Welcome! This is a place for people to learn more about machine learning techniques, discuss applications and ask questions.
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Google open sources tools to support AI model development
Google is launching Jetstream, a new engine to run generative AI models, and MaxDiffusion, a collection of reference implementations of various diffusion models.
Introduction to Kernel Methods for Machine Learning
Kernel methods give a systematic and principled approach to training learning machines and the good generalization performance achieved can be readily justified using statistical learning theory or Bayesian arguments. We describe how to use kernel methods for classification, regression and novelty detection and in each case we find that training can be reduced to optimization of a convex cost function.
The Kernel Cookbook: Advice on Covariance functions
If you've ever asked yourself: "How do I choose the covariance function for a Gaussian process?" this is the page for you. Here you'll find concrete advice on how to choose a covariance function for your problem, or better yet, make your own.
An Intuitive Tutorial to Gaussian Processes Regression
This tutorial aims to provide an intuitive introduction to Gaussian process regression (GPR). GPR models have been widely used in machine learning applications due to their representation flexibility and inherent capability to quantify uncertainty over predictions. The tutorial starts with explainin...
This tutorial aims to provide an intuitive understanding of the Gaussian processes regression. Gaussian processes regression (GPR) models have been widely used in machine learning applications because of their representation flexibility and inherent uncertainty measures over predictions.
Understanding UMAP - Google PAIR
UMAP is a new dimensionality reduction technique that offers increased speed and better preservation of global structure.
Has nice interactive examples and UMAP vs t-SNE
MIT OpenCourseWare: Introduction To Machine Learning
This course introduces principles, algorithms, and applications of machine learning from the point of view of modeling and prediction. It includes formulation of learning problems and concepts of representation, over-fitting, and generalization. These concepts are exercised in supervised learning an...
Style Guide for Python Code: PEP 8
This document gives coding conventions for the Python code comprising the standard library in the main Python distribution. Please see the companion informational PEP describing style guidelines for the C code in the C implementation of Python.
MIT OpenCourseWare: Statistical Learning Theory
The main goal of this course is to study the generalization ability of a number of popular machine learning algorithms such as boosting, support vector machines and neural networks. Topics include Vapnik-Chervonenkis theory, concentration inequalities in product spaces, and other elements of empiric...
MIT OpenCourseWare: Mathematics Of Machine Learning
Broadly speaking, Machine Learning refers to the automated identification of patterns in data. As such it has been a fertile ground for new statistical and algorithmic developments. The purpose of this course is to provide a mathematically rigorous introduction to these developments with emphasis on...
Broadly speaking, Machine Learning refers to the automated identification of patterns in data. As such it has been a fertile ground for new statistical and algorithmic developments. The purpose of this course is to provide a mathematically rigorous introduction to these developments with emphasis on methods and their analysis.
Durham University Materials for COMP3547 (Deep Learning) and COMP3667 (Reinforcement Learning) from Dr. Robert Lieck
lecture materials. Contribute to cwkx/materials development by creating an account on GitHub.
Includes lectures, lecture notes and assignments.
Lectures for Deep Learning: https://www.youtube.com/playlist?list=PLMsTLcO6etti_SObSLvk9ZNvoS_0yia57
Lectures for Reinforcement Learning: https://www.youtube.com/playlist?list=PLMsTLcO6ettgmyLVrcPvFLYi2Rs-R4JOE
Rules of Machine Learning from Google
A good set of best practices for deployment that isn't language-specific
Coding Practices for Python/ML
:bathtub: Clean Code concepts adapted for Python. Contribute to zedr/clean-code-python development by creating an account on GitHub.
Coding nowadays is a big part of ML and while it's important that the model works well, it's also important that the code is written properly too.
Link is the general python version, ML-specific version here: https://github.com/davified/clean-code-ml
Video version: https://bit.ly/2yGDyqT
Tutorial: Image Recognition with CNN in Matlab
Introduces neural networks, the convolution operation, a few critical machine learning concepts and some state-of-the-art CNN models. Includes a hands-on Matlab tutorial (and code) demonstrating the model configuration, training process, and performance evaluation using the MNIST dataset.
Tutorial: State of Charge Estimation with EKF and SVSF in Matlab
This tutorial describes the process for the state of charge (SOC) estimation of Li-Ion cells using an equivalent circuit model. It helps students create and run a SOC estimation strategy based on the 3rd-order R-RC model in MATLAB-Simulink. The tutorial starts with a general overview of state estimation using the extended Kalman filter (EKF) and the novel smooth variable structure filter (SVSF) method.
Standford University Cheat Sheets for ML (web version)
I'm not sure if I'd call a 10+ page pdf a "cheat sheet" but they are good resources
Mathematics for Neural Networks
Can't say I agree with all of this 100% (I'd put backpropagation in the math side, add in model evaluation, remove convex optimization, etc) plus it's kind of an oversimplification but the basics are there
Materials from CORNELL CS4780/CS5780: Machine Learning for Intelligent Systems
Lecture notes: https://www.cs.cornell.edu/courses/cs4780/2018fa/syllabus/
Recorded lectures: https://www.youtube.com/playlist?list=PLl8OlHZGYOQ7bkVbuRthEsaLr7bONzbXS
