1:36:28
- Applied ML 2020 - 21 - Time Series and Forecasting
1:12:36
- Channel Intro - Applied Machine Learning
- Youtube: dabl: Automatic Machine Learning with a Human in the Loop
00:25:43
- Coursera: Introduction to Tensorflow
- Coursera: Convolutional Neural Networks in TensorFlow
- Coursera: Getting Started With Tensorflow 2
- Coursera: Customising your models with TensorFlow 2
- Deeplizard: Keras - Python Deep Learning Neural Network API
- Book: Deep Learning with Python (Page: 276)
- Datacamp: Deep Learning in Python
- Datacamp: Convolutional Neural Networks for Image Processing
- Datacamp: Introduction to TensorFlow in Python
- Datacamp: Introduction to Deep Learning with Keras
- Datacamp: Advanced Deep Learning with Keras
- Google: Intro to Tensorflow
- Google: Machine Learning Crash Course
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- Udacity: Intro to TensorFlow for Deep Learning
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- Documentation: Pytorch Lightning
- Datacamp: Introduction to Deep Learning with PyTorch
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- Udacity: Intro to Deep Learning with PyTorch
- Youtube: PyTorch Lightning 101
- Youtube: SimCLR with PyTorch Lightning
- Youtube: PyTorch Performance Tuning Guide
26:41:00
- Youtube: Skin Cancer Detection with PyTorch
- Youtube: Learn with Lightning
- Youtube: PyTorch Tutorial - RNN & LSTM & GRU - Recurrent Neural Nets
00:15:51
- Youtube: Pytorch Zero to All
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0:10:18
- PyTorch Lecture 02: Linear Model
0:12:52
- PyTorch Lecture 03: Gradient Descent
0:08:24
- PyTorch Lecture 04: Back-propagation and Autograd
0:15:25
- PyTorch Lecture 05: Linear Regression in the PyTorch way
0:11:50
- PyTorch Lecture 06: Logistic Regression
0:10:41
- PyTorch Lecture 07: Wide and Deep
0:10:37
- PyTorch Lecture 08: PyTorch DataLoader
0:06:41
- PyTorch Lecture 09: Softmax Classifier
0:18:47
- PyTorch Lecture 10: Basic CNN
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- PyTorch Lecture 11: Advanced CNN
0:12:58
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0:28:47
- PyTorch Lecture 13: RNN 2 - Classification
0:17:22
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- AWS: Introduction to Amazon Comprehend
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- AWS: Introduction to Amazon Elastic Inference
- AWS: Introduction to Amazon Forecast
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- AWS: Introduction to Amazon Polly
- AWS: Introduction to Amazon SageMaker Ground Truth
- AWS: Introduction to Amazon SageMaker Neo
- AWS: Introduction to Amazon Transcribe
- AWS: Introduction to Amazon Translate
- AWS: Introduction to AWS Marketplace - Machine Learning Category
- AWS: Machine Learning Exam Basics
- AWS: Neural Machine Translation with Sockeye
- AWS: Process Model: CRISP-DM on the AWS Stack
- AWS: Satellite Image Classification in SageMaker
- Datacamp: Introduction to AWS Boto in Python
- edX: Amazon SageMaker: Simplifying Machine Learning Application Development
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- Article: Variational autoencoders
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- Article: Affinity Propagation Algorithm Explained
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- Article: Self-Supervised Representation Learning
- Article: GANs in computer vision - Introduction to generative learning
- Article: GANs in computer vision - self-supervised adversarial training and high-resolution image synthesis with style incorporation
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- Berkeley: Deep Unsupervised Learning Spring 2020
- L1 Introduction -- CS294-158-SP20 Deep Unsupervised Learning -- UC Berkeley, Spring 2020
1:10:02
- L2 Autoregressive Models -- CS294-158-SP20 Deep Unsupervised Learning -- UC Berkeley, Spring 2020
2:27:23
- L3 Flow Models -- CS294-158-SP20 Deep Unsupervised Learning -- UC Berkeley -- Spring 2020
1:56:53
- L4 Latent Variable Models (VAE) -- CS294-158-SP20 Deep Unsupervised Learning -- UC Berkeley
2:19:33
- Lecture 5 Implicit Models -- GANs Part I --- UC Berkeley, Spring 2020
2:32:32
- Lecture 6 Implicit Models / GANs part II --- CS294-158-SP20 Deep Unsupervised Learning -- Berkeley
2:09:14
- Lecture 7 Self-Supervised Learning -- UC Berkeley Spring 2020 - CS294-158 Deep Unsupervised Learning
2:20:41
- L8 Round-up of Strengths and Weaknesses of Unsupervised Learning Methods -- UC Berkeley SP20
0:41:51
- L9 Semi-Supervised Learning and Unsupervised Distribution Alignment -- CS294-158-SP20 UC Berkeley
2:16:00
- L10 Compression -- UC Berkeley, Spring 2020, CS294-158 Deep Unsupervised Learning
3:09:49
- L11 Language Models -- guest instructor: Alec Radford (OpenAI) --- Deep Unsupervised Learning SP20
2:38:19
- L12 Representation Learning for Reinforcement Learning --- CS294-158 UC Berkeley Spring 2020
2:01:56
- L1 Introduction -- CS294-158-SP20 Deep Unsupervised Learning -- UC Berkeley, Spring 2020
- Datacamp: Customer Segmentation in Python
- Datacamp: Unsupervised Learning in Python
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- DeepMind: Inefficient Data Efficiency
- Google: Clustering
- Udacity: Segmentation and Clustering
- Wandb: Unsupervised Visual Representation Learning with SwAV
- Youtube: BYOL: Bootstrap Your Own Latent: A New Approach to Self-Supervised Learning (Paper Explained)
- Youtube: A critical analysis of self-supervision, or what we can learn from a single image (Paper Explained)
- Youtube: Week 10 – Lecture: Self-supervised learning (SSL) in computer vision (CV)
- Youtube: CVPR 2020 Tutorial: Towards Annotation-Efficient Learning
- Youtube: Yuki Asano | Self-Supervision | Self-Labelling | Labelling Unlabelled videos | CV | CTDS.Show #81
- Youtube: Contrastive Clustering with SwAV
- Youtube: Variational Autoencoders - EXPLAINED!
