Single Variable Calculus
Notes from the MIT OCW Single Variable Calculus course by Herb Gross.
Rates of change and the basic definition of limits and go onto cover differentiation, integration, infinite series and more.
Rates of change and the basic definition of limits and go onto cover differentiation, integration, infinite series and more.
Multivariable Calculus
Notes from the MIT OCW Multivariable Calculus course by Herb Gross.
Multivariable limits, partial derivatives, multiple integration, vector calculus, complex numbers and differential equations.
Multivariable limits, partial derivatives, multiple integration, vector calculus, complex numbers and differential equations.
Linear Algebra
Notes from the MIT OCW Linear Algebra course by Gilbert Strang.
Vector spaces, elimination, decompositions, projections, eigenspaces, determinants and a broad overview of applications.
Vector spaces, elimination, decompositions, projections, eigenspaces, determinants and a broad overview of applications.
Probability
Notes from the Harvard Stats 110 course by Joe Blitzstein.
Random variables, axioms of probability, distributions, operating on distributions, independence, conditional probability.
Random variables, axioms of probability, distributions, operating on distributions, independence, conditional probability.
Statistics
Notes from the CMU Intermediate Statistics course by Larry Wasserman.
Inequalities, uniform bounds, convergence, sufficiency, likelihood, point estimates, hypothesis testing, confidence sets.
Inequalities, uniform bounds, convergence, sufficiency, likelihood, point estimates, hypothesis testing, confidence sets.
Other Useful Math for ML
Miscellaneous notes on other topics.
Convex optimization, lagrange multipliers, multivariate gaussians, gaussian processes, associated intuitions.
Convex optimization, lagrange multipliers, multivariate gaussians, gaussian processes, associated intuitions.
Deep Learning: the book
Notes from the Deep Learning book by Goodfellow et al.
Cover to cover book notes on intuitions, practical methodology, optimization, regularization, recent research avenues.
Cover to cover book notes on intuitions, practical methodology, optimization, regularization, recent research avenues.
Neural Networks and Deep Learning: online book
Notes from the online book by Michael Nielsen.
Intuitions behind neural nets, the mechanics of backprop, improving the way networks learn, challenges in training networks.
Intuitions behind neural nets, the mechanics of backprop, improving the way networks learn, challenges in training networks.
Machine Learning
Notes from the Stanford CS 229 course by Andrew Ng.
The basics, supervised learning, unsupervised learning, reinforcement learning, learning theory and practical advice.
The basics, supervised learning, unsupervised learning, reinforcement learning, learning theory and practical advice.
Deep Reinforcement Learning
Notes from the UC Berkeley course by Sergey Levine.
Model based methods, policy gradient methods, Q-learning, inverse RL, recent research overview of meta learning and more.
Model based methods, policy gradient methods, Q-learning, inverse RL, recent research overview of meta learning and more.
Tensorflow & OpenAI Gym
Notes on the main concepts in Tensorflow and OpenAI Gym.
Computational graphs, define and run, define by run, tensorboard, automatic differentiation, OpenAI gym overview.
Computational graphs, define and run, define by run, tensorboard, automatic differentiation, OpenAI gym overview.
Deep Learning for Vision
Notes from the Stanford course by Fei Fei Li and Andrej Karpathy.
NLP with Deep Learning
Notes from the Stanford course by Chris Manning and Richard Socher.
Miscellaneous Topics in ML
Topics not covered by other courses. Random forests, structured deep learning, collaborative filtering.
Operating Systems
Threads, Processes, Scheduling, Parallelism, Virtual Memroy, File Systems, Databases and more.