Awesome ML Resources
Curated list of resources on various ML topics found after browsing through lots and lots of materials over the years.
Pre requisites for ML:
- Linear Algebra refresher: http://cs229.stanford.edu/section/cs229-linalg.pdf
- Python numpy refresher: https://cs231n.github.io/python-numpy-tutorial/
Machine Learning Topics:
- Optimizers: https://ruder.io/optimizing-gradient-descent/
- SVM and kernel functions: https://www.youtube.com/watch?v=XUj5JbQihlU
- Xgboost: https://youtu.be/Vly8xGnNiWs
- All topics revision: http://cs229.stanford.edu/syllabus.html
Deep Learning Topics:
-
Neural networks : http://karpathy.github.io/neuralnets/
-
Backprop: https://cs231n.github.io/optimization-2/
-
RNN: http://karpathy.github.io/2015/05/21/rnn-effectiveness/
-
LSTM : http://karpathy.github.io/2015/05/21/rnn-effectiveness/ 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
-
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
Particular to computer vision
- 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
Reinforcement learning
- UCL course by David Silver: https://www.davidsilver.uk/teaching/
- Hands on: https://pythonprogramming.net/q-learning-algorithm-reinforcement-learning-python-tutorial/
NLP
- Tokenizers: https://blog.floydhub.com/tokenization-nlp/
GNNs:
- https://www.youtube.com/watch?v=8owQBFAHw7E
- https://twitter.com/PetarV_93/status/1306689702020382720
Top conferences:
- ICLR: https://iclr.cc/
- Neurips: https://nips.cc/
- ICML: https://icml.cc/
- ICCV: http://iccv2019.thecvf.com/
Practical/ hands on:
- Kaggle (for ML contests/ Data Science kernels): https://www.kaggle.com/
- Google Colab (with free GPU/ TPU): https://colab.research.google.com/
- AIcrowd: https://www.aicrowd.com/