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- Youtube: OptaProAnalyticsForum– Learning to watch football: Self-supervised representations for tracking data
- Youtube: Can a Neural Net tell if an image is mirrored? – Visual Chirality
- Youtube: Deep InfoMax: Learning deep representations by mutual information estimation and maximization
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- Deep Learning: Unsupervised Learning - Part 1
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- ECCV 2020: New Frontiers for Learning with Limited Labels or Data
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- Inverting Neural Networks for Data-free Knowledge Transfer
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0:36:31
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0:43:09
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- Youtube: Marco Cuturi - A Primer on Optimal Transport
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- Youtube: Clustering Algorithms
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- Article: NLP Year In Review
- Article: Using an NLP Q&A System To Study Climate Hazards and Nature-Based Solutions
- Article: Building a sentence embedding index with fastText and BM25
- Article: ML and NLP Research Highlights of 2020
- Article: Hugging Face Reads - 01/2021 - Sparsity and Pruning
- Article: Understanding building blocks of ULMFIT
- Article: Swiss army knife for unsupervised task solving
- Article: Unsupervised synonym harvesting
- Article: Maximizing BERT model performance
- Article: Unsupervised creation of interpretable sentence representations
- Article: Document search with fragment embeddings
- Article: Unsupervised NER using BERT
- Article: Examining BERT’s raw embeddings
- Article: T5 — a model that explores the limits of transfer learning
- Article: T5 — XLNet — a clever language modeling solution
- Article: A review of BERT based models
- Article: Deconstructing BERT
- Article: Text classification from few training examples
- Article: Improved Few-Shot Text classification
- Article: Solving NER with BERT for any entity type with very little training data (compared to past approaches)
- Article: Trends in input representation for state-of-art NLP models (2019)
- Article: Brief review of word embedding families
- Article: GPT-2 A nascent transfer learning method that could eliminate supervised learning in some NLP tasks
- Article: Semantic search using BERT embeddings
- Article: A Survey of Long-Term Context in Transformers
- Article: A Deep Dive into the Reformer
- Article: Representation Learning and Retrieval
- Article: Large Memory Layers with Product Keys
- Article: Pattern-Exploiting Training
- Article: Optimal Transport and the Sinkhorn Transformer
- Article: Talking-Heads Attention
- Article: Rebuilding the most popular spellchecker. Part 1
- Article: Rebuilding the spellchecker, pt.2: Just look in the dictionary, they said!
- Article: Rebuilding the spellchecker, pt.3: Lookup—compounds and solutions
- Article: Rebuilding the spellchecker, pt.4: Introduction to suggest algorithm
- Article: Rebuilding the spellchecker: Hunspell and the order of edits
- Article: Performers: The Kernel Trick, Random Fourier Features, and Attention
- Article: Advance BERT model via transferring knowledge from Cross-Encoders to Bi-Encoders
- Article: Interactive Topic Modeling with BERTopic
- Article: Commonsense Reasoning for Natural Language Processing
- Article: Zero shot NER using RoBERTA
- Article: Understanding Climate Change Domains through Topic Modeling
- Article: Simple PyTorch Transformer Example with Greedy Decoding
- Article: Topic Modeling for Keyword Extraction
- Article: Poor man’s GPT-3: Few shot text generation with T5 Transformer
- Article: Paraphrasing
- Article: Spelling Correction: How to make an accurate and fast corrector
- Article: Text Generation
- Article: How to steal modern NLP systems with gibberish?
- Article: Part of Speech Tagging with Hidden Markov Chain Models
- Article: Building RNNs is Fun with PyTorch and Google Colab
- Article: Automatically Summarize Trump’s State of the Union Address
- Article: Evaluation Metrics for Language Modeling
- Article: Does GPT-2 Know Your Phone Number?
- Article: ColumnTransformer Meets Natural Language Processing
- Article: pyLDAvis: Topic Modelling Exploration Tool That Every NLP Data Scientist Should Know
- Article: Explain NLP models with LIME & SHAP
- Article: When Topic Modeling is Part of the Text Pre-processing
- Article: Automatic Topic Labeling in 2018: History and Trends
- Article: The Annotated Transformer
- Article: Question Classification using Self-Attention Transformer — Part 1
- Article: Question Classification using Self-Attention Transformer — Part 1.1
- Article: Question Classification using Self-Attention Transformer — Part 2
- Article: Question Classification using Self-Attention Transformer — Part 3
- Article: Attention? An Other Perspective!: Part 1
- Article: Attention? An Other Perspective!: Part 2
- Article: Attention? An Other Perspective!: Part 3
- Article: Attention? An Other Perspective!: Part 4
- Article: Attention? An Other Perspective!: Part 5
- Article: On word embeddings - Part 1
- Article: On word embeddings - Part 2: Approximating the Softmax
- Article: On word embeddings - Part 3: The secret ingredients of word2vec
- Article: A survey of cross-lingual word embedding models
- Article: Leveraging Pre-trained Language Model Checkpoints for Encoder-Decoder Models
- Article: Semantic search using BERT embeddings
- Article: GPT-2 A nascent transfer learning method that could eliminate supervised learning in some NLP tasks
- Article: Brief review of word embedding families (2019)
- Article: Trends in input representation for state-of-art NLP models (2019)
- Article: An Overview of Multi-Task Learning in Deep Neural Networks
- Article: Deep Learning for NLP Best Practices
- Article: Learning to select data for transfer learning
- Article: Multi-Task Learning Objectives for Natural Language Processing
- Article: Word embeddings in 2017: Trends and future direction
- Article: Tracking the Progress in Natural Language Processing
- Article: NLP's ImageNet moment has arrived
- Article: A Review of the Neural History of Natural Language Processing
- Article: 10 Exciting Ideas of 2018 in NLP
- Article: The 4 Biggest Open Problems in NLP
- Article: Neural Transfer Learning for Natural Language Processing
- Article: The State of Transfer Learning in NLP
- Article: Unsupervised Cross-lingual Representation Learning
- Article: Why You Should Do NLP Beyond English
- Article: Solving NER with BERT for any entity type with very little training data (compared to past approaches)
- Article: Deconstructing BERT
- Article: A review of BERT based models
- Article: XLNet — a clever language modeling solution
- Article: T5 — a model that explores the limits of transfer learning
- Article: Examining BERT’s raw embeddings
- Article: Unsupervised NER using BERT
- Article: Document search with fragment embeddings
- Article: Unsupervised creation of interpretable sentence representations
- Article: Maximizing BERT model performance
- Article: Character level language model RNN
- Article: How To Create Natural Language Semantic Search For Arbitrary Objects With Deep Learning
- Article: How To Create Data Products That Are Magical Using Sequence-to-Sequence Models
- Article: State-of-the-Art Language Models in 2020
- Article: UNDERSTANDING WORD2VEC THROUGH CULTURAL DIMENSIONS
- Article: How to solve 90% of NLP problems: a step-by-step guide
- Article: The Unreasonable Effectiveness of Recurrent Neural Networks
- Article: How to Apply BERT to Arabic and Other Languages
- Article: The Illustrated Transformer
- Article: Under the Hood of RNNs
- Article: The Annotated GPT-2
- Article: How to Use n-gram Models to Detect Format Errors in Datasets
- Article: How to Use n-gram Models to Detect Format Errors in Datasets
- Article: Shrinking fastText embeddings so that it fits Google Colab
- Article: Perplexity Intuition (and its derivation)
- Article: How To Do Things With Words. And Counters
- Article: ML and NLP Publications in 2020
- Article: How GPT3 Works
- Article: Explaining RNNs without neural networks
- Article: Implementing Bengio’s Neural Probabilistic Language Model (NPLM) using Pytorch
- Doc: Huggingface Summary of the models
- Doc: Summary of the tokenizers
- Article: OpenAI's GPT-3 Language Model: A Technical Overview
- Article: Adapting Text Augmentation to Industry problems
- Article: How I Used Deep Learning To Train A Chatbot To Talk Like Me (Sorta)
- Article: Semantic Entailment
- Article: Feature-based Approach with BERT
- Article: Introduction to recurrent neural networks
- Article: Aspect-Based Opinion Mining (NLP with Python)
- Article: The Transformer Explained
- Article: Controlling Text Generation with Plug and Play Language Models
- Article: What makes a good conversation?
