Resources

Last update: 10-07-2018

Here you will find lots of courses, blogs, tutorials, projects and other interesting resources that will help you if you are starting a Machine Learning career or if you just want to learn some AI in your free time.

Disclaimer: I haven’t done all of these courses (and not all of the courses I have done are here), so I can’t guarantee its quality. But based on the research and readings I have done, I can recommend you to take a look if you are interested in.

Courses and MOOCs

Machine Learning and Deep Learning

• Machine Learning by Andrew Ng, Coursera
• Deep Learning Specialization by Andrew Ng, Coursera
• Deep Learning by Google, Udacity
• Deep Learning with Tensorflow, Big Data University
• Practical Deep Learning for Coders, Fast.ai
• Tensorflow for Deep Learning Research, Stanford

Natural Language Processing

• Deep Natural Language Processing 2017, Oxford
• Deep Natural Language Processing 2017, Stanford

Reinforcement Learning

• Deep Reinforcement Learning 2017, Berkeley
• Deep Reinforcement Learning Bootcamp 2017

Tutorials

• Understanding LSTM Networks: a great introduction to LSTM Networks. To see some funny applications of Recurrent Neural Networks, check this link.
• Attention and Augmented Recurrent Neural Networks: some improvements on RNNs by letting the network sequentially focus on a subset of the input. Here is another good explanation about Attention NN, and here another one.
• Neural Machine Translation (seq2seq): a tutorial to understand and implement NMT with tensorflow.
• ResNets, HighwayNets and DenseNets: special convolutional layers to train Deep Networks.
• Image Segmentation: a brief history of CNNs in Image Segmentation, from R-CNN to Mask R-CNN.
• CycleGAN: understanding and implementing CycleGAN in TensorFlow.
• Model Stacking: combine several trained models to build a better stacked model. 1, 2, 3.
• Reinforcement Learning: repo with the implementation of Reinforcement Learning Algorithms, exercises, solutions and links to their theory.
• Evolution Strategies: a visual explanation of some Evolution Strategies.
• Network Science: online textbook for network science. From graph theory to advanced network algorithms.
• The Matrix Calculus You Need For Deep Learning: this paper is an attempt to explain all the matrix calculus you need in order to understand the training of deep neural networks.
• Time Series Analysis
• Data Science Glossary on Kaggle: glossary of data science models, techniques and tools shared on kaggle kernels.

Blogs

• Machine Learning Is Fun: practical examples to get started with Machine Learning and its applications.
• Machine Learning For Artists: examples and projects for Machine Learning applications in art.
• Kaggle Blog: articles about the winner solutions in its competitions and some tutorials.
• PyImageSearch: Computer Vision articles and tutorials.

Mailing lists

• Python Weekly, DataScience Weekly, AIWeekly, Kaggle Blog: subscribe here to read news, tutorials and projects related with Data Science.
• Bonilista (Spanish): opinion articles related with the technology and Startup world.

Youtube Channels

• Siraj Raval: lots of tutorials and interesting applications for Machine Learning and Neural Networks.
• Two Minutes Paper: amazing papers summaries about Computer Graphics and Artificial Intelligence

Projects and Tools:

• Keras: a high-level neural networks API for Python with TensorFlow or Theano backend. This repo contains lots of resources, tutorials and examples.
• Python Plays GTA V: apply Computer Vision and Machine Learning step by step in a complex environment.
• Face Recognition: find, identify and manipulate faces with this simple library. This is another great repo with interesting face recognition tools.
• Deep Feature Flow for Video Recognition: a simple, fast, accurate, and end-to-end framework for video recognition.
• Open AI RL Algorithms: a set of high-quality implementations of Reinforcement Learning algorithms.
• Neural Style Transfer: resources to understand and implement Neural Style Transfer.
• Prophet: tool for producing high quality forecasts for time series data that has multiple seasonality with linear or non-linear growth.

Online courses compilations

• Custom Data Science Master
• How To Become A Machine Learning Engineer
• Dive into Deep Learning with 15 free online courses
• Every single Machine Learning Course on the Internet ranked
• All AI resources at one place

Other resources:

• Deep Learning Papers Reading Roadmap
• Awesome Deep Learning Papers: a curated list of the most cited deep learning papers (since 2012).
• Papers with Code: trending ML researchs and the code to implement it.
• Kaggle: a highly recommended platform with data science competitions, datasets and forums.
• OpenAI: great environments and resources to use Reinforcement Learning algorithms.
• Awesome Python: a curated list of awesome Python frameworks, libraries, software and resources.
• Awesome NLP: A curated list of resources dedicated to Natural Language Processing (NLP).
• Neural Network architectures Cheat Sheet: all kinds of Neural Network architectures briefly explained.
• Cheat Sheets for Machine Learning and Deep Learning Engineers
• More Cheat Sheets
• Machine Learning Glossary by Google Developers.

If you have any other interesting resource to stay up-to-date with Machine Learning don’t hesitate to share it. Leave a comment here .