Table of Contents generated with DocToc

• Basic algorithm/Framework Study Notes
  • Basics Machine learning
  • CNN
  • RNN
  • TensorFlow
  • Tree
  • SVM
  • Machine learning implementation in large scale system
  • Industrial machine Learning Application design
  • Deep Learning/AI Chip Design
• Applications
  • Kaggle
  • Text Classification
  • Recommendations
  • Industrial Usage
• Book
• Tutorial
• Courses
• Workshop
• Blog
  • Blog Posts
• Code and Framework
• Open Source
• All Code Implementations for NIPS 2016 papers

Basics Machine learning

CNN

RNN

TensorFlow and Keras

Tree(decision_tree,xgboost)

SVM

Machine learning implementation in large scale system

Industrial machine Learning Application design

Deep Learning/AI Chip Design

• Kaggle experience

• Summary of Text Classification

• Convolutional Neural Networks for Sentence Classification. Paper and blog post

• Bidirectional LSTM and one level attentional RNN. blog

• Recommendation using DL

• How does Quora use machine learning in 2017?

• Using Deep Learning at Scale in Twitter’s Timelines

• Deep Learning: An MIT Press Book
  • By Ian Goodfellow and Yoshua Bengio and Aaron Courville

• Stanford Deep Learning Tutorial

• A Guide to Deep Learning

• Reinforcement Learning- OpenAI Gym

• Fast AI Tutorial

• Introduction to Boosted Trees

• Stanford: CS231n: Convolutional Neural Networks for Visual Recognition

  • CS231n Chinese Translation

• Berkeley: CS 294: Deep Reinforcement Learning

• MIT: Deep Learning Self Driving Car

• Oxford: Deep Learning on NLP

• UCL: Reinforcement learning (By D.Silver)

• Stanford: CS224n: Natural Language Processing with Deep Learning

• CMU:Scalable Machine Learning (By A. Smola)

• Applied Deep Learning Workshop London 2017

• Deep Learning course: lecture slides and lab notebooks

  • Lab 1: Neural Networks and Backpropagation:

    • Intro to MLP with Keras, Numpy and TensorFlow

  • Lab 2: Embeddings and Recommender Systems.

    • Neural Recommender Systems with Explicit Feedback. Neural Recommender Systems with
    • Implicit Feedback and the Triplet Loss

  • Lab 3: Convolutional Neural Networks for Image Classification

  • Convolution and ConvNets with TensorFlow

    • Pretrained ConvNets with Keras
    • Fine Tuning a pretrained ConvNet with Keras (GPU required)

  • Lab 4: Deep Learning for Object Dection and Image Segmentation

    • Fully Convolutional Neural Networks
    • ConvNets for Classification and Localization

  • Lab 5: Text Classification, Word Embeddings and Language Models

  • Text Classification and Word Vectors

    • Character Level Language Model (GPU required)

  • Lab 6: Sequence to Sequence for Machine Translation

• Simple Reinforcement Learning: Reinforcement Learning With Tensorflow Series(On Medium)

• Deep Reinforcement Learning: Pong from Pixels

• Word2Vec Resource (by Chris McCormick)

• AI, Deep Learning and NLP (By Denny Britz in Google Brain)

• Understand CNN on NLP

  • IMPLEMENTING A CNN FOR TEXT CLASSIFICATION IN TENSORFLOW

• Understanding RNN

• A Brief History of CNNs in Image Segmentation: From R-CNN to Mask R-CNN

• Understanding LSTM Networks and LSTM by Example using Tensorflow

• LEARNING REINFORCEMENT LEARNING (WITH CODE, EXERCISES AND SOLUTIONS)
  • Github link

• Prophet: forecasting at scale by Facebook

Facebook is open sourcing Prophet, a forecasting tool available

some interesting project based on it

1. Forecasting iPad sales using Facebook's Prophet package

• Using Fast Weights to Attend to the Recent Past (https://arxiv.org/abs/1610.06258) Repo: https://github.com/ajarai/fast-weights