- Article: NLP for Supervised Learning - A Brief Survey
- Article: Generating Questions Using Transformers
- Article: Neural Language Models as Domain-Specific Knowledge Bases
- Article: Understanding BERT’s Semantic Interpretations
- Article: Using NLP (BERT) to improve OCR accuracy
- Article: Hyperparameter Optimization for 🤗Transformers: A guide
- Article: Faster and smaller quantized NLP with Hugging Face and ONNX Runtime
- Article: Learning Word Embedding
- Article: The Transformer Family
- Article: Generalized Language Models
- Article: Document clustering
- Article: The Unreasonable Effectiveness of Recurrent Neural Networks
- Article: LSTM Primer With Real Life Application( DeepMind Kidney Injury Prediction )*
- Article: Making sense of LSTMs by example
- Article: 3 subword algorithms help to improve your NLP model performance
- Article: Exploring LSTMs
- Article: Understanding LSTM Networks
- Article: 74 Summaries of Machine Learning and NLP Research
- Article: Smart Batching Tutorial - Speed Up BERT Training
- Article: GPU Benchmarks for Fine-Tuning BERT
- Article: Existing Tools for Named Entity Recognition
- Article: Domain-Specific BERT Models
- Article: Search (Pt 1) — A Gentle Introduction
- Article: Search (Pt 2) — A Semantic Horse Race
- Article: Search (Pt 3) — Elastic Transformers
- Article: How to Implement a Beam Search Decoder for Natural Language Processing
- Article: Speller100: Zero-shot spelling correction at scale for 100-plus languages
- A friendly introduction to Recurrent Neural Networks
- Book: Embeddings in Natural Language Processing
- Book: Linguistic Fundamentals for Natural Language Processing: 100 Essentials from Morphology and Syntax
- Coursera: Sequence Models
- Coursera: Natural Language Processing in TensorFlow
- CMU: Low-resource NLP Bootcamp 2020
- CMU Low resource NLP Bootcamp 2020 (1): NLP Tasks
1:46:06
- CMU Low resource NLP Bootcamp 2020 (2): Linguistics - Phonology and Morphology
1:24:08
- CMU Low resource NLP Bootcamp 2020 (3): Machine Translation
1:55:59
- CMU Low resource NLP Bootcamp 2020 (4): Linguistics - Syntax and Morphosyntax
2:00:21
- CMU Low resource NLP Bootcamp 2020 (5): Neural Representation Learning
1:19:57
- CMU Low resource NLP Bootcamp 2020 (6): Multilingual NLP
2:04:34
- CMU Low resource NLP Bootcamp 2020 (7): Speech Synthesis
2:22:14
- CMU Low resource NLP Bootcamp 2020 (1): NLP Tasks
- CMU: Neural Nets for NLP 2020
- CMU Neural Nets for NLP 2020 (1): Introduction
1:11:38
- CMU Neural Nets for NLP 2020 (2): Language Modeling, Efficiency/Training Tricks
1:18:31
- CMU Neural Nets for NLP 2020 (3): Convolutional Neural Networks for Text
0:54:45
- CMU Neural Nets for NLP 2020 (4): Recurrent Neural Networks
1:11:28
- CMU Neural Nets for NLP 2020 (5): Efficiency Tricks for Neural Nets
1:05:37
- CMU Neural Nets for NLP 2020 (6): Conditioned Generation
1:07:13
- CMU Neural Nets for NLP 2020 (7): Attention
1:05:26
- CMU Neural Nets for NLP 2020 (8): Distributional Semantics and Word Vectors
1:10:45
- CMU Neural Nets for NLP 2020 (9): Sentence and Contextual Word Representations
1:16:19
- CMU Neural Nets for NLP 2020 (10): Debugging Neural Nets (for NLP)
1:15:26
- CMU Neural Nets for NLP 2020 (11): Structured Prediction with Local Independence Assumptions
1:08:38
- CMU Neural Nets for NLP 2020 (12): Generating Trees Incrementally
1:14:13
- CMU Neural Nets for NLP 2020 (13): Generating Trees Incrementally
0:51:58
- CMU Neural Nets for NLP 2020 (14): Search-based Structured Prediction
1:06:19
- CMU Neural Nets for NLP 2020 (15): Minimum Risk Training and Reinforcement Learning
1:09:16
- CMU Neural Nets for NLP 2020 (16): Advanced Search Algorithms
1:03:02
- CMU Neural Nets for NLP 2020 (17): Adversarial Methods
1:14:55
- CMU Neural Nets for NLP 2020 (18): Models w/ Latent Random Variables
1:13:16
- CMU Neural Nets for NLP 2020 (19): Unsupervised and Semi-supervised Learning of Structure
1:12:47
- CMU Neural Nets for NLP 2020 (20): Multitask and Multilingual Learning
1:02:46
- CMU Neural Nets for NLP 2020 (21): Document Level Models
0:52:04
- CMU Neural Nets for NLP 2020 (22): Neural Nets + Knowledge Bases
1:18:39
- CMU Neural Nets for NLP 2020 (23): Machine Reading w/ Neural Nets
1:06:11
- CMU Neural Nets for NLP 2020 (24): Natural Language Generation
1:21:48
- CMU Neural Nets for NLP 2020 (25): Model Interpretation
1:04:11
- CMU Neural Nets for NLP 2020 (1): Introduction
- CMU Multilingual NLP 2020
- CMU Multilingual NLP 2020 (1): Introduction
1:17:29
- CMU Multilingual NLP 2020 (2): Typology - The Space of Language
0:37:13
- CMU Multilingual NLP 2020 (3): Words, Parts of Speech, Morphology
0:38:58
- CMU Multilingual NLP 2020 (4): Text Classification and Sequence Labeling
0:45:56
- CMU Multilingual NLP 2020 (5): Advanced Text Classification/Labeling
0:49:40
- CMU Multilingual NLP 2020 (6): Translation, Evaluation, and Datasets
0:46:17
- CMU Multilingual NLP 2020 (7): Machine Translation/Sequence-to-sequence Models
0:43:51
- CMU Multilingual NLP 2020 (8): Data Augmentation for Machine Translation
0:24:42
- CMU Multilingual NLP 2020 (9): Language Contact and Similarity Across Languages
0:30:25
- CMU Multilingual NLP 2020 (10): Multilingual Training and Cross-lingual Transfer
0:39:58
- CMU Multilingual NLP 2020 (11): Unsupervised Translation
0:51:17
- CMU Multilingual NLP 2020 (12): Code Switching, Pidgins, and Creoles
0:46:37
- CMU Multilingual NLP 2020 (13): Speech
0:41:16
- CMU Multilingual NLP 2020 (14): Automatic Speech Recognition
0:39:33
- CMU Multilingual NLP 2020 (15): Low Resource ASR
0:43:38
- CMU Multilingual NLP 2020 (16): Text to Speech
0:39:00
- CMU Multilingual NLP 2020 (17): Morphological Analysis and Inflection
0:45:22
- CMU Multilingual NLP 2020 (18): Dependency Parsing
0:38:15
- CMU Multilingual NLP 2020 (19): Data Annotation
0:53:08
- CMU Multilingual NLP 2020 (20): Active Learning
0:28:37
- CMU Multilingual NLP 2020 (21): Information Extraction
0:41:00
- CMU Multilingual NLP 2020 (22): Multilingual NLP for Indigenous Languages
1:21:58
- CMU Multilingual NLP 2020 (23): Universal Translation at Scale
1:27:33
- CMU Multilingual NLP 2020 (1): Introduction
