AI-ML Deep Dives
Every topic rebuilt 3Blue1Brown-style: foundations from zero, the derivation in pictures, worked examples for every scenario, graded exercises, and concept-trap question banks.
361 topics · 1780 pages — more land as generation runs
1.1
Linear Algebra Essentials
1 deep-dive topics1.2
Calculus & Optimization Basics
14 deep-dive topics1.3
Probability & Statistics
21 deep-dive topics1.4
Python & Scientific Computing
12 deep-dive topics2.1
Data Preprocessing & Feature Engineering
15 deep-dive topics2.2
Linear & Logistic Regression
16 deep-dive topics2.3
Tree-Based & Instance Methods
10 deep-dive topics2.5
Unsupervised Learning
11 deep-dive topics2.6
Model Evaluation & Selection
16 deep-dive topics3.1
Neural Network Fundamentals
13 deep-dive topics3.2
Training Deep Networks
15 deep-dive topics3.2.4AdaGrad and RMSprop
3.2.7Learning rate warmup
3.2.8Batch normalization
3.2.9Layer normalization
3.2.10Dropout regularization
3.2.11Early stopping
3.2.14Gradient clipping
3.3
Deep Learning Frameworks
11 deep-dive topics3.4
Convolutional Neural Networks
15 deep-dive topics3.5
Sequence Models
14 deep-dive topics4.1
Transformer Architecture
14 deep-dive topics4.2
Tokenization & Language Modeling
10 deep-dive topics4.3
Pretraining & Fine-Tuning LLMs
14 deep-dive topics4.4
Alignment, Prompting & RAG
16 deep-dive topics4.5
Generative Models
17 deep-dive topics5.1
Reinforcement Learning Foundations
13 deep-dive topics5.2
Deep & Advanced RL
14 deep-dive topics5.2.2Experience replay
5.2.3Target networks
5.2.6REINFORCE algorithm
5.2.7Actor-critic methods
5.2.11Soft Actor-Critic (SAC)
5.3
MLOps & Deployment
18 deep-dive topics5.3.1ML project lifecycle
5.3.5Feature stores
5.3.11CI - CD pipelines for ML
5.3.14A - B testing for models
5.3.17Edge deployment and ONNX
6.1
Scaling & Efficient Architectures
13 deep-dive topics6.2
AI Agents & Tool Use
10 deep-dive topics6.3
Interpretability & Explainability
11 deep-dive topics6.4
AI Safety & Alignment
15 deep-dive topics6.5