Links

Here are a bunch of pages that brings me, new ideas everyday. If you are looking for organized learn plan see my ML-DOJO on GitHUB

• First things first and FAQ🔗

  Some of the Quora's well asked and answered question.

• Arxiv🔗

  e-print service in many interesting fields with ML among them.

• GitXiv🔗

  Here the papers from arXiv are being materialized in form of Github implementations.

• What is a Tensor?🔗

  Physics explain starting from what is actually a number.

• r/ReproducibleML🔗

  A Reddit with links to Arxiv papers joined with their implementations. A way to organize, encourage, and evaluate implementations of machine learning papers.

• Colah's blog🔗

  Christopher Olah blog explains comprehensively on neural and convolution networks.

• Andrej Karpathy Medium blog 2017, blog 2016🔗

  Andrej Karpathy present the academia point of view and elaborates on NN.

• Efficient Processing of Deep Neural Networks: A Tutorial and Survey🔗

  as of 2017. Great tutorial.

• Kaggle wiki🔗

  Nice wiki page from Kaggle on what a data science and ML are.

• Berkeley Artificial Intelligence Research (BAIR) blog🔗

  Research blog from Berkeley.

• Convolutional NN for vision🔗

  Some of the material for CNN on vision.

• Deeplearning4J🔗

  Useful introduction to Deep Learning and Neural Nets

• DeepLearning on GPU - Useful DL blogposts🔗

  On DeepLearning from NVidia. Useful posts on topic and framework e.g. Keras, Tehano, Caffe etc.

• DeepLearning Glossary🔗

  Glossary explaining the terms of modern DL.

• Awesome List: Most Cited Deep Learning Papers🔗

  A curated list of the most cited deep learning papers (since 2012).

• Deep Learning Papers Reading Roadmap🔗

• Awesome List: Awesome Machine Learning🔗

  A curated list of awesome machine learning frameworks, libraries and software (by language).

• Neural Machine Translation (seq2seq)🔗

  Technical tutorial on how to build NMT.

• Practical Deep Learning For Coders🔗

  Course on ML requires Nvidia GPU uses Amazon Web Services (AWS).

• Reddit r/learnmachinelearning🔗

• Reddit r/MachineLearning/🔗

• Reddit r/textdatamining/ focused on NLP🔗

• Reddit r/dataisbeautiful/ 🔗

  Reddit on data visualizations. Specific data and artistic endeavors.

• List of Data science blogs🔗

  A curated list of data science blogs.

Academic courses:🔗

• Harvard CS109: Data Science and GitHub notebooks, slides.🔗

  The course is using Python for all programming assignments and projects.

• Stanford:🔗

  • CS221: Artificial Intelligence: Principles and Techniques Foundational principles of ML.
  • CS229: Machine Learning Broad introduction to machine learning and statistical pattern recognition
  • CS231n: Convolutional Neural Networks for Visual Recognition its
    • Youtube, 2016
    • Youtube, 2017
  • CS 20SI: Tensorflow for Deep Learning Research Stanford course on Tensorflow.
  • CS 224n: Natural Language Processing with Deep Learning Stanford course on DeepNLP Pythorch implementation

• Berkeley:🔗

  • CS188: Intro to AI -- Course Materials and blog UC Berkeley's introductory artificial intelligence course
  • CS 189: Notes related to Berkeley Artificial Intelligence Research (BAIR) laboratory ML community at UC Berkeley website.
  • CS 294: Deep Reinforcement Learning and Youtube version

• UC Toronto CSC 321 by Geoffrey Hinton🔗

  Course form 2014

• MIT 6.S094: Deep Learning for Self-Driving Cars🔗

YouTube content:🔗

• Stanford CS231n Winter 2016 and Reddit related discussions🔗

  Video of Stanford course and its related Reddit posts.

• The Math of Intelligence🔗

  One of the best IMHO course on youtube.

• Deep Learning Fundamentals (PlayList)🔗

  Essential topics in deep learning for beginners

• Deep Learning with Keras (PlayList)🔗

  Step-by-step tutorials for getting started with deep learning using Python and Keras

What people read:🔗

• Denny Blitz (@Google)🔗

  Summaries and notes on Deep Learning research papers

• ShortScience.org🔗

  Short summaries of research papers.

• Shorts of papers🔗

  Short summaries of some research papers.

• Good papers🔗

Books🔗

• DeepLearning Book 🔗

  by I. Goodfellow and Y. Bengio and A. Courville

• Foundations of Statistical Natural Language Processing🔗

  by Ch. Manning and H. Schütze; Scroll bottom to see list of courses that uses this book.

• Neural Networks and Deep Learning🔗

  Michael A. Nielsen, "Neural Networks and Deep Learning", Determination Press, 2015

• Machine learning cheat sheet🔗

  This cheat sheet contains many classical equations and diagrams on machine learning, which will help you quickly recall knowledge and ideas on machine learning.

• Natural Language Processing with Python🔗

  By creator of Keras Francois Chollet

• Deep Learning with Python🔗

  Online book with Python examples from NLTK

Tools:🔗

A set of handy helpers.

• NBViewer🔗

  The interactive features of the Jupyter notebook, such as custom JavaScript plots, will not work in your repository on GitHub. To view your Jupyter notebook with JavaScript content rendered or to share your notebook files with others you can use nbviewer.

• TensorFlow AwesomeList🔗

  A curated list of awesome TensorFlow experiments, libraries, and projects.

• Apple's ARKit and Showcase .🔗

  Amazing framework that allows you to easily create unparalleled augmented reality experiences for iPhone and iPad

• Best-websites-a-programmer-should-visit🔗

• Drawing in Jupyter🔗

• MathBox2: Drawing 3D in Jupyter and live Jupyter example🔗

• Librarian for arXiv | Fermat's Library🔗

  Chrome plugin extracting references and BibTex form articles.

Sources of datasets🔗

• Quandl🔗

  A website with free / premium datasets to test ML. Quandl API can be used with Python pip package (pip install Quandl)

• Linguistic Data Consortium (LDC)🔗

  Open consortium of universities, libraries, corporations and government research laboratories that creates and distributes a wide array of language resources

• Ranking of the BEST dataset solutions🔗

  Online, crowd sourced list of known result one some of the “major” visual classification, detection, and pose estimation datasets.

Generators🔗

• Deep Dream Generator🔗

  Many interesting filters. Requires logging in and waiting. Many options.

• DeepArt Generator🔗

  No login required. Fewer filters than Deep dream Generator. Few second wait for result.

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