- CMU: MultiModal Machine Learning Fall 2020
- Lecture 1.1: Course Introduction
- Lecture 1.2: Multimodal applications and datasets
- Lecture 2.1: Basic concepts: neural networks
- Lecture 2.2: Basic concepts: network optimization
- Lecture 3.1: Visual unimodal representations
- Lecture 3.2: Language unimodal representations
- Lecture 4.1: Multimodal representation learning
- Lecture 4.2: Coordinated representations
- Lecture 5.1: Multimodal alignment
- Lecture 5.2: Alignment and representation
- Lecture 7.1: Alignment and translation
- Lecture 7.2: Probabilistic graphical models
- Lecture 8.1: Discriminative graphical models
- Lecture 8.2: Deep Generative Models
- Lecture 9.1: Reinforcement learning
- Lecture 9.2: Multimodal RL
- Lecture 10.1: Fusion and co-learning
- Lecture 10.2: New research directions
- Datacamp: Advanced NLP with spaCy
- Datacamp: Building Chatbots in Python
- Datacamp: Clustering Methods with SciPy
- Datacamp: Feature Engineering for NLP in Python
- Datacamp: Machine Translation in Python
- Datacamp: Natural Language Processing Fundamentals in Python
- Datacamp: Natural Language Generation in Python
- Datacamp: RNN for Language Modeling
- Datacamp: Regular Expressions in Python
- Datacamp: Sentiment Analysis in Python
- Datacamp: Spoken Language Processing in Python
- Notebook: NNLM - Predict Next Word
- Notebook: Word2Vec
- Notebook: FastText Sentence Classification
- Notebook: TextCNN - Binary Sentiment Classification
- Notebook: TextRNN - Predict Next Step
- Notebook: TextLSTM - Autocomplete
- Notebook: Bi-LSTM - Predict Next Word in Long Sentence
- Notebook: SeqSeq - Change Word
- Notebook: Seq2Seq with Attention - Translate
- Notebook: Bi-LSTM with Attention - Binary Sentiment Classification
- Notebook: The Transformer - Translate
- Notebook: The Transformer - Greedy Decoder
- Notebook: BERT - NSP and MLM
- RNN and LSTM
- Spacy Tutorial
- Stanford CS224U: Natural Language Understanding | Spring 2019
- Lecture 1 – Course Overview | Stanford CS224U: Natural Language Understanding | Spring 2019
1:12:59
- Lecture 2 – Word Vectors 1 | Stanford CS224U: Natural Language Understanding | Spring 2019
1:17:10
- Lecture 3 – Word Vectors 2 | Stanford CS224U: Natural Language Understanding | Spring 2019
1:16:52
- Lecture 4 – Word Vectors 3 | Stanford CS224U: Natural Language Understanding | Spring 2019
0:38:20
- Lecture 5 – Sentiment Analysis 1 | Stanford CS224U: Natural Language Understanding | Spring 2019
1:10:44
- Lecture 6 – Sentiment Analysis 2 | Stanford CS224U: Natural Language Understanding | Spring 2019
1:03:23
- Lecture 7 – Relation Extraction | Stanford CS224U: Natural Language Understanding | Spring 2019
1:19:04
- Lecture 8 – NLI 1 | Stanford CS224U: Natural Language Understanding | Spring 2019
1:15:02
- Lecture 9 – NLI 2 | Stanford CS224U: Natural Language Understanding | Spring 2019
1:15:35
- Lecture 10 – Grounding | Stanford CS224U: Natural Language Understanding | Spring 2019
1:23:15
- Lecture 11 – Semantic Parsing | Stanford CS224U: Natural Language Understanding | Spring 2019
1:07:05
- Lecture 12 – Evaluation Methods | Stanford CS224U: Natural Language Understanding | Spring 2019
1:18:32
- Lecture 13 – Evaluation Metrics | Stanford CS224U: Natural Language Understanding | Spring 2019
1:11:32
- Lecture 14 – Contextual Vectors | Stanford CS224U: Natural Language Understanding | Spring 2019
1:14:33
- Lecture 15 – Presenting Your Work | Stanford CS224U: Natural Language Understanding | Spring 2019
1:15:11
- Lecture 1 – Course Overview | Stanford CS224U: Natural Language Understanding | Spring 2019
- Stanford CS224N: Stanford CS224N: NLP with Deep Learning | Winter 2019
- Stanford CS224N: NLP with Deep Learning | Winter 2019 | Lecture 1 – Introduction and Word Vectors
1:21:52
- Stanford CS224N: NLP with Deep Learning | Winter 2019 | Lecture 2 – Word Vectors and Word Senses
1:20:43
- Stanford CS224N: NLP with Deep Learning | Winter 2019 | Lecture 3 – Neural Networks
1:18:50
- Stanford CS224N: NLP with Deep Learning | Winter 2019 | Lecture 4 – Backpropagation
1:22:15
- Stanford CS224N: NLP with Deep Learning | Winter 2019 | Lecture 5 – Dependency Parsing
1:20:22
- Stanford CS224N: NLP with Deep Learning | Winter 2019 | Lecture 6 – Language Models and RNNs
1:08:25
- Stanford CS224N: NLP with Deep Learning | Winter 2019 | Lecture 7 – Vanishing Gradients, Fancy RNNs
1:13:23
- Stanford CS224N: NLP with Deep Learning | Winter 2019 | Lecture 8 – Translation, Seq2Seq, Attention
1:16:56
- Stanford CS224N: NLP with Deep Learning | Winter 2019 | Lecture 9 – Practical Tips for Projects
1:22:39
- Stanford CS224N: NLP with Deep Learning | Winter 2019 | Lecture 10 – Question Answering
1:21:01
- Stanford CS224N: NLP with Deep Learning | Winter 2019 | Lecture 11 – Convolutional Networks for NLP
1:20:18
- Stanford CS224N: NLP with Deep Learning | Winter 2019 | Lecture 12 – Subword Models
1:15:30
- Stanford CS224N: NLP with Deep Learning | Winter 2019 | Lecture 13 – Contextual Word Embeddings
1:20:18
- Stanford CS224N: NLP with Deep Learning | Winter 2019 | Lecture 14 – Transformers and Self-Attention
0:53:48
- Stanford CS224N: NLP with Deep Learning | Winter 2019 | Lecture 15 – Natural Language Generation
1:19:37
- Stanford CS224N: NLP with Deep Learning | Winter 2019 | Lecture 16 – Coreference Resolution
1:19:20
- Stanford CS224N: NLP with Deep Learning | Winter 2019 | Lecture 17 – Multitask Learning
1:11:54
- Stanford CS224N: NLP with Deep Learning | Winter 2019 | Lecture 18 – Constituency Parsing, TreeRNNs
1:20:37
- Stanford CS224N: NLP with Deep Learning | Winter 2019 | Lecture 19 – Bias in AI
0:56:03
- Stanford CS224N: NLP with Deep Learning | Winter 2019 | Lecture 20 – Future of NLP + Deep Learning
1:19:15
- Stanford CS224N: NLP with Deep Learning | Winter 2019 | Lecture 1 – Introduction and Word Vectors
- Stanford: Natural Language Processing | Dan Jurafsky, Christopher Manning
- Course Introduction
0:12:52
- Regular Expressions
0:11:25
- Regular Expressions in Practical NLP
0:06:05
- Word Tokenization
0:14:26
- Word Normalization and Stemming
0:11:48
- Sentence Segmentation
0:05:34
- Defining Minimum Edit Distance
