Awesome ML resources

Curated list of resources on various ML topics found after browsing through lots and lots of materials over the years.

Deep Learning Topics:

Neural networks : http://karpathy.github.io/neuralnets/
Backprop: https://cs231n.github.io/optimization-2/
CNN: https://cs231n.github.io/convolutional-networks/
RNN: http://karpathy.github.io/2015/05/21/rnn-effectiveness/
LSTM : https://colah.github.io/posts/2015-08-Understanding-LSTMs/ accompanied with CS231n lecture 10
Sequence to sequence models: https://lilianweng.github.io/lil-log/2018/06/24/attention-attention.html or https://www.youtube.com/watch?v=XXtpJxZBa2c&list=PLoROMvodv4rOhcuXMZkNm7j3fVwBBY42z&index=8
Attention: https://jalammar.github.io/visualizing-neural-machine-translation-mechanics-of-seq2seq-models-with-attention/
Transformers: Maths/implementation: http://nlp.seas.harvard.edu/2018/04/01/attention.html and https://www.tensorflow.org/tutorials/text/transformer or if you're looking for an overview: http://jalammar.github.io/illustrated-transformer/
Gaussian processes/ bayesian optimization: https://www.cs.cornell.edu/courses/cs4780/2018fa/lectures/lecturenote15.html

Faster rcnn : https://tryolabs.com/blog/2018/01/18/faster-r-cnn-down-the-rabbit-hole-of-modern-object-detection/ and flow with code: https://medium.com/@fractaldle/guide-to-build-faster-rcnn-in-pytorch-95b10c273439
Yolo: Entertaining paper- https://pjreddie.com/media/files/papers/YOLOv3.pdf
Centernet: https://medium.com/visionwizard/centernet-objects-as-points-a-comprehensive-guide-2ed9993c48bc

UCL course by David Silver: https://www.davidsilver.uk/teaching/
Hands on: https://pythonprogramming.net/q-learning-algorithm-reinforcement-learning-python-tutorial/

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