Machine-Learning-Resources
Curated list of resources
Neural Networks
General
- Neural networks class - Université de Sherbrooke ; Hugo Larochelle - Basic approaches, CRFS, RBMs, Autoencoders, Vision, NLP, Sparse coding
- A guide to convolution arithmetic for deep learning - A good guide to various elements of convNets.
- Autograd github - Autograd - efficiently compute derivates of python and numpy code
- Yandex Deep learning - Yandex deep learning repository
Tensorflow
- Tensorflow for deep learning research - Standford course on tensorflow. Lecture slides only.
Deep NLP
- explosion.ai Deep nlp pipeline - Good overview of sota deep nlp approach
- Stanford Deep NLP - Deep NLP course with lecture videos.
Variational Inference
- UCL gatsby unit repository - Large amount of foundational papers in Variational Bayes
Differentiable Inference
*Toronto Differentiable Inference course - Lecture notes only, advanced course covering VAEs, GANs etc.
Variational Autoencoders
- kvfrans VAE introduction - Basic overview of VAEs
- Wiseodds blog on VAE - More mathematical introduction to VAEs
- Shakrim's blog on reparameterisation trick - Useful discussion on reparamerterising trick which is used in VAEs.
- jaan.io VAEs - What is VAE tutorial.
Generative Adversarial Networks
Probabilistic Programming
- Statistical Rethinking in PyMC3 github - Bayesian statistics & modelling
- Bayesian Neural Network introduction - Introduction to edward and bayesian neural networks
General Machine Learning
- Harvard AM207 - Advanced course surveying variety of ML methods. Lecture notes only.
- Ghahramani. Course on ML and non-parametric bayes
- Cambridge Proababilistic Machine Learning
Metrics
- What is the ROC curve - Good explanation of what the ROC curve is.
Python
- Python to Numpy - Excellent book on numpy and efficient python. Worth a read even for experienced users
- Python Plotting for Exploratory Data Analysis - Python plotting for EDA
Application focussed
- Machine Learning for Healthcare - MIT course 2017. Lecture notes only.