0:07:05
- Computing Minimum Edit Distance
0:05:55
- Backtrace for Computing Alignments
0:05:56
- Weighted Minimum Edit Distance
0:02:48
- Minimum Edit Distance in Computational Biology
0:09:30
- Introduction to N grams
0:08:41
- Estimating N gram Probabilities
0:09:38
- Evaluation and Perplexity
0:11:09
- Generalization and Zeros
0:05:15
- Smoothing Add One
0:06:31
- Interpolation
0:10:25
- Good Turing Smoothing
0:15:35
- Kneser Ney Smoothing
0:08:59
- The Spelling Correction Task
0:05:40
- The Noisy Channel Model of Spelling
0:19:31
- Real Word Spelling Correction
0:09:20
- State of the Art Systems
0:07:10
- What is Text Classification
0:08:12
- Naive Bayes
0:03:20
- Formalizing the Naive Bayes Classifier
0:09:29
- Naive Bayes Learning
0:05:23
- Naive Bayes Relationship to Language Modeling
0:04:36
- Multinomial Naive Bayes A Worked Example
0:08:59
- Precision, Recall, and the F measure
0:16:17
- Text Classification Evaluation
0:07:17
- Practical Issues in Text Classification
0:05:57
- What is Sentiment Analysis
0:07:18
- Sentiment Analysis A baseline algorithm
0:13:27
- Sentiment Lexicons
0:08:38
- Learning Sentiment Lexicons
0:14:46
- Other Sentiment Tasks
0:11:02
- Generative vs Discriminative Models
0:07:50
- Making features from text for discriminative NLP models
0:18:12
- Feature Based Linear Classifiers
0:13:35
- Building a Maxent Model The Nuts and Bolts
0:08:05
- Generative vs Discriminative models
0:12:10
- Maximizing the Likelihood
0:10:30
- Introduction to Information Extraction
0:09:19
- Evaluation of Named Entity Recognition
0:06:35
- Sequence Models for Named Entity Recognition
0:15:06
- Maximum Entropy Sequence Models
0:13:02
- What is Relation Extraction
0:09:47
- Using Patterns to Extract Relations
0:06:17
- Supervised Relation Extraction
0:10:51
- Semi Supervised and Unsupervised Relation Extraction
0:09:53
- The Maximum Entropy Model Presentation
0:12:14
- Feature Overlap Feature Interaction
0:12:52
- Conditional Maxent Models for Classification
0:04:11
- Smoothing Regularization Priors for Maxent Models
0:29:24
- An Intro to Parts of Speech and POS Tagging
0:13:19
- Some Methods and Results on Sequence Models for POS Tagging
0:13:04
- Syntactic Structure Constituency vs Dependency
0:08:46
- Empirical Data Driven Approach to Parsing
0:07:11
- The Exponential Problem in Parsing
0:14:31
- Instructor Chat
0:09:03
- CFGs and PCFGs
0:15:30
- Grammar Transforms
0:12:06
- CKY Parsing
0:23:26
- CKY Example
0:21:25
- Constituency Parser Evaluation
0:09:46
- Lexicalization of PCFGs
0:07:03
- Charniak's Model
0:18:24
- PCFG Independence Assumptions
0:09:44
- The Return of Unlexicalized PCFGs
0:20:53
- Latent Variable PCFGs
0:12:08
- Dependency Parsing Introduction
0:10:25
- Greedy Transition Based Parsing
0:31:05
- Dependencies Encode Relational Structure
0:07:21
- Introduction to Information Retrieval
0:09:16
- Term Document Incidence Matrices
0:08:59
- The Inverted Index
0:10:43
- Query Processing with the Inverted Index
0:06:44
- Phrase Queries and Positional Indexes
0:19:46
- Introducing Ranked Retrieval
0:04:27
- Scoring with the Jaccard Coefficient
0:05:07
- Term Frequency Weighting
0:06:00
- Inverse Document Frequency Weighting
0:10:17
- TF IDF Weighting
0:03:42
- The Vector Space Model
0:16:23
- Calculating TF IDF Cosine Scores
0:12:48
- Evaluating Search Engines
0:09:03
- Word Senses and Word Relations
0:11:50
- WordNet and Other Online Thesauri
0:06:23
- Word Similarity and Thesaurus Methods
0:16:18
- Word Similarity Distributional Similarity I
0:13:15
- Word Similarity Distributional Similarity II
0:08:16
- What is Question Answering
0:07:29
- Answer Types and Query Formulation
0:08:48
- Passage Retrieval and Answer Extraction
0:06:38
- Using Knowledge in QA
0:04:25
- Advanced Answering Complex Questions
0:04:53
- Introduction to Summarization
0:04:46
- Generating Snippets
0:07:35
- Evaluating Summaries ROUGE
0:05:03
- Summarizing Multiple Documents
0:10:42
- Instructor Chat II
0:05:24
- Course Introduction
- TextBlob Tutorial Series
- Natural Language Processing Tutorial With TextBlob -Tokens,Translation and Ngrams
0:11:01
- NLP Tutorial With TextBlob and Python - Parts of Speech Tagging
0:05:59
- NLP Tutorial With TextBlob & Python - Lemmatizating
0:06:32
- NLP Tutorial with TextBlob & Python - Sentiment Analysis(Polarity,Subjectivity)
0:06:31
- Building a NLP-based Flask App with TextBlob
0:37:30
- Natural Language Processing with Polyglot - Installation & Intro
0:12:49
- Natural Language Processing Tutorial With TextBlob -Tokens,Translation and Ngrams
- Youtube: fast.ai Code-First Intro to Natural Language Processing
- What is NLP? (NLP video 1)
0:22:42
- Topic Modeling with SVD & NMF (NLP video 2)
1:06:39
- Topic Modeling & SVD revisited (NLP video 3)
0:33:05
- Sentiment Classification with Naive Bayes (NLP video 4)
0:58:20
- Sentiment Classification with Naive Bayes & Logistic Regression, contd. (NLP video 5)
0:51:29
- Derivation of Naive Bayes & Numerical Stability (NLP video 6)
0:23:56
- Revisiting Naive Bayes, and Regex (NLP video 7)
0:37:33
- Intro to Language Modeling (NLP video 8)
0:40:58
- Transfer learning (NLP video 9)
1:35:16
- ULMFit for non-English Languages (NLP Video 10)
1:49:22
- Understanding RNNs (NLP video 11)
0:33:16
- Seq2Seq Translation (NLP video 12)
0:59:42
- Word embeddings quantify 100 years of gender & ethnic stereotypes-- Nikhil Garg (NLP video 13)
0:47:17
- Text generation algorithms (NLP video 14)
0:25:39
- Implementing a GRU (NLP video 15)
0:23:13
- Algorithmic Bias (NLP video 16)
1:26:17
- Introduction to the Transformer (NLP video 17)
0:22:54
- The Transformer for language translation (NLP video 18)
0:55:17
- What you need to know about Disinformation (NLP video 19)
0:51:21
- Article: Zero to Hero with fastai - Beginner
- Article: Zero to Hero with fastai - Intermediate
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- Designing a machine learning platform for both data scientists and engineers
- How to serve batch predictions with TensorFlow Serving
- How to deploy Transformer models for language tasks
- How we scale machine learning model deployment on Kubernetes
