Machine Learning is a branch of Artificial Intelligence dedicated at making machines learn from observational data without being explicitly programmed.
NOTE: There is no particular rank or order for each link. The order in which they appear does not convey any meaning and is not essential.
- How do I learn machine learning? - Quora
- Intro to Machine Learning | Udacity
- Machine Learning: Supervised, Unsupervised & Reinforcement | Udacity
- Machine Learning Mastery
- Andrew Ng's Course
- https://karpathy.github.io/
- Advanced Topics: RL 2015 (COMPM050/COMPGI13)
- An Introduction Book by Richard S. Sutton and Andrew G. Barto
- Deep Reinforcement Learning: Pong from Pixels
- Lecture 10: Reinforcement Learning - YouTube
- A Survey Paper
A set of machine learning techniques specialized at training deep artificial neural networks.
- Deep learning | Udacity
- Deep Learning Resources (Papers, Online Courses, Books) - deeplearning4j.org
- Introduction to Deep Neural Networks - deeplearning4j.org
- NVIDIA Deep Learning Institute
- Deep Learning Book
- Unsupervised Feature Learning and Deep Learning
- DeepMind Publications
- DeepLearning.TV - YouTube
- CS224d: Deep Learning for Natural Language Processing
- CS231n: Convolutional Neural Networks for Visual Recognition
- Deep Learning Summer School, Montreal 2015
- UFLDL Deep Learning Tutorial
- http://deeplearning.net/
- https://developer.nvidia.com/deep-learning
- http://neuralnetworksanddeeplearning.com/index.html
- https://github.com/terryum/awesome-deep-learning-papers
- https://github.com/ChristosChristofidis/awesome-deep-learning
- https://github.com/lisa-lab/DeepLearningTutorials
- http://www.wildml.com/
- http://colah.github.io/
- http://karpathy.github.io/2015/05/21/rnn-effectiveness/
- http://colah.github.io/posts/2015-08-Understanding-LSTMs/
- http://www.wildml.com/2015/09/recurrent-neural-networks-tutorial-part-1-introduction-to-rnns/
- deepdream inceptionism
- Magenta: Music and Art Generation with Machine Intelligence
- SyntaxNet (Parsey McParseface)
Taking a look at their github statistics can give you a sense of how active/popular each library is.
- scikit-learn (Python)
- TensorFlow (Python); Learning TensorFlow
- Theano (Python)
- Caffe (Python) does best at computer vision problems
- Computational Network Toolkit (CNTK)
- Torch (LuaJIT)
- DeepLearning4j (Java)
- Software Tools for Reinforcement Learning, Artificial Neural Networks and Robotics (Matlab and Python)
- Pedro Domingos: "The Master Algorithm" | Talks at Google
- The AI Revolution: The Road to Superintelligence
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