Resources for Deep Machine Learning

Big Pictures

source: scikit-learn algorithm cheat-sheet

Guide

• A Guide to Deep Learning by YerevaNN

Tutorials

• Official Tensorflow Tutorials (python)
• prettytensor
• Learn Tensorflow and deep learning w/o a PhD
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• Hvass Laboratories  on tensorflow
• https://github.com/tensorflow/tensorflow/issues/5036
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• TensorFlow Examples (python)
• TFLearn Examples GitHub
• Deep Learning for Java Tutorials (java)
• Keras
• Sequential Model
• Learning Deep Learning with Keras
• Introduction to Deep Neural Networks with Keras and Tensorflow
• Keras Tutorial: The Ultimate Beginner’s Guide to Deep Learning in Python
• Keras Tutorial: Deep Learning in Python by DataCamp
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• Top 10 IPython Notebook Tutorials for Data Science and Machine Learning

Courses

• Practical Deep Learning For Coders by Jeremy Howard
• GitHub
• Forum
• Wiki
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• Coursera Machine Learning class by Andrew Ng
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• Stanford CS224d: Deep Learning for Natural Language Processing  
• Class http://cs224d.stanford.edu/    
• Class http://web.stanford.edu/class/cs224n/
• GitHub https://github.com/bobflagg/cs224d-deep-learning-for-nlp
• python crash course
• spaCy - fast NLP
• NLP intro by Clark Grubb
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• Stanford CS231n: Convolutional Neural Networks for Visual Recognition 
• Class http://cs231n.stanford.edu/
• GitHub https://cs231n.github.io/
• Stanford Student Project Reports

• MIT 6.S094: Deep Learning for Self-Driving Cars  
• Class http://selfdrivingcars.mit.edu/
• GitHub https://github.com/lexfridman/deeptesla

• Learning Deep Learning with Keras by Migdal
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• Data Science Resources by EliteDataScience.com
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• Dive into Deep Learning with 15 free online courses by David Venturi
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• Intro to Data Science course on the internet
• very good list of related MOOC resources
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• How do I learn Machine Learning in 2 months?

Books

• Deep Learning: A Practitioner's Approach (Josh Patterson; Adam Gibson)
• some excerpt are here : https://deeplearning4j.org/neuralnet-overview
• Deep Learning by MIT Press 
• Deep Learning in Python with Keras 
• Scikit-Learn Documentation  
• Flow Chart to choose the right model
• A First Encounter with Machine Learning by Max Welling (81 pages, pub 2010-04)
• A Course in Machine Learning by Hal Daume III (224 pages, pub 2017-01)
• GitHub
• An Introduction To Statistical Learning by 4 authors (440 pages, pub 2015)
• Elements of Statistical Learning by 3 authors (760 pages, pub 2008)
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• Scipy Lecture Notes 
• local files : D:\machine_learn\Scipy_Lecture_Notes\scipy-lecture-notes\Notebook
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• R for Data Science
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• Learning IPython for Interactive Computing and Data Visualization by Cyrille Rossant
• IPython Interactive Computing and Visualization Cookbook by Cyrille Rossant
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• Quest for AI - History by Nils J Nilsson
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• Essential Cheat Sheets for Machine Learning and Deep Learning Engineers  (GitHub)

Videos

• Series of  Deep Learning Talks (2016-09) 
• PyData Workshop by Jacob VanderPlas
• PyCon Tutorial by Olivier Grisel
• Machine Learning with Text in scikit-learn  (GitHub)
• More Videos and Talks
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• Siraj Raval AI Education 

• Morvan Python Education 

Websites

• Awesome Deep Learning
• Keras
• Resources
• scikit-learn - Machine Learning in Python
• Spark MLlib - Machine Learning in Spark
• https://www.tensorflow.org/ (python)
• http://tflearn.org/ (python, API wrapper over tensorflow)
• https://deeplearning4j.org  (java)
• Kaggle Competitions in Data Science
• OpenAI - Elon Musk's initiative
• PythonProgramming.net 
• Dive Into Machine Learning
• Big Data Science blog by Fabian Schreiber

Data

• Top 10 Open Dataset resources on GitHub
• Data.World
• Open Data for Deep Learning
• a very organized list of interesting, high-quality datasets for machine learning research.
• UC Irvine Machine Learning Repository
• Microsoft Azure sample datset
• Data Is Plural
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Research

• Wikipedia on Machine Learning
• GitHub
• Google search - text, image, video, book

List of Open Source Machine Learning Resources

Deep Learning resource spreadsheet by Wen Gong   Keras - high-level neural networks API in python Caffe: a fast open framework for deep learning. http://caffe.berkeleyvision.org/ DIY Deep Learning for Vision with Caffe   spaCy - fast NLP for python https://spacy.io/docs/usage/

Name	URL	Language	comments
Open Source Machine Learning Tools	http://opensourceforu.com/2017/01/best-open-source-machine-learning-frameworks/
Open Source Machine Learning Tools	http://www.kdnuggets.com/2016/04/top-15-frameworks-machine-learning-experts.html
Apache Singa	http://singa.apache.org/en/index.html	python	Apache project developed at Singapore/China similar to Tensorflow
Shogun	http://www.shogun-toolbox.org/	python ++	The Shogun Machine learning toolbox offers a wide range of efficient and unified Machine Learning methods.
Apache Mahout	https://mahout.apache.org/
Apache Spark MLlib	http://spark.apache.org/mllib/		good platform, lack of examples
TensorFlow	http://www.tensorflow.org
Oryx 2	http://oryx.io/		Oryx 2 is a realization of the lambda architecture built on Apache Spark and Apache Kafka, but with specialization for real-time large scale machine learning
Accord.NET	http://www.accord-framework.net/		Machine Learning made in a min with lots of examples and samples
Amazon Machine Learning (AML)	https://aws.amazon.com/machine-learning/
Scikit-learn	http://scikit-learn.org/	python
The Factorie Toolkit	http://factorie.cs.umass.edu/
Azure ML Studio	https://studio.azureml.net/		Microsoft ML Studio
Caffe	http://caffe.berkeleyvision.org/
H2O	http://www.h2o.ai/	java ++
Massive Online Analysis (MOA)	http://moa.cms.waikato.ac.nz/	java	collection of machine learning algorithms (classification, regression, clustering, outlier detection, concept drift detection and recommender systems) and tools for evaluation
mlpack	http://mlpack.org/	C++	C++ machine learning library with emphasis on scalability, speed, and ease-of-use
Pattern	http://www.clips.ua.ac.be/pattern	python	Pattern is a web mining module for the Python programming language. It has tools for data mining (Google, Twitter and Wikipedia API, a web crawler, a HTML DOM parser), natural language processing (part-of-speech taggers, n-gram search, sentiment analysis, WordNet), machine learning (vector space model, clustering, SVM), network analysis and <canvas> visualization
Theano	http://deeplearning.net/software/theano/	python
Torch	http://torch.ch/	LuaJIT	Torch is a scientific computing framework with wide support for machine learning algorithms that puts GPUs first. It is easy to use and efficient, thanks to an easy and fast scripting language, LuaJIT, and an underlying C/CUDA implementation.
Veles	https://velesnet.ml/	Python	Distributed platform for rapid Deep learning application development

Blog / Journal / Notebook

• use www.blogger.com 
• document your learning, research, work, thought
• teach and share with others
• Jupyter Notebook 

• Apache Spot
• Tensorflow 
• DeepLearning4J

Q&A

1. Deep Learning @ Quora
2. What is the difference between TF Learn (aka Scikit Flow) and TFLearn (aka TFLearn.org)