- Why we built a serverless machine learning platform—instead of using AWS Lambda
- Why we don’t deploy machine learning models with Flask
- How to deploy machine learning models from a notebook to production
- A/B testing machine learning models in production
- How to deploy 1,000 models on one CPU with TensorFlow Serving
- How to reduce the cost of machine learning inference
- Improve NLP inference throughput 40x with ONNX and Hugging Face
- How to deploy PyTorch Lightning models to production
- Doc: Environment variables in Compose
- Doc: Lecture 3: Data engineering
- Luigi Patruno: ML in Production
- Video: You trained a machine learning model. Now what?
- Article: Docker for Machine Learning – Part I
- Article: Docker for Machine Learning – Part II
- Article: Docker for Machine Learning – Part III
- Article: Using Docker to Generate Machine Learning Predictions in Real Time
- Article: Batch Inference vs Online Inference
- Article: Storing Metadata from Machine Learning Experiments
- Article: How Data Leakage Impacts Machine Learning Models
- Article: An Introduction to Kubernetes for Data Scientists
- Article: How to Use Kubernetes Pods for Machine Learning
- Article: Kubernetes Jobs for Machine Learning
- Article: Kubernetes CronJobs for Machine Learning
- Article: Kubernetes Deployments for Machine Learning
- Article: Kubernetes Services for Machine Learning
- Article: The Ultimate Guide to Model Retraining
- Article: Top ML Resources: Interview with Eric Colson
- Article: Top ML Resources: Interview with Veronika Megler, PhD
- Article: Top ML Resources: Interview with Erik Bernhardsson
- Article: Top ML Resources: Interview with Rui Carmo
- Article: Top ML Resources: Interview with Jeremy Jordan
- Article: 5 Challenges to Running Machine Learning Systems in Production
- Article: Enabling Machine-Learning-as-a-Service Through Privacy Preserving Machine Learning
- Article: What Does it Mean to Deploy a Machine Learning Model? (Deployment Series: Guide 01)
- Article: Software Interfaces for Machine Learning Deployment (Deployment Series: Guide 02)
- Article: Batch Inference for Machine Learning Deployment (Deployment Series: Guide 03)
- Article: The Challenges of Online Inference (Deployment Series: Guide 04)
- Article: Online Inference for ML Deployment (Deployment Series: Guide 05)
- Article: Model Registries for ML Deployment (Deployment Series: Guide 06)
- Article: Test-Driven Machine Learning Development (Deployment Series: Guide 07)
- Article: A/B Testing Machine Learning Models (Deployment Series: Guide 08)
- Article: Lessons Learned from 15 Years of Monitoring Machine Learning in Production
- Article: Why is it Important to Monitor Machine Learning Models?
- Article: Maximizing Business Impact with Machine Learning
- Article: Proxy Metrics
- Article: Celery: an overview of the architecture and how it works
- Article: Unit Testing Celery Tasks
- Article: Testing Celery Chains
- Article: Task Routing in Celery
- Article: Dynamic Task Routing in Celery
- Article: Dockerize a Celery app with Django and RabbitMQ
- Article: How to call a Celery task from another app
- Article: Distributed Monte Carlo with Celery chords
- Article: An incredibly simple no-frills Celery setup
- Article: 3 Strategies to Customise Celery logging handlers
- Article: Celery task exceptions and automatic retries
- Article: Concurrency and Parallelism
- Article: Celery, docker and the missing startup banner
- Article: Monitoring a Dockerized Celery Cluster with Flower
- Article: Quick Guide: Custom Celery Task Logger
- Article: Celery on Docker: From the Ground up
- Article: Kubernetes for Python Developers: Part 1
- Article: Auto-reload Celery on code changes
- Book: Building Intelligent Systems: A Guide to Machine Learning Engineering
- Datacamp: Parallel Computing with Dask
- Datacamp: Data Engineering for Everyone
- Datacamp: Cloud Computing for Everyone
- Pluralsight: Docker and Containers: The Big Picture
- Pluralsight: Docker and Kubernetes: The Big Picture
- Pluralsight: AWS Developer: The Big Picture
- Pluralsight: AWS Networking Deep Dive: Virtual Private Cloud (VPC)
- Pluralsight: AWS VPC Operations
- Pluralsight: Building Applications Using Elastic Beanstalk
- Servers for Hackers Series
- The Hacker's Guide to Scaling Python
- Udacity: Intro to DevOps
- Udacity: Configuring Linux Web Servers
- Udacity: Scalable Microservices with Kubernetes
- Udemy: AWS Concepts
- Udemy: Serverless Concepts
- Udemy: AWS Certified Developer - Associate 2018
- Whitepaper: Architecting for the Cloud AWS Best Practices
- Whitepaper: AWS Well-Architected Framework
- Whitepaper: AWS Security Best Practices
- Whitepaper: Blue/Green Deployments on AWS
- Whitepaper: Microservices on AWS
- Whitepaper: Optimizing Enterprise Economics with Serverless Architectures
- Whitepaper: Practicing Continuous Integration and Continuous Delivery on AWS
- Whitepaper: Running Containerized Microservices on AWS
- Whitepaper: Serverless Architectures with AWS Lambda
- Youtube: Deploying a machine learning model to the cloud using AWS Lambda
- Youtube: Shawn Scully: Production and Beyond: Deploying and Managing Machine Learning Models
- Youtube: Human-Centric Machine Learning Infrastructure @Netflix
- Youtube: Instrumentation, Observability & Monitoring of Machine Learning Models
- Youtube: OpML '20 - How ML Breaks: A Decade of Outages for One Large ML Pipeline
- Youtube: Applied ML in Production
- MLOps Community Talks
- Our 1st MLOps Meetup - Luke Marsden
0:56:11
- MLOps Community Meetup #3: Hierarchy of Machine Learning Needs with Phil Winder
0:58:25
- MLOps Community March 25 2020 featuring Charles Radclyffe
1:03:42
- MLOps #4: Shubhi Jain - Building an ML Platform @SurveyMonkey
0:55:42
- MLOps Meetup #6: Mid-Scale Production Feature Engineering with Dr. Venkata Pingali
1:01:35
- MLOps meetup #5 High Stakes ML with Flavio CLesio
0:55:27
- MLOps meetup #7 Alex Spanos // TrueLayer 's MLOps Pipeline
0:56:17
- #8 Optimizing your ML workflow with kubeflow 1.0
1:03:41
- #10 MLOps the Elephant and the Blind Men with Saurav Chakravorty
0:55:02
