dair-ai/ML-YouTube-Courses
A repository to index and organize the latest machine learning courses found on YouTube.
| repo name | dair-ai/ML-YouTube-Courses |
| repo link | https://github.com/dair-ai/ML-YouTube-Courses |
| homepage | |
| language | |
| size (curr.) | 48 kB |
| stars (curr.) | 2597 |
| created | 2021-06-25 |
| license | |
📺 ML YouTube Courses
At dair.ai we ❤️ open education. We are excited to share some of the best and most recent machine learning courses available on YouTube.
Course List:
- Stanford CS229: Machine Learning
- Applied Machine Learning
- Machine Learning with Graphs (Stanford)
- Probabilistic Machine Learning
- Introduction to Deep Learning (MIT)
- Deep Learning: CS 182
- Deep Unsupervised Learning
- NYU Deep Learning SP21
- CS224N: Natural Language Processing with Deep Learning
- CMU Neural Networks for NLP
- Multilingual NLP
- Advanced NLP
- Deep Learning for Computer Vision
- Deep Reinforcement Learning
- Full Stack Deep Learning
- AMMI Geometric Deep Learning Course (2021)
Stanford CS229: Machine Learning
To learn some of the basics of ML:
• Linear Regression and Gradient Descent • Logistic Regression • Naive Bayes • SVMs • Kernels • Decision Trees • Introduction to Neural Networks • Debugging ML Models …
Applied Machine Learning
To learn some of the most widely used techniques in ML:
• Optimization and Calculus • Overfitting and Underfitting • Regularization • Monte Carlo Estimation • Maximum Likelihood Learning • Nearest Neighbours …
Machine Learning with Graphs (Stanford)
To learn some of the latest graph techniques in machine learning:
• PageRank • Matrix Factorizing • Node Embeddings • Graph Neural Networks • Knowledge Graphs • Deep Generative Models for Graphs …
Probabilistic Machine Learning
To learn the probabilistic paradigm of ML:
• Reasoning about uncertainty • Continuous Variables • Sampling • Markov Chain Monte Carlo • Gaussian Distributions • Graphical Models • Tuning Inference Algorithms …
Introduction to Deep Learning
To learn some of the fundamentals of deep learning:
• Introduction to Deep Learning
Deep Learning: CS 182
To learn some of the widely used techniques in deep learning:
• Machine Learning Basics • Error Analysis • Optimization • Backpropagation • Initialization • Batch Normalization • Style transfer • Imitation Learning …
Deep Unsupervised Learning
To learn the latest and most widely used techniques in deep unsupervised learning:
• Autoregressive Models • Flow Models • Latent Variable Models • Self-supervised learning • Implicit Models • Compression …
NYU Deep Learning SP21
To learn some of the advanced techniques in deep learning:
• Neural Nets: rotation and squashing • Latent Variable Energy Based Models • Unsupervised Learning • Generative Adversarial Networks • Autoencoders …
CS224N: Natural Language Processing with Deep Learning
To learn the latest approaches for deep leanring based NLP: • Dependency parsing • Language models and RNNs • Question Answering • Transformers and pretraining • Natural Language Generation • T5 and Large Language Models • Future of NLP …
CMU Neural Networks for NLP
To learn the latest neural network based techniques for NLP: • Language Modeling • Efficiency tricks • Conditioned Generation • Structured Prediction • Model Interpretation • Advanced Search Algorithms …
Multilingual NLP
To learn the latest concepts for doing multilingual NLP:
• Typology • Words, Part of Speech, and Morphology • Advanced Text Classification • Machine Translation • Data Augmentation for MT • Low Resource ASR • Active Learning …
Advanced NLP
To learn advanced concepts in NLP:
• Attention Mechanisms • Transformers • BERT • Question Answering • Model Distillation • Vision + Language • Ethics in NLP • Commonsense Reasoning …
Deep Learning for Computer Vision
To learn some of the fundamental concepts in CV:
• Introduction to deep learning for CV • Image Classification • Convolutional Networks • Attention Networks • Detection and Segmentation • Generative Models …
AMMI Geometric Deep Learning Course (2021)
To learn about concepts in geometric deep learning:
• Learning in High Dimensions • Geometric Priors • Grids • Manifolds and Meshes • Sequences and Time Warping …
Deep Reinforcement Learning
To learn the latest concepts in deep RL:
• Intro to RL • RL algorithms • Real-world sequential decision making • Supervised learning of behaviors • Deep imitation learning • Cost functions and reward functions …
Full Stack Deep Learning
To learn full-stack production deep learning:
• ML Projects • Infrastructure and Tooling • Experiment Managing • Troubleshooting DNNs • Data Management • Data Labeling • Monitoring ML Models • Web deployment …
Introduction to Deep Learning and Deep Generative Models
Covers the fundamental concepts of deep learning
• Single-layer neural networks and gradient descent • Multi-layer neura networks and backpropagation • Convolutional neural networks for images • Recurrent neural networks for text • autoencoders, variational autoencoders, and generative adversarial networks • encoder-decoder recurrent neural networks and transformers • PyTorch code examples
🔗 Link to Course 🔗 Link to Materials
What’s Next?
There are many plans to keep improving this collection. For instance, I will be sharing notes and better organizing individual lectures in a way that provides a bit of guidance for those that are getting started with machine learning.
If you are interested to contribute, feel free to open a PR with links to all individual lectures for each course. It will take a bit of time, but I have plans to do many things with these individual lectures. We can summarize the lectures, include notes, provide additional reading material, include difficulty of content, etc.
