A collection of video resources for machine learning

Overview

Machine Learning Videos

This is a collection of recorded talks at machine learning conferences, workshops, seminars, summer schools, and miscellaneous programs. It is actively maintained; please add to this list by submitting a pull request!

This collection builds off the original compilation from a blog post.

General

In general the following are excellent resources:

  • VideoLectures.NET is the primary video archive for machine learning. Much of its content focuses on the field, incorporating conferences, workshops, lectures, and even discussions.
  • TechTalks.tv is the second most used archive. Notably, many ICML videos are located here.
  • Slideslive is recently being used by the ML community. Eg: ICML, NeurIPS, ICLR.
  • Youtube contains a few, such as certain AISTATS and ICLR years. It's best for collecting lectures, such as by Nando de Freitas and Alex Smola. Also, the user mathematicalmonk has created several basic mathematical and probability overviews for many ML methods.
  • Models, Inference, & Algorithms at the Broad Institute of MIT and Harvard has slide decks and a growing playlist on machine learning for biomedical research.

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Owner
Dustin Tran
Dustin Tran
A linear equation solver using gaussian elimination. Implemented for fun and learning/teaching.

A linear equation solver using gaussian elimination. Implemented for fun and learning/teaching. The solver will solve equations of the type: A can be

Sanjeet N. Dasharath 3 Feb 15, 2022
This is an implementation of the proximal policy optimization algorithm for the C++ API of Pytorch

This is an implementation of the proximal policy optimization algorithm for the C++ API of Pytorch. It uses a simple TestEnvironment to test the algorithm

Martin Huber 59 Dec 09, 2022
CVXPY is a Python-embedded modeling language for convex optimization problems.

CVXPY The CVXPY documentation is at cvxpy.org. We are building a CVXPY community on Discord. Join the conversation! For issues and long-form discussio

4.3k Jan 08, 2023
Adversarial Framework for (non-) Parametric Image Stylisation Mosaics

Fully Adversarial Mosaics (FAMOS) Pytorch implementation of the paper "Copy the Old or Paint Anew? An Adversarial Framework for (non-) Parametric Imag

Zalando Research 120 Dec 24, 2022
AutoOED: Automated Optimal Experiment Design Platform

AutoOED is an optimal experiment design platform powered with automated machine learning to accelerate the discovery of optimal solutions. Our platform solves multi-objective optimization problems an

Yunsheng Tian 107 Jan 03, 2023
QuickAI is a Python library that makes it extremely easy to experiment with state-of-the-art Machine Learning models.

QuickAI is a Python library that makes it extremely easy to experiment with state-of-the-art Machine Learning models.

152 Jan 02, 2023
Open MLOps - A Production-focused Open-Source Machine Learning Framework

Open MLOps - A Production-focused Open-Source Machine Learning Framework Open MLOps is a set of open-source tools carefully chosen to ease user experi

Data Revenue 590 Dec 28, 2022
Time-series momentum for momentum investing strategy

Time-series-momentum Time-series momentum strategy. You can use the data_analysis.py file to find out the best trigger and window for a given asset an

Victor Caldeira 3 Jun 18, 2022
Falken provides developers with a service that allows them to train AI that can play their games

Falken provides developers with a service that allows them to train AI that can play their games. Unlike traditional RL frameworks that learn through rewards or batches of offline training, Falken is

Google Research 223 Jan 03, 2023
Magenta: Music and Art Generation with Machine Intelligence

Magenta is a research project exploring the role of machine learning in the process of creating art and music. Primarily this involves developing new

Magenta 18.1k Dec 30, 2022
Relevance Vector Machine implementation using the scikit-learn API.

scikit-rvm scikit-rvm is a Python module implementing the Relevance Vector Machine (RVM) machine learning technique using the scikit-learn API. Quicks

James Ritchie 204 Nov 18, 2022
Massively parallel self-organizing maps: accelerate training on multicore CPUs, GPUs, and clusters

Somoclu Somoclu is a massively parallel implementation of self-organizing maps. It exploits multicore CPUs, it is able to rely on MPI for distributing

Peter Wittek 239 Nov 10, 2022
Home repository for the Regularized Greedy Forest (RGF) library. It includes original implementation from the paper and multithreaded one written in C++, along with various language-specific wrappers.

Regularized Greedy Forest Regularized Greedy Forest (RGF) is a tree ensemble machine learning method described in this paper. RGF can deliver better r

RGF-team 363 Dec 14, 2022
Python based GBDT implementation

Py-boost: a research tool for exploring GBDTs Modern gradient boosting toolkits are very complex and are written in low-level programming languages. A

Sberbank AI Lab 20 Sep 21, 2022
My project contrasts K-Nearest Neighbors and Random Forrest Regressors on Real World data

kNN-vs-RFR My project contrasts K-Nearest Neighbors and Random Forrest Regressors on Real World data In many areas, rental bikes have been launched to

1 Oct 28, 2021
A repository for collating all the resources such as articles, blogs, papers, and books related to Bayesian Statistics.

A repository for collating all the resources such as articles, blogs, papers, and books related to Bayesian Statistics.

Aayush Malik 80 Dec 12, 2022
This handbook accompanies the course: Machine Learning with Hung-Yi Lee

This handbook accompanies the course: Machine Learning with Hung-Yi Lee

RenChu Wang 472 Dec 31, 2022
MosaicML Composer contains a library of methods, and ways to compose them together for more efficient ML training

MosaicML Composer MosaicML Composer contains a library of methods, and ways to compose them together for more efficient ML training. We aim to ease th

MosaicML 2.8k Jan 06, 2023
Test symmetries with sklearn decision tree models

Test symmetries with sklearn decision tree models Setup Begin from an environment with a recent version of python 3. source setup.sh Leave the enviro

Rupert Tombs 2 Jul 19, 2022
Simple, light-weight config handling through python data classes with to/from JSON serialization/deserialization.

Simple but maybe too simple config management through python data classes. We use it for machine learning.

Eren Gölge 67 Nov 29, 2022