- #11 Machine Learning at scale in Mercado Libre with Carlos de la Torre
0:59:28
- MLOps meetup #12 // Why data scientists should know data engineering with Dan Sullivan
0:58:28
- #13 Maximizing job opportunities as a data scientists with Anthony Kelly
0:58:24
- MLOps #14: Kubeflow vs MLflow with Byron Allen
0:54:57
- MLOps #15 - Scaling Human in the Loop Machine Learning with Robert Munro
0:55:04
- #16 Venture Capital In Machine Learning Startups with John Spindler
1:05:41
- MLOps #17 // The challenges of ML Operations and how Hermione helps along the way
1:01:30
- MLOps #18 // Nubank - Running a fintech on ML
0:53:19
- MLOps #19 // DataOps and Data versioning in ML
1:01:55
- MLOps #21 Build vs Buy an ML platform // Diego Oppenheimer - CEO Algorithmia
0:57:20
- MLOps #22 Deep Dive on Paperspace Tooling // Misha Kutsovsky - Senior ML Architect at Paperspace
1:07:15
- Feature Stores: An essential part of the ML stack to build great data / Kevin Stumpf - CTO at Tecton
1:05:46
- MLOps #24 Monitoring the ML stack // Lina Weichbrodt
0:55:32
- MLOps #25 - How to become a better data scientist: the definite guide // Alexey Grigorev
1:00:42
- MLOps #26 Python and Dask: Scaling the DataFrame // Dan Gerlanc - Founder of Enplus Advisors
1:28:56
- #27 How to Leverage ML Tooling Ecosystem Mariya Davydova Head of Product at Neu ro
0:55:57
- MLOps #28 ML Observability // Aparna Dhinakaran - Chief Product Officer at Arize AI
0:55:04
- MLOps #29 Continuous Evaluation & Model Experimentation // Danny Ma - Founder of Sydney Data Science
1:00:46
- MLOps Coffee Sessions #6 Continuous Integration for ML // Featuring Elle O'Brien
1:01:46
- MLOps #30 Scaling ML Capabilities in Large Organizations // Bertjan Broeksema & Axel Goblet
1:02:47
- MLOps #31 Path to Production and Monetizing Machine Learning // Vin Vashishta - Data Scientist
0:56:35
- MLOps #32 Creating Beautiful Ambient Music with Google Brain’s Music Transformer // Daniel Jeffries
0:55:52
- MLOps #33 Building Say Less: An AI-Powered Summarization App // Yoav Zimmerman - Model Zoo
0:53:08
- MLOps #34 Owned By Statistics: How Kubeflow & MLOps Can Help Secure ML Workloads // David Aronchick
0:56:18
- MLOps #35: Streaming Machine Learning with Apache Kafka and Tiered Storage // Kai Waehner, Confluent
0:52:50
- Bring Your On-Prem ML Use Cases to Production on Google Cloud using Kubeflow
0:20:28
- Moving deep learning from research to prod using DeterminedAI and Kubeflow // David Hershey
0:56:06
- MLOps Coffee Sessions #11: Analyzing “Continuous Delivery and Automation Pipelines in ML" // Part 3
1:06:28
- MLOps Coffee Sessions #14 Conversation with the creators of Dask // Hugo Bowne and Matthew Rocklin
0:56:27
- When You Say Data Scientist Do You Mean Data Engineer? Lessons Learned From Start Up Life
1:00:43
- Scalable Python for Everyone, Everywhere // Matthew Rocklin // MLOps Meetup #37
0:57:07
- Operationalize Open Source Models with SAS Open Model Manager // Ivan Nardini // MLOps Meetup #39
0:56:53
- Data Engineering + ML + Software Engineering // Satish Chandra Gupta // MLOps Coffee Sessions #16
0:57:05
- Hands-on serving models using KFserving // Theofilos Papapanagiotou // MLOps Meetup #40
0:57:40
- Luigi in Production // MLOps Coffee Sessions #18 // Luigi Patruno ML in Production
0:47:23
- Metaflow: Supercharging Our Data Scientist Productivity // Ravi Kiran Chirravuri // MLOps Meetup #41
1:00:30
- UN Global Platform // Mark Craddock // Co-Founder & CTO, Global Certification // MLOps Meetup #42
0:58:48
- The Current MLOps Landscape // Nathan Benaich & Timothy Chen // MLOps Meetup #43
0:58:31
- A Conversation with Seattle Data Guy // Benjamin Rogojan // MLOps Coffee Sessions #21
0:47:11
- Our 1st MLOps Meetup - Luke Marsden
- Youtube: Josh Wills: Visibility and Monitoring for Machine Learning Models
- Youtube: PyData Vancouver meetup: cortex.dev : Serving machine learning models in production
- Youtube: Why Your Web Server Should Log to Stdout (Especially with Docker)
- Article: Multi-Armed Bandit (MAB) – A/B Testing Sans Regret
- Article: When to Run Bandit Tests Instead of A/B/n Tests
- Datacamp: Customer Analytics & A/B Testing in Python
- Udacity: A/B Testing
- Udacity: A/B Testing for Business Analysts
- Youtube: Hypothesis testing with Applications in Data Science
0:10:33
- Article: No Really, Python's Pathlib is Great
- Article: A deep dive on Python type hints
- Article: I wish I knew these things when I learned Python
- Article: Python Concurrency: The Tricky Bits
- Article: The Complete Python Development Guide
- Article: Hypermodern Python
- Article: Hypermodern Python Chapter 2: Testing
- Article: Hypermodern Python Chapter 3: Linting
- Article: Hypermodern Python Chapter 4: Typing
- Article: Hypermodern Python Chapter 5: Documentation
- Article: Hypermodern Python Chapter 6: CI/CD
- Article: Speeding Up Python with Concurrency, Parallelism, and asyncio
- Article: Speed Up Your Python Program With Concurrency
- Regex For Noobs (like me!) - An Illustrated Guide
- Book: A Byte of Python
- Book: Learn Python The Hard way
- Book: Python 201
- Book: Python Anti-Patterns
- Book: Real Python
- Book: The Python 3 Standard Library By Example
- Book: Writing Idiomatic Python 3
- Calmcode: logging
- Calmcode: tqdm
- Calmcode: virtualenv
- Calmcode: ray
- Codecademy: Learn Python
- Cognitiveclass.ai: Python for Data Science
- Doc: Python Lifecycle Training
- Datacamp: Python for R Users
- Datacamp: Python for Spreadsheet Users
- Datacamp: Python for MATLAB Users
- Datacamp: Importing Data in Python (Part 1)
- Datacamp: Intermediate Python for Data Science
- Datacamp: Python Data Science Toolbox (Part 1)
- Datacamp: Python Data Science Toolbox (Part 2)
- Datacamp: Intro to Python for Finance
- Datacamp: Writing Efficient Python Code
- Datacamp: Writing Functions in Python
- Datacamp: Working with Dates and Times in Python
- Datacamp: Object-Oriented Programming in Python
- edX: Introduction to Python for Data Science
- edX: Programming with Python for Data Science
- Google's Python Class
- Treehouse: Python Basics
- TheNewBoston: Python Programming Tutorials
- Udacity: Introduction to Python Programming
