Machine Learning Visual Guide

A comprehensive, interactive roadmap to mastering ML concepts

Welcome to ML Visual Guide

This interactive guide helps you navigate the complex landscape of machine learning concepts, algorithms, and applications. Click through the tabs above to explore different aspects of ML.

Core Paradigms

Understand the fundamental learning approaches that form the basis of ML systems.

Supervised Unsupervised Reinforcement

Method Families

Explore different algorithmic approaches to solving ML problems.

Tree-Based Neural Nets Bayesian

Deep Learning

Dive into advanced neural network architectures and techniques.

CNNs Transformers GANs

Applications

Discover real-world domains where ML is making an impact.

Computer Vision NLP Recommenders

Learning Path

Follow this recommended path to systematically learn machine learning:

1

Pick a cluster (e.g., Transformers)

Select a specific area to focus on based on your interests.

2

Absorb the theory

Study foundational concepts and read seminal papers in the area.

3

Code implementations

Follow with hands-on projects in PyTorch/TensorFlow.

4

Optimize and deploy

Tune your models, deploy them, and reflect on the process.

5

Repeat

Move to the next concept for comprehensive knowledge retention.

Key Research Papers

Attention Is All You Need

Transformer Architecture

arXiv:1706.03762

Deep Residual Learning

ResNet Architecture

arXiv:1512.03385

Generative Adversarial Nets

GANs

arXiv:1406.2661

BERT: Pre-training

Language Representation

arXiv:1810.04805

Core Learning Paradigms

Supervised Learning

Learning from labeled training data to make predictions.

Classification Regression

Unsupervised Learning

Finding hidden patterns in unlabeled data.

Clustering Dimensionality Reduction

Reinforcement Learning

Learning through trial and error using rewards.

Q-Learning Policy Gradients

Self-supervised Learning

Generating labels from the data itself.

Autoencoders Contrastive Learning

Core Tasks & Problems

Regression

Predicting continuous values

Linear Polynomial

Classification

Categorizing inputs into classes

Binary Multiclass

Clustering

Grouping similar data points

K-means DBSCAN

Dimensionality Reduction

Reducing number of variables

PCA t-SNE

Generative Modeling

Creating new data samples

GANs VAEs

Anomaly Detection

Identifying unusual patterns

Isolation Forest One-Class SVM

Method Families

Tree-Based Models

Decision Trees

Simple tree structure for classification/regression

Random Forests

Ensemble of decision trees

Gradient Boosting

Sequentially corrects errors (XGBoost, LightGBM)

Probabilistic & Bayesian

Naive Bayes

Simple probabilistic classifier

Gaussian Processes

Non-parametric Bayesian approach

Ensemble Methods

Bagging

Parallel training of diverse models

Boosting

Sequential training focusing on errors

Stacking

Meta-model learns to combine predictions

Reinforcement Learning

Q-Learning

Value-based approach

Policy Gradients

Direct policy optimization

PPO

Proximal Policy Optimization

Deep Learning Architectures

Foundational Architectures

Feedforward Networks

Basic neural networks with dense layers

MLP Perceptron
Nature Deep Learning Review

Convolutional Networks (CNNs)

For processing grid-like data (images)

LeNet ResNet

Recurrent Networks (RNNs)

For sequential data processing

LSTM GRU

Transformers & Attention

Transformer

Self-attention based architecture

Encoder Decoder Attention Positional Encoding
Original Transformer Paper GPT-3 Paper

Advanced Variants

Specialized transformer models

BERT GPT LoRA

Generative Models

Generative Adversarial Networks (GANs)

Generator vs discriminator framework

DCGAN StyleGAN

Variational Autoencoders (VAEs)

Probabilistic approach to generation

Encoder Latent Space

Transfer Learning

Feature Extraction

Using pretrained models as feature extractors

Fine-tuning

Adapting pretrained models to new tasks

Multi-task Learning

Learning multiple tasks simultaneously

ML Applications & Domains

Computer Vision

Object Detection

Identifying objects in images

YOLO Faster-RCNN

Image Segmentation

Pixel-level classification

U-Net Mask R-CNN

Image Generation

Creating new images

GANs Diffusion

Natural Language Processing

Text Classification

Categorizing text documents

Sentiment Topic

Sequence-to-Sequence

Text generation/translation

Machine Translation Summarization

Large Language Models

Advanced text understanding

GPT BERT

Other Applications

Time-Series Forecasting

Predicting future values

Recommender Systems

Personalized recommendations

Graph Machine Learning

Learning on graph structures

Learning Resources

Books

Pattern Recognition and ML

Christopher Bishop

Math & Theory
Book PDF

Deep Learning

Ian Goodfellow et al.

Comprehensive DL

Dive into Deep Learning

ZX Li et al.

Hands-on approach

Courses

Andrew Ng's ML

Coursera

Beginner-friendly intro

Stanford CS231n

Computer Vision

Advanced CV concepts

Stanford CS224N

NLP with DL

Cutting-edge NLP

Spinning Up in RL

OpenAI

Reinforcement Learning

Toolkits & Libraries

Classical ML

scikit-learn

Deep Learning

PyTorch, TensorFlow

NLP

Hugging Face

Research & Community

DL Papers Roadmap

floodsung GitHub

Subreddits

r/MachineLearning, r/learnmachinelearning

Structured Learning Path

1. Core Paradigms & Problems

A. Learning Paradigms

B. Core Tasks & Problems

2. Method Families

A. Instance-Based

D. Tree-Based Models

3. Deep Learning Foundations

A. Neural Network Basics

B. Advanced Architectures

4. Transformers & LLMs

A. Core Concepts

B. Popular Models

5. AI Agents & Autonomy

A. Frameworks

How to Use This Path

  1. Pick a topic from any section
  2. Watch the video introduction
  3. Read the linked articles
  4. Practice with provided code examples
  5. Move to next topic when comfortable