What is this?
A topic-wise curated list of machine learning and deep learning tutorials, articles and other resources, maintained as a single GitHub README.
A topic-wise curated list of machine learning and deep learning tutorials, articles and other resources, maintained as a single GitHub README.Is it a course I work through, or just a big index of links?Just an index: it hosts no lessons of its own — it organizes links to external tutorials, university courses, blogs, books and cheat sheets, and also points to other awesome lists, including curated R and Python tutorial lists for data science.
Just an index: it hosts no lessons of its own — it organizes links to external tutorials, university courses, blogs, books and cheat sheets, and also points to other awesome lists, including curated R and Python tutorial lists for data science.Where should someone new to machine learning actually start in a list this size?The Introduction section gathers entry points: Andrew Ng's Stanford machine learning course, 'An Introduction to Statistical Learning', and an in-depth introduction to machine learning in 15 hours of expert videos.
The Introduction section gathers entry points: Andrew Ng's Stanford machine learning course, 'An Introduction to Statistical Learning', and an in-depth introduction to machine learning in 15 hours of expert videos.Is the content still fresh, or should I expect dated links?Expect some rust: the repo dates back to 2015 and gives no update schedule, and entries like Oxford's 2015 video lectures show their age — check that links still work before relying on them.
Expect some rust: the repo dates back to 2015 and gives no update schedule, and entries like Oxford's 2015 video lectures show their age — check that links still work before relying on them.Which topics does the list cover?Very broad ground: intro machine learning and statistics, classification, linear and logistic regression, model validation and resampling, deep learning and its frameworks, NLP, computer vision, SVMs, reinforcement learning, decision trees, ensembles and Bayesian machine learning.
Very broad ground: intro machine learning and statistics, classification, linear and logistic regression, model validation and resampling, deep learning and its frameworks, NLP, computer vision, SVMs, reinforcement learning, decision trees, ensembles and Bayesian machine learning.How deep does the deep learning coverage go, and which frameworks does it include?There is a large Deep Learning section: fast.ai courses, a deep learning papers reading roadmap, the 'Neural Networks and Deep Learning' online book, and per-architecture tutorials — feed-forward nets, RNN/LSTM, autoencoders and CNNs. Frameworks covered include TensorFlow, Theano, Torch, Caffe and Keras/Lasagne.
There is a large Deep Learning section: fast.ai courses, a deep learning papers reading roadmap, the 'Neural Networks and Deep Learning' online book, and per-architecture tutorials — feed-forward nets, RNN/LSTM, autoencoders and CNNs. Frameworks covered include TensorFlow, Theano, Torch, Caffe and Keras/Lasagne.Does it help with data science job interviews or Kaggle competitions?Yes. A dedicated Interview Resources section collects lists like '41 Essential Machine Learning Interview Questions (with answers)', and the Kaggle section gathers competition write-ups, including 'How to Rank 10% in Your First Kaggle Competition'.
Yes. A dedicated Interview Resources section collects lists like '41 Essential Machine Learning Interview Questions (with answers)', and the Kaggle section gathers competition write-ups, including 'How to Rank 10% in Your First Kaggle Competition'.Can I copy the list and reuse it for my own project?Yes — the repository is released under CC0-1.0, a public-domain-style license, so the list can be reused, modified and republished without attribution.
Yes — the repository is released under CC0-1.0, a public-domain-style license, so the list can be reused, modified and republished without attribution.What are similar projects?DeepLearning, awesome-deep-learning, awesome-tensorflow, 500-AI-Machine-learning-Deep-learning-Computer-vision-NLP-Projects-with-code, the-incredible-pytorch, awesome-deep-learning-papers, awesome-machine-learning, Awesome-pytorch-list
DeepLearning, awesome-deep-learning, awesome-tensorflow, 500-AI-Machine-learning-Deep-learning-Computer-vision-NLP-Projects-with-code, the-incredible-pytorch, awesome-deep-learning-papers, awesome-machine-learning, Awesome-pytorch-listWhich topics is it in?machinelearning, kaggle, r, deep-learning-tutorial, deeplearning, deep-neural-networks, tutorials, neural-networks, cheatsheet, interview-questions, reinforcement-learning, neural-network
machinelearning, kaggle, r, deep-learning-tutorial, deeplearning, deep-neural-networks, tutorials, neural-networks, cheatsheet, interview-questions, reinforcement-learning, neural-network