- Udacity: Programming Foundations with Python
- Youtube: Python 3 Programming Tutorial - Regular Expressions / Regex with re
- Youtube: Python Tutorial: re Module - How to Write and Match Regular Expressions (Regex)
- Youtube: Tutorial: Sebastian Witowski - Modern Python Developer's Toolkit
- Youtube: Tutorial: Santiago Basulto - Python Concurrency: from beginner to pro
- Youtube: Aaron Richter- Parallel Processing in Python| PyData Global 2020
- Book: Refactoring UI
- Codecademy: Learn HTML
- Codecademy: Learn Color Design
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- Codecademy: Make a website
- Codecademy: Learn ReactJS: Part I
- Codecademy: Learn ReactJS: Part II
- Codecademy: Learn JavaScript
- Codecademy: Jquery Track
- Codecademy: Learn Ruby
- Code School: Fundamentals of Design
- Code School: Blasting Off with Bootstrap
- (ES6) - Beau teaches JavaScript
- Pluralsight: UX Fundamentals
- Pluralsight: HTML, CSS, and JavaScript: The Big Picture
- Pluralsight: CSS Positioning
- Pluralsight: Introduction to CSS
- Pluralsight: CSS: Specificity, the Box Model, and Best Practices
- Pluralsight: CSS: Using Flexbox for Layout
- Pluralsight: Using The Chrome Developer Tools
- Thoughtbot: Design for Developers
- Treehouse: HTML
- Treehouse: Javascript Booleans
- Udacity: ES6 - JavaScript Improved
- Udacity: Intro to Javascript
- Udacity: Object Oriented JS 1
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- Udemy: Understanding Typescript
- Book: Grokking Algorithms
- Codecademy: Big O
- Crashcourse: Computer Science
- Khan Academy: Data Structures
- Udacity: Intro to Algorithms
- Udacity: Intro to Computer Science
- Udacity: Intro to Theoretical Computer Science
- Udacity: Programming Languages
- Udacity: Networking for Web Developers
- Pluralsight: Security Awareness: Basic Concepts and Terminology
- Pluralsight: Secure Software Development
- Pluralsight: Clean Architecture: Patterns, Practices, and Principles
- Thoughtbot: Software Development Process
- Thoughtbot: Refactoring
- Udacity: Design of Computer Programs
- Udacity: Product Design
- Udacity: Rapid Prototyping
- Udacity: Software Architecture and Design
- Udacity: Software Development Process
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- Article: Work remotely with PyCharm, TensorFlow and SSH
- Article: Python remote debugging with PyCharm, CUDA, and Conda
- Article: How To Use Visual Studio Code for Remote Development via the Remote-SSH Plugin
- Article: Docker as Remote Interpreter for PyCharm Professional
- Learn Enough Text Editor to Be Dangerous
- Mastering Pycharm
- Video: Pycharm Tips
- Youtube: Productive pytest with PyCharm
- Youtube: Getting Started with Python in Visual Studio Code
- Youtube: 42 PyCharm Tips and Tricks
- Youtube: Pycharm Quick Tips & Tricks
- How to use Live Edit to edit HTML&CSS in PyCharm
0:00:49
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0:00:35
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0:00:48
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0:01:36
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0:00:58
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0:01:03
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0:01:33
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0:01:06
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0:01:08
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0:00:39
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0:00:46
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0:00:47
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0:01:21
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0:01:21
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0:00:55
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0:01:45
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0:01:06
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0:00:49
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0:01:01
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0:01:08
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0:00:52
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0:01:25
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0:01:11
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- Google: Technical Writing
- Book: Emotional Intelligence
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- Book: Influence: The Psychology of Persuasion
- Book: Leaders Eat Last: Why Some Teams Pull Together and Others Don't
- Book: Multipliers: How the Best Leaders Make Everyone Smarter
- Book: Soft Skills: The software developer's life manual
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- Calmcode: Remote Work
- Youtube: Building a psychologically safe workplace | Amy Edmondson | TEDxHGSE
- Article: What You Need to Know Before Considering a PhD
- Article: Advice to aspiring data scientists: start a blog
- Article: Systems Design Interview Guide
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- Book: Machine Learning Systems Design
- Datacamp: Preparing for Statistics Interview Questions in Python
- Datacamp: Preparing for Coding Interview Questions in Python
- Datacamp: Kaggle Competition
- Udacity: Optimize your GitHub
- Udacity: Strengthen Your LinkedIn Network & Brand
- Udacity: Data Science Interview Prep
- Udacity: Full-Stack Interview Prep
- Udacity: Refresh Your Resume
- Udacity: Craft Your Cover Letter
- Udacity: Technical Interview
- Youtube: Stanford CS230: Deep Learning | Autumn 2018 | Lecture 8 - Career Advice / Reading Research Papers
- Youtube: The Importance of Writing in a Tech Career - Eugene Yan
- Youtube: How to prepare for Machine Learning interviews- Part 1 | Applied AI Course
- Youtube: How to prepare for Machine Learning interviews- Part 2 | Applied AI Course
- Book: Atomic Habits
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- Calmcode: Pomodoro
- Youtube: Why specializing early doesn't always mean career success | David Epstein
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- Youtube: How to Build a Personal Monopoly with Jack Butcher
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