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<!doctype html>
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<title>ML Sparknotes</title>
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<h2 class="mdl-card__title-text">Single Variable Calculus</h2>
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Notes from the MIT OCW Single Variable Calculus course by Herb Gross.
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Rates of change and the basic definition of limits and go onto cover differentiation, integration, infinite series and more.
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<h2 class="mdl-card__title-text">Multivariable Calculus</h2>
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Notes from the MIT OCW Multivariable Calculus course by Herb Gross.
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Multivariable limits, partial derivatives, multiple integration, vector calculus, complex numbers and differential equations.
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Notes from the MIT OCW Linear Algebra course by Gilbert Strang.
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Vector spaces, elimination, decompositions, projections, eigenspaces, determinants and a broad overview of applications.
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Notes from the Harvard Stats 110 course by Joe Blitzstein.
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Random variables, axioms of probability, distributions, operating on distributions, independence, conditional probability.
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<h2 class="mdl-card__title-text">Statistics</h2>
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Notes from the CMU Intermediate Statistics course by Larry Wasserman.
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Inequalities, uniform bounds, convergence, sufficiency, likelihood, point estimates, hypothesis testing, confidence sets.
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<h2 class="mdl-card__title-text">Other Useful Math for ML</h2>
</div>
<div class="mdl-card__supporting-text">
Miscellaneous notes on other topics.
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Convex optimization, lagrange multipliers, multivariate gaussians, gaussian processes, associated intuitions.
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<h2 class="mdl-card__title-text">Deep Learning: the book</h2>
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Notes from the Deep Learning book by Goodfellow et al.
<br><br>
Cover to cover book notes on intuitions, practical methodology, optimization, regularization, recent research avenues.
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<h2 class="mdl-card__title-text">Neural Networks and Deep Learning: online book</h2>
</div>
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Notes from the online book by Michael Nielsen.
<br><br>
Intuitions behind neural nets, the mechanics of backprop, improving the way networks learn, challenges in training networks.
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<h2 class="mdl-card__title-text">Machine Learning</h2>
</div>
<div class="mdl-card__supporting-text">
Notes from the Stanford CS 229 course by Andrew Ng.
<br><br>
The basics, supervised learning, unsupervised learning, reinforcement learning, learning theory and practical advice.
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<h2 class="mdl-card__title-text">Deep Reinforcement Learning</h2>
</div>
<div class="mdl-card__supporting-text">
Notes from the UC Berkeley course by Sergey Levine.
<br><br>
Model based methods, policy gradient methods, Q-learning, inverse RL, recent research overview of meta learning and more.
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<h2 class="mdl-card__title-text">Tensorflow & OpenAI Gym</h2>
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Notes on the main concepts in Tensorflow and OpenAI Gym.
<br><br>
Computational graphs, define and run, define by run, tensorboard, automatic differentiation, OpenAI gym overview.
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<h2 class="mdl-card__title-text">Deep Learning for Vision</h2>
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Notes from the Stanford course by Fei Fei Li and Andrej Karpathy.
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Notes from the Stanford course by Chris Manning and Richard Socher.
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<h2 class="mdl-card__title-text">Computer Vision</h2>
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Notes from the Georgia Tech course Aaron Bobick. (not deep learning focused)
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Notes from the UC Berkeley course by Pieter Abeel.
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Notes from the The CUDA Handbook by Nicholas Wilt.
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