Mathematics for AI
📽 Mathematics for Ai | Persian Course
📽 Gilbert Strang lectures on Linear Algebra (MIT)
Machine Learning
▪️ Machine Learning Summer School 2013-2017 Tübingen
▪️ CORNELL CS4780 : Machine Learning for Intelligent Systems
▪️ Neural Networks for Machine Learning — Geoffrey Hinton, UofT [FULL COURSE]
▪️ Caltech CS156: Learning from Data
▪️ Stanford CS229: Machine Learning
▪️ Making Friends with Machine Learning
▪️ Introduction to Machine Learning (Tübingen)
▪️ Machine Learning Lecture (Stefan Harmeling)
▪️ Statistical Machine Learning (Tübingen)
▪️ Probabilistic Machine Learning
▪️ MIT 6.S897: Machine Learning for Healthcare (2019)
Deep Learning
▪️ Foundations of Deep Learning- Soheil Feizi
▪️ Deep Learning Summer School, Montreal 2015-2016
▪️ Neural Networks: Zero to Hero
▪️ MIT: Deep Learning for Art, Aesthetics, and Creativity
▪️ Stanford CS230: Deep Learning (2018)
▪️ Introduction to Deep Learning
▪️ CMU Introduction to Deep Learning (11-785)
Scientific Machine Learning
▪️ Parallel Computing and Scientific Machine Learning
Practical Machine Learning
▪️ LLMOps: Building Real-World Applications With Large Language Models
▪️ Evaluating and Debugging Generative AI
▪️ ChatGPT Prompt Engineering for Developers
▪️ LangChain for LLM Application Development
▪️ LangChain: Chat with Your Data
▪️ Building Systems with the ChatGPT API
▪️ LangChain & Vector Databases in Production
▪️ Practical Deep Learning for Coders
▪️ Machine Learning Engineering for Production (MLOps)
▪️ MIT Introduction to Data-Centric AI
Natural Language Processing ▪️ [UMass CS685: Advanced Natural Language Processing (Spring 2023) ]( ▪️ LLM Multimodel
▪️ XCS224U: Natural Language Understanding (2023)
▪️ Stanford CS25 - Transformers United
▪️ CS224N: Natural Language Processing with Deep Learning
▪️ CMU Neural Networks for NLP
▪️ CS224U: Natural Language Understanding
▪️ CMU Advanced NLP 2021/2022/2024
▪️ Advanced NLP
Computer Vision
▪️ CS231N: Convolutional Neural Networks for Visual Recognition
▪️ Deep Learning for Computer Vision
▪️ Deep Learning for Computer Vision (DL4CV)
▪️ Deep Learning for Computer Vision (neuralearn.ai)
Reinforcement Learning
▪️ Deep Reinforcement Learning
▪️ Reinforcement Learning Lecture Series (DeepMind)
▪️ Reinforcement Learning (Polytechnique Montreal, Fall 2021)
▪️ Stanford CS234: Reinforcement Learning
Graph Machine Learning
▪️ Machine Learning with Graphs (Stanford)
▪️ AMMI Geometric Deep Learning Course
Multi-Task Learning
▪️ Multi-Task and Meta-Learning (Stanford)
▪️ Machine Learning Summer School 2013 Tübingen
▪️ Machine Learning Summer School 2017 Tübingen
Others
▪️ MIT Deep Learning in Life Sciences
▪️ Self-Driving Cars (Tübingen)
▪️ Advanced Robotics (Berkeley)
▪ Machine Learning for Intelligent Systems
▪ lecture notes
▪ Official class webpage
▪️ Deep Learning Summer School, Montreal 2015
▪️ Deep Learning Summer School, Montreal 2015
📽 Gilbert Strang lectures on Linear Algebra (MIT)
🔗 Mathematics for Ai | Persian Course
An introductory course in machine learning that covers the basic theory, algorithms, and applications.
Lecture 1: The Learning Problem
Lecture 2: Is Learning Feasible?
Lecture 3: The Linear Model I
Lecture 4: Error and Noise
Lecture 5: Training versus Testing
Lecture 6: Theory of Generalization
Lecture 7: The VC Dimension
Lecture 8: Bias-Variance Tradeoff
Lecture 9: The Linear Model II
Lecture 10: Neural Networks
Lecture 11: Overfitting
Lecture 12: Regularization
Lecture 13: Validation
Lecture 14: Support Vector Machines
Lecture 15: Kernel Methods
Lecture 16: Radial Basis Functions
Lecture 17: Three Learning Principles
Lecture 18: Epilogue
Introduction to Machine Learning (CSC2515 - Fall 2021), Department of Computer Science, University of Toronto.
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 ...
A series of mini lectures covering various introductory topics in ML:
Explainability in AI
Classification vs. Regression
Precession vs. Recall
Statistical Significance
Clustering and K-means
Ensemble models ...
▪️ Neural Networks for Machine Learning — Geoffrey Hinton, UofT [FULL COURSE]
Course providing an in-depth overview of neural networks.
Backpropagation
Spelled-out intro to Language Modeling
Activation and Gradients
Becoming a Backprop Ninja
Diffusion models, LLMs, multi-modal models, reasoning, etc)
Covers the application of deep learning for art, aesthetics, and creativity.
Nostalgia -> Art -> Creativity -> Evolution as Data + Direction
Efficient GANs
Explorations in AI for Creativity
Neural Abstractions
Easy 3D Content Creation with Consistent Neural Fields ...
Covers the foundations of deep learning, how to build different neural networks(CNNs, RNNs, LSTMs, etc...), how to lead machine learning projects, and career advice for deep learning practitioners.
Deep Learning Intuition
Adversarial examples - GANs
Full-cycle of a Deep Learning Project
AI and Healthcare
Deep Learning Strategy
Interpretability of Neural Networks
Career Advice and Reading Research Papers
Deep Reinforcement Learning
🔗 Link to Course 🔗 Link to Materials
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
...
The course serves as a basic introduction to machine learning and covers key concepts in regression, classification, optimization, regularization, clustering, and dimensionality reduction.
Linear regression
Logistic regression
Regularization
Boosting
Neural networks
PCA
Clustering
...
Covers many fundamental ML concepts:
Bayes rule
From logic to probabilities
Distributions
Matrix Differential Calculus
PCA
K-means and EM
Causality
Gaussian Processes
...
The course covers the standard paradigms and algorithms in statistical machine learning.
KNN
Bayesian decision theory
Convex optimization
Linear and ridge regression
Logistic regression
SVM
Random Forests
Boosting
PCA
Clustering
...
This course covers topics such as how to:
Build and train deep learning models for computer vision, natural language processing, tabular analysis, and collaborative filtering problems
Create random forests and regression models
Deploy models
Use PyTorch, the world’s fastest growing deep learning software, plus popular libraries like fastai and Hugging Face
Foundations and Deep Dive to Diffusion Models
...
A seminar series on all sorts of topics related to building machine learning systems.
Specialization course on MLOPs by Andrew Ng.
Covers the emerging science of Data-Centric AI (DCAI) that studies techniques to improve datasets, which is often the best way to improve performance in practical ML applications. Topics include:
Data-Centric AI vs. Model-Centric AI
Label Errors
Dataset Creation and Curation
Data-centric Evaluation of ML Models
Class Imbalance, Outliers, and Distribution Shift
...
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
...
🎥 20 hours of video lectures
✨ 17 sets of slides
📖 Lecture notes
To learn the probabilistic paradigm of ML:
Reasoning about uncertainty
Continuous Variables
Sampling
Markov Chain Monte Carlo
Gaussian Distributions
Graphical Models
Tuning Inference Algorithms
...
This course introduces students to machine learning in healthcare, including the nature of clinical data and the use of machine learning for risk stratification, disease progression modeling, precision medicine, diagnosis, subtype discovery, and improving clinical workflows.
To learn some of the fundamentals of deep learning:
Introduction to Deep Learning
The course starts off gradually from MLPs (Multi Layer Perceptrons) and then progresses into concepts like attention and sequence-to-sequence models.
🔗 Link to Course 🔗 Lectures 🔗 Tutorials/Recitations
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
...
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
...
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
...
To learn about foundation models like GPT-3, CLIP, Flamingo, Codex, and DINO.
This course introduces the practical and theoretical principles of deep neural networks.
Computation graphs
Activation functions and loss functions
Training, regularization and data augmentation
Basic and state-of-the-art deep neural network architectures including convolutional networks and graph neural networks
Deep generative models such as auto-encoders, variational auto-encoders and generative adversarial networks
...
The Basics of Scientific Simulators
Introduction to Parallel Computing
Continuous Dynamics
Inverse Problems and Differentiable Programming
Distributed Parallel Computing
Physics-Informed Neural Networks and Neural Differential Equations
Probabilistic Programming, AKA Bayesian Estimation on Programs
Globalizing the Understanding of Models
🔴 Responsible AI
🔴 Chirp
🔴 Codey
🔴 MedPaLM 2
more
This course covers topics such as:
Contextual Word Representations
Information Retrieval
In-context learning
Behavioral Evaluation of NLU models
NLP Methods and Metrics
...
This course consists of lectures focused on Transformers, providing a deep dive and their applications
Introduction to Transformers
Transformers in Language: GPT-3, Codex
Applications in Vision
Transformers in RL & Universal Compute Engines
Scaling transformers
Interpretability with transformers
...
Learn about different NLP concepts and how to apply language models and Transformers to NLP:
What is Transfer Learning?
BPE Tokenization
Batching inputs
Fine-tuning models
Text embeddings and semantic search
Model evaluation
...
To learn the latest approaches for deep learning based NLP:
Dependency parsing
Language models and RNNs
Question Answering
Transformers and pretraining
Natural Language Generation
T5 and Large Language Models
Future of NLP
...
To learn the latest neural network based techniques for NLP:
Language Modeling
Efficiency tricks
Conditioned Generation
Structured Prediction
Model Interpretation
Advanced Search Algorithms
...
To learn the latest concepts in natural language understanding:
Grounded Language Understanding
Relation Extraction
Natural Language Inference (NLI)
NLU and Neural Information Extraction
Adversarial testing
...
To learn:
Basics of modern NLP techniques
Multi-task, Multi-domain, multi-lingual learning
Prompting + Sequence-to-sequence pre-training
Interpreting and Debugging NLP Models
Learning from Knowledge-bases
Adversarial learning
...
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
...
To learn advanced concepts in NLP:
Attention Mechanisms
Transformers
BERT
Question Answering
Model Distillation
Vision + Language
Ethics in NLP
Commonsense Reasoning
...
Stanford's Famous CS231n course. The videos are only available for the Spring 2017 semester. The course is currently known as Deep Learning for Computer Vision, but the Spring 2017 version is titled Convolutional Neural Networks for Visual Recognition.
Image Classification
Loss Functions and Optimization
Introduction to Neural Networks
Convolutional Neural Networks
Training Neural Networks
Deep Learning Software
CNN Architectures
Recurrent Neural Networks
Detection and Segmentation
Visualizing and Understanding
Generative Models
Deep Reinforcement Learning
🔗 Link to Course 🔗 Link to Materials
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
To learn modern methods for computer vision:
CNNs
Advanced PyTorch
Understanding Neural Networks
RNN, Attention and ViTs
Generative Models
GPU Fundamentals
Self-Supervision
Neural Rendering
Efficient Architectures
To learn modern methods for computer vision:
Self-Supervised Learning
Neural Rendering
Efficient Architectures
Machine Learning Operations (MLOps)
Modern Convolutional Neural Networks
Transformers in Vision
Model Deployment
To learn about concepts in geometric deep learning:
Learning in High Dimensions
Geometric Priors
Grids
Manifolds and Meshes
Sequences and Time Warping
...
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
...
The Deep Learning Lecture Series is a collaboration between DeepMind and the UCL Centre for Artificial Intelligence.
Introduction to RL
Dynamic Programming
Model-free algorithms
Deep reinforcement learning
...
Learn to build modern software with LLMs using the newest tools and techniques in the field.
You'll learn:
Instrument A Jupyter Notebook
Manage Hyperparameters Config
Log Run Metrics
Collect artifacts for dataset and model versioning
Log experiment results
Trace prompts and responses for LLMs
...
Learn how to use a large language model (LLM) to quickly build new and powerful applications.
You'll learn:
Models, Prompt, and Parsers
Memories for LLMs
Chains
Question Answering over Documents
Agents
You'll learn about:
Document Loading
Document Splitting
Vector Stores and Embeddings
Retrieval
Question Answering
Chat
Learn how to automate complex workflows using chain calls to a large language model.
Learn how to use LangChain and Vector DBs in Production:
LLMs and LangChain
Learning how to Prompt
Keeping Knowledge Organized with Indexes
Combining Components Together with Chains
...
Learn how to build LLM-powered applications using LLM APIs
Unpacking LLM APIs
Building a Baseline LLM Application
Enhancing and Optimizing LLM Applications
...
To learn how to build and deploy LLM-powered applications:
Learn to Spell: Prompt Engineering
LLMOPs
UX for Language User Interfaces
Augmented Language Models
Launch an LLM App in One Hour
LLM Foundations
Project Walkthrough: askFSDL
...
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
...
Covers the fundamental concepts of deep learning
Single-layer neural networks and gradient descent
Multi-layer neural 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
Covers the most dominant paradigms of self-driving cars: modular pipeline-based approaches as well as deep-learning based end-to-end driving techniques.
Camera, lidar and radar-based perception
Localization, navigation, path planning
Vehicle modeling/control
Deep Learning
Imitation learning
Reinforcement learning
Designing autonomous decision making systems is one of the longstanding goals of Artificial Intelligence. Such decision making systems, if realized, can have a big impact in machine learning for robotics, game playing, control, health care to name a few. This course introduces Reinforcement Learning as a general framework to design such autonomous decision making systems.
Introduction to RL
Multi-armed bandits
Policy Gradient Methods
Contextual Bandits
Finite Markov Decision Process
Dynamic Programming
Policy Iteration, Value Iteration
Monte Carlo Methods
...
🔗 Link to Course 🔗 Link to Materials
A mini 6-lecture series by Pieter Abbeel.
MDPs, Exact Solution Methods, Max-ent RL
Deep Q-Learning
Policy Gradients and Advantage Estimation
TRPO and PPO
DDPG and SAC
Model-based RL
Covers topics from basic concepts of Reinforcement Learning to more advanced ones:
Markov decision processes & planning
Model-free policy evaluation
Model-free control
Reinforcement learning with function approximation & Deep RL
Policy Search
Exploration
...
🔗 Link to Course 🔗 Link to Materials
This is a graduate-level course covering different aspects of deep multi-task and meta learning.
Multi-task learning, transfer learning basics
Meta-learning algorithms
Advanced meta-learning topics
Multi-task RL, goal-conditioned RL
Meta-reinforcement learning
Hierarchical RL
Lifelong learning
Open problems
🔗 Link to Course 🔗 Link to Materials
A course introducing foundations of ML for applications in genomics and the life sciences more broadly.
Interpreting ML Models
DNA Accessibility, Promoters and Enhancers
Chromatin and gene regulation
Gene Expression, Splicing
RNA-seq, Splicing
Single cell RNA-sequencing
Dimensionality Reduction, Genetics, and Variation
Drug Discovery
Protein Structure Prediction
Protein Folding
Imaging and Cancer
Neuroscience
This is course is from Peter Abbeel and covers a review on reinforcement learning and continues to applications in robotics.
MDPs: Exact Methods
Discretization of Continuous State Space MDPs
Function Approximation / Feature-based Representations
LQR, iterative LQR / Differential Dynamic Programming
...