Resources

This page is a collection of resources for learning machine learning, deep learning, and artificial intelligence. It includes online courses, books, blogs, and YouTube channels. The resources are categorized into different sections based on the topics. The resources are collected from various sources and are free to access. The page is updated regularly with new resources.

Mathematics Basic

THis is is some content about mathematics

MIT 18.01: Single Variable Calculus
⭐️⭐️⭐️⭐️⭐️
MIT’s 18.01 Single Variable Calculus is a foundational mathematics course covering differential and integral calculus. It focuses on the fundamental concepts of…

MIT 18.02: Multivariable Calculus
⭐️⭐️⭐️⭐️⭐️
MIT’s 18.02 Multivariable Calculus extends the principles of single-variable calculus to functions of multiple variables. It introduces partial derivatives, *multiple…

MIT 18.06: Linear Algebra
⭐️⭐️⭐️⭐️⭐️
MIT’s 18.06 Linear Algebra is a foundational course that explores the fundamental concepts of vectors, matrices, determinants, eigenvalues, and linear transformations.…

MIT 18.065: Matrix Methods in Data Analysis, Signal Processing, and Machine Learning
⭐️⭐️⭐️⭐️
MIT’s 18.065 Matrix Methods in Data Analysis, Signal Processing, and Machine Learning is an applied mathematics course that explores the **use of matrix algebra in data…
MIT

MIT 18.S096: Matrix Calculus For Machine Learning And Beyond
⭐️⭐️⭐️
MIT’s 18.S096 Matrix Calculus for Machine Learning and Beyond is an advanced mathematics course designed to provide a deep understanding of matrix calculus and its…

Stanford CS109: Probability for Computer Scientists
NN
Basic
In this blog, I am going to summary the 5 most common generative models: Auto-Regreesive Model, Variational AutoEncoder, Engery Based Model, Flow Model and Diffusion Model.…
Stanford

CMU 10-708: Probabilistic Graphical Models
NN
Basic
In this blog, I am going to summary the 5 most common generative models: Auto-Regreesive Model, Variational AutoEncoder, Engery Based Model, Flow Model and Diffusion Model.…
Stanford

CMU 10-725: Convex Optimization
NN
Basic
In this blog, I am going to summary the 5 most common generative models: Auto-Regreesive Model, Variational AutoEncoder, Engery Based Model, Flow Model and Diffusion Model.…
Stanford

MIT RES.18-009: Learn Differential Equations: Up Close With Gilbert Strang And Cleve Moler
NN
Basic
In this blog, I am going to summary the 5 most common generative models: Auto-Regreesive Model, Variational AutoEncoder, Engery Based Model, Flow Model and Diffusion Model.…
Stanford
No matching items

Computer Basics & Algorithms

Introduction to AI

Machine Learning & Deep Learning Basic

Coursera: Machine Learning Specialization
NN
Basic
In this blog, I am going to summary the 5 most common generative models: Auto-Regreesive Model, Variational AutoEncoder, Engery Based Model, Flow Model and Diffusion Model.…
Stanford

Coursera: Deep Learning Specialization
NN
Basic
In this blog, I am going to summary the 5 most common generative models: Auto-Regreesive Model, Variational AutoEncoder, Engery Based Model, Flow Model and Diffusion Model.…
Stanford

CS4780: Machine Learning for Intelligent Systems
NN
Basic
In this blog, I am going to summary the 5 most common generative models: Auto-Regreesive Model, Variational AutoEncoder, Engery Based Model, Flow Model and Diffusion Model.…
Stanford

Stanford CS229: Machine Learning
NN
Basic
In this blog, I am going to summary the 5 most common generative models: Auto-Regreesive Model, Variational AutoEncoder, Engery Based Model, Flow Model and Diffusion Model.…
Stanford

UCB CS182/282A: Designing, Visualizing and Understanding Deep Neural Networks
NN
Basic
In this blog, I am going to summary the 5 most common generative models: Auto-Regreesive Model, Variational AutoEncoder, Engery Based Model, Flow Model and Diffusion Model.…
Stanford

CMU 11-785: Introduction to Deep Learning
NN
Basic
In this blog, I am going to summary the 5 most common generative models: Auto-Regreesive Model, Variational AutoEncoder, Engery Based Model, Flow Model and Diffusion Model.…
CMU

MIT 6.5940: TinyML and Efficient Deep Learning Computing
NN
Basic
In this blog, I am going to summary the 5 most common generative models: Auto-Regreesive Model, Variational AutoEncoder, Engery Based Model, Flow Model and Diffusion Model.…
MIT
No matching items

Applications

Computer Vision

Natural Language Processing

Reinforcement Learning & Robotics

Generative Models

Others

ML/DL System & Hardware

Other Resources

Youtuber

Blogs

Books