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100-days-of-ml-code

100 Days of ML Coding

aialpha

Use unsupervised and supervised learning to predict stocks

awesome-deep-learning-papers

The most cited deep learning papers

cheatsheets-ai

Essential Cheat Sheets for deep learning and machine learning researchers https://medium.com/@kailashahirwar/essential-cheat-sheets-for-machine-learning-and-deep-learning-researchers-efb6a8ebd2e5

concrete_nlp_tutorial

An NLP workshop about concrete solutions to real problems

ebook

feature-engineering

java-spark-exercises

java-spring-ebook

lambda-arch

Applying the Lambda Architecture with Spark, Kafka, and Cassandra.

machine-learning-interview-questions

This repository is to prepare for Machine Learning interviews.

medium_articles

Scripts used for articles published on Medium

mlinterview

:octocat: A curated awesome list of AI Startups in India & Machine Learning Interview Guide. Feel free to contribute!

probability_cheatsheet

A comprehensive 10-page probability cheatsheet that covers a semester's worth of introduction to probability.

py

Repository to store sample python programs for python learning

python-machine-learning-book

The "Python Machine Learning (1st edition)" book code repository and info resource

python-machine-learning-book-2nd-edition

The "Python Machine Learning (2nd edition)" book code repository and info resource

scikit-learn

scikit-learn: machine learning in Python

scikit-learn-videos

Jupyter notebooks from the scikit-learn video series

stockpredictionai

In this noteboook I will create a complete process for predicting stock price movements. Follow along and we will achieve some pretty good results. For that purpose we will use a Generative Adversarial Network (GAN) with LSTM, a type of Recurrent Neural Network, as generator, and a Convolutional Neural Network, CNN, as a discriminator. We use LSTM for the obvious reason that we are trying to predict time series data. Why we use GAN and specifically CNN as a discriminator? That is a good question: there are special sections on that later.

t81_558_deep_learning

Washington University (in St. Louis) Course T81-558: Applications of Deep Neural Networks

tensorflow-tutorials

TensorFlow Tutorials with YouTube Videos