Artificial Intelligence
Intuitive Maths behind AI
Overview
Research Ideas and Philosophy
Basic Principles
Information Theory
- Entropy
- Log Probability
Probability & Statistics
Linear Algebra
Optimization
Statistical Learning Theory
- Expectation Maximization
Machine Learning
- Clustering
- Bias Variance Trade-off
Deep Learning
Bayesian Deep Learning
- Probabilistic View
- Uncertainty
- Variational Inference for Bayesian Neural Network
Reinforcement Learning
Transformers
LLMs
SSL, ViT, Latest vision learning spring
Diffusion Models
- ImageGen
- Denoising Autoencoders
Distributed Training
State Space Models
RLHF
Robotics
- Kalman Filter
- Unscented Kalman Filter
Game Theory and ML
Continual Learning
Computer Vision
- Hough Transform
- Projective Geometry
  - Extrinsic and Intrinsic Parameters
  - Image Rectification
- Tracking
- Optical Flow
- Harris Corner
- Others
Papers
Deep Learning Book
- Chapter 4: Optimization
- Chapter 5: Machine Learning Basics
- Chapter 6: Deep FeedForward Networks
Project Euler
Python
Computer Science
TensorFlow
Pytorch
- RNN/LSTM in Pytorch
- Dataset/ Data loader
- Resuming/Loading Saved model
Programming
- Unit Testing
- How to write code
General Software Engineering
- SSH tunneling and Ngrok
How To Do Research
Resources
ROS for python3
Kitti

Powered by GitBook

On this page

State Space Models

PreviousDistributed Training NextRLHF

Last updated 1 year ago

CtrlK

The Annotated S4