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The library

17 technical volumes,grouped for the long read.

452 chapters · 2463 lessons across AI, mathematics, computer science, physics, and security. Every volume free to read. Jump straight to the subject area you came for.

AI & Machine Learning10Mathematics3Computer Science2Physics & Materials Science1Cybersecurity1
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Featured volumes.

17 books in the library
QKVAOTRANSFORMERSFROM SCRATCH
№ 01
Featured
AI & Machine LearningAdvanced

Transformers from Scratch

Master the Transformer architecture from scratch. Learn attention mechanisms, positional encoding, and build a complete German-to-English translation model.

18 chapters · 75 lessons
Chapter preview
  • 01Prerequisites
  • 02Introduction to Transformers
  • 03Attention Mechanism From Scratch
Open volume
t=4t=3t=2t=1t=0x_t = √ᾱ_t x_0 + √(1-ᾱ_t) εDIFFUSIONMODELS
№ 02
Featured
AI & Machine LearningAdvanced

Denoising Diffusion Models from Scratch

Master diffusion models from mathematical foundations to image generation. Learn the theory, implement DDPM in PyTorch, and understand modern systems like Stable Diffusion.

18 chapters · 76 lessons
Chapter preview
  • 01Prerequisites
  • 02Introduction to Generative Models
  • 03The Forward Diffusion Process
Open volume
INPUTHIDDENOUTPUTDEEP LEARNINGNEURAL NETWORKS
№ 03
Featured
AI & Machine LearningIntermediate

Deep Learning from Scratch with PyTorch

Master deep learning from mathematical foundations to production-ready models. Cover CNNs, RNNs, Transformers, GNNs, GANs, VAEs, Reinforcement Learning, and more with PyTorch implementations.

37 chapters · 179 lessons
Chapter preview
  • 01Development Environment
  • 02Linear Algebra for Deep Learning
  • 03Calculus for Deep Learning
Open volume
σ(z)x₁w₁x₂w₂x₃w₃x₄w₄Σwx+bŷactivateFORWARDBACKWARDNEURAL NETWORKSHOW THEY WORK
№ 04
Featured
AI & Machine LearningBeginner

How Neural Networks Work

Understand neural networks from the ground up with Python and PyTorch. From single neurons to CNNs and RNNs, build intuition through code and visualizations.

20 chapters · 65 lessons
Chapter preview
  • 01What Are Neural Networks?
  • 02Python and PyTorch Essentials
  • 03Mathematics for Neural Networks
Open volume
🧠ReadWriteBashPerceive → Reason → ActAGENTIC AISYSTEMSClaude Code • Codex • Gemini
№ 05
Featured
AI & Machine LearningAdvanced

Building Agentic AI Systems

Master the art of building production-ready AI agents. Learn from Claude Code, Gemini, and Codex architectures. Build multi-agent systems with tool use, memory, and safety guardrails.

29 chapters · 175 lessons
Chapter preview
  • 01Prerequisites and Setup
  • 02The Agentic AI Revolution
  • 03Agent Architecture Fundamentals
Open volume
Failure ZoneHealthyWarningCriticalRemaining Useful LifeRUL: 42 cyclesCNNBiLSTMAttentionDEEP LEARNING FORPREDICTIVE MAINTENANCEAMNL: State-of-the-Art on NASA C-MAPSS
№ 06
Featured
AI & Machine LearningAdvanced

Gradient-Aware Multi-Task Learning for Predictive Maintenance: AMNL, GABA, and GRACE

A research-grade walkthrough of three multi-task learning strategies for Remaining Useful Life prediction — AMNL (accuracy-first), GABA (safety-first), and GRACE (balanced) — all built on a shared CNN-BiLSTM-Attention backbone. Validated across 335 experiments on NASA C-MAPSS and N-CMAPSS DS02, beating the published SOTA (DKAMFormer) on multi-condition data.

29 chapters · 121 lessons
Chapter preview
  • 01Predictive Maintenance & RUL
  • 02Benchmarks: C-MAPSS & N-CMAPSS
  • 03Mathematical Preliminaries
Open volume
AI & Machine Learning

10 volumes in this track.

QKVAOTRANSFORMERSFROM SCRATCH
№ 01
Featured
AI & Machine LearningAdvanced

Transformers from Scratch

Master the Transformer architecture from scratch. Learn attention mechanisms, positional encoding, and build a complete German-to-English translation model.

18 chapters · 75 lessons
Chapter preview
  • 01Prerequisites
  • 02Introduction to Transformers
  • 03Attention Mechanism From Scratch
Open volume
t=4t=3t=2t=1t=0x_t = √ᾱ_t x_0 + √(1-ᾱ_t) εDIFFUSIONMODELS
№ 02
Featured
AI & Machine LearningAdvanced

Denoising Diffusion Models from Scratch

Master diffusion models from mathematical foundations to image generation. Learn the theory, implement DDPM in PyTorch, and understand modern systems like Stable Diffusion.

18 