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Name: heemokyim
Type: User
Name: heemokyim
Type: User
My useful resources of Deep Learning in Tensorflow
Kaggle competitions
Different approaches as (ANN,DecisionTree,Bayes and KNeighbors) to solve and predict with the best accuracy malignous cancers
Prediction model for Kaggle/Rossmann competition.
2018 공개 소프트웨어 컨트리뷰톤 - 캐글 속 커널 한글화 작업을 통한 데이터 사이언스 대중화 프로젝트
Kaggle Struggle
Deep Learning for humans
Keras 예제에 대한 스터디를 한 것입니다.
Simple tutorials using Keras Framework
Jupyter Notebook Code for Keras Deep Learning Framework Study
Learn, understand, and implement deep neural networks in a math- and programming-friendly approach using Keras and Python. The book focuses on an end-to-end approach to developing supervised learning algorithms in regression and classification with practical business-centric use-cases implemented in Keras. The overall book comprises three sections with two chapters in each section. The first section prepares you with all the necessary basics to get started in deep learning. Chapter 1 introduces you to the world of deep learning and its difference from machine learning, the choices of frameworks for deep learning, and the Keras ecosystem. You will cover a real-life business problem that can be solved by supervised learning algorithms with deep neural networks. You’ll tackle one use case for regression and another for classification leveraging popular Kaggle datasets. Later, you will see an interesting and challenging part of deep learning: hyperparameter tuning; helping you further improve your models when building robust deep learning applications. Finally, you’ll further hone your skills in deep learning and cover areas of active development and research in deep learning. At the end of Learn Keras for Deep Neural Networks, you will have a thorough understanding of deep learning principles and have practical hands-on experience in developing enterprise-grade deep learning solutions in Keras.
This is the Curriculum for "Learn Data Science in 3 Months" By Siraj Raval on Youtube
This is the Curriculum for "Learn Deep Learning in 6 Weeks" by Siraj Raval on Youtube
Codes related to Lord of the Machines hackathon
A complete daily plan for studying to become a machine learning engineer.
Project from 2017
Multilayer Perceptron Implementation for Kaggle Zillow Competition, University of Bridgeport 2017
Machine Learning Problem Bible | Problem Set Here >>
Statistical, Machine and Deep Learning Playground
Deep Learning: Feedforward Neural Network for churn prediction
Implementing Multiple Layer Neural Network from Scratch
This is my assignment on Andrew Ng's course “neural networks and deep learning”
Supporting code for short YouTube series Neural Networks Demystified.
Multi-layer Perceptron
Play Kaggle Repository : Enjoy!
A declarative, efficient, and flexible JavaScript library for building user interfaces.
🖖 Vue.js is a progressive, incrementally-adoptable JavaScript framework for building UI on the web.
TypeScript is a superset of JavaScript that compiles to clean JavaScript output.
An Open Source Machine Learning Framework for Everyone
The Web framework for perfectionists with deadlines.
A PHP framework for web artisans
Bring data to life with SVG, Canvas and HTML. 📊📈🎉
JavaScript (JS) is a lightweight interpreted programming language with first-class functions.
Some thing interesting about web. New door for the world.
A server is a program made to process requests and deliver data to clients.
Machine learning is a way of modeling and interpreting data that allows a piece of software to respond intelligently.
Some thing interesting about visualization, use data art
Some thing interesting about game, make everyone happy.
We are working to build community through open source technology. NB: members must have two-factor auth.
Open source projects and samples from Microsoft.
Google ❤️ Open Source for everyone.
Alibaba Open Source for everyone
Data-Driven Documents codes.
China tencent open source team.