• Learning to learn by gradient descent by gradient descent (https://arxiv.org/abs/1606.04474) Repo: https://github.com/deepmind/learning-to-learn

• R-FCN: Object Detection via Region-based Fully Convolutional Networks (https://arxiv.org/abs/1605.06409) Repo: https://github.com/Orpine/py-R-FCN

• Fast and Provably Good Seedings for k-Means (https://las.inf.ethz.ch/files/bachem16fast.pdf). Repo: https://github.com/obachem/kmc2

• How to Train a GAN Repo: https://github.com/soumith/ganhacks

• Phased LSTM: Accelerating Recurrent Network Training for Long or Event-based Sequences (https://arxiv.org/abs/1610.09513) Repo: https://github.com/dannyneil/public_plstm

• Generative Adversarial Imitation Learning (https://arxiv.org/abs/1606.03476) Repo: https://github.com/openai/imitation

• Adversarial Multiclass Classification: A Risk Minimization Perspective (https://www.cs.uic.edu/~rfathony/pdf/fathony2016adversarial.pdf) Repo: https://github.com/rizalzaf/adversarial-multiclass

• Unsupervised Learning for Physical Interaction through Video Prediction (https://arxiv.org/abs/1605.07157) Repo: https://github.com/tensorflow/models/tree/master/video_prediction

• Weight Normalization: A Simple Reparameterization to Accelerate Training of Deep Neural Networks (https://arxiv.org/abs/1602.07868) Repo: https://github.com/openai/weightnorm

• Full-Capacity Unitary Recurrent Neural Networks (https://arxiv.org/abs/1611.00035) Repo: Code: https://github.com/stwisdom/urnn

• Sequential Neural Models with Stochastic Layers (https://arxiv.org/pdf/1605.07571.pdf) Repo: https://github.com/marcofraccaro/srnn

• Convolutional Neural Networks on Graphs with Fast Localized Spectral Filtering (https://arxiv.org/abs/1606.09375) Repo: https://github.com/mdeff/cnn_graph

• Interpretable Distribution Features with Maximum Testing Power (https://papers.nips.cc/paper/6148-interpretable-distribution-features-with-maximum-testing-power.pdf) Repo: https://github.com/wittawatj/interpretable-test/

• Composing graphical models with neural networks for structured representations and fast inference (https://arxiv.org/abs/1603.06277) Repo: https://github.com/mattjj/svae

• Supervised Learning with Tensor Networks (https://arxiv.org/abs/1605.05775) Repo: https://github.com/emstoudenmire/TNML

• Fast ε-free Inference of Simulation Models with Bayesian Conditional Density Estimation: (https://arxiv.org/abs/1605.06376) Repo: https://github.com/gpapamak/epsilon_free_inference

• Bayesian Optimization for Probabilistic Programs (http://www.robots.ox.ac.uk/~twgr/assets/pdf/rainforth2016BOPP.pdf) Repo: https://github.com/probprog/bopp

• PVANet: Lightweight Deep Neural Networks for Real-time Object Detection (https://arxiv.org/abs/1611.08588) Repo: https://github.com/sanghoon/pva-faster-rcnn

• Data Programming: Creating Large Training Sets Quickly (https://arxiv.org/abs/1605.07723) Repo: snorkel.stanford.edu

• Convolutional Neural Fabrics for Architecture Learning (https://arxiv.org/pdf/1606.02492.pdf) Repo: https://github.com/shreyassaxena/convolutional-neural-fabrics

• Value Iteration Networks in TensorFlow (https://arxiv.org/abs/1602.02867) Repo: https://github.com/TheAbhiKumar/tensorflow-value-iteration-networks

• Stochastic Variational Deep Kernel Learning (https://arxiv.org/abs/1611.00336) Repo: https://people.orie.cornell.edu/andrew/code

• Unsupervised Domain Adaptation with Residual Transfer Networks (https://arxiv.org/abs/1602.04433) Repo: https://github.com/thuml/transfer-caffe

• Binarized Neural Networks (https://arxiv.org/abs/1602.02830) Repo: https://github.com/MatthieuCourbariaux/BinaryNet