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ML & DL

Overview

Active Learning

总览

Core Set Selection

Diversity Based Sampling

Expected Error Reduction

Expected Model Change

Query By Committee

Uncertainty Sampling

Imitation Learning

总览

Behavior Cloning

Dagger

Generative Adversarial Imitation Learning

Inverse Reinforcement Learning

Maximum Entropy Irl

Reinforcement Learning

总览

Bandit

总览

Epsilon Greedy

Multi Armed Bandit

Thompson Sampling

Upper Confidence Bound

Deep Reinforcement Learning

总览

Deep Q Network

Muzero

Proximal Policy Optimization

Soft Actor Critic

Dynamic Programming

总览

Policy Evaluation

Policy Iteration

Value Iteration

Key Concepts

总览

Action

Environment Model

Policy

Reward

State

Value Function

Monte Carlo Rl

总览

Monte Carlo Control

Monte Carlo Prediction

Temporal Difference Learning

总览

Q Learning

Sarsa

Td

Self Supervised Learning

总览

Autoregressive Prediction

总览

Gpt Style Next Token Prediction

N Gram Language Model

Rnn Language Model

Transformer Language Model

Contrastive Learning

总览

Barlow Twins

Byol

Clip Style Image Text Contrastive Learning

Moco

Simclrbi

Swav

Word2vec

Denoising

总览

Bart Style Denoising

Denoising Autoencoder

Diffusion Denoising Objective

Masked Prediction

总览

Bert Style Pretraining

Masked Autoencoder

Masked Image Modeling

Masked Language Modeling

Predictive Representation Learning

总览

Bootstrap Representation Learning

Contrastive Predictive Coding

Semi Supervised Learning

总览

Consistency Regularization

Fixmatch

Pseudo Labeling

Supervised Learning

总览

Classification Models

总览

Adaboost

Cnn Classifier

Decision Tree

K Nearest Neighbors

Logistic Regression

Multilayer Perceptron

Naive Bayes

Random Forest

Rnn Classifier

Softmax Regression

Support Vector Machine

Transformer Classifier

Regression Models

总览

Elastic Net

Gaussian Process Regression

Gradient Boosting Regression

Lasso Regression

Linear Regression

Neural Network Regression

Random Forest Regression

Ridge Regression

Support Vector Regression

Unsupervised Learning

总览

Clustering Methods

总览

Gaussian Mixture Model

K Means

Spectral Clustering

Density Estimation

总览

Gaussian Mixture Model

Kernel Density Estimation

Dimensionality Reduction

总览

Principal Component Analysis

Anomaly Detection

ML & DL Resources

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Contrastive Learning

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This section is synchronized with the Chinese version and will continue to be expanded in English.

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barlow twins
byol
clip style image text contrastive learning
moco
simclrbi
swav
word2vec

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