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

Reinforcement Learning
Representation and Policy Learning for Robotic Control

TDMPC

PreviousRepresentation and Policy Learning for Robotic Control NextTransformers

Last updated 1 month ago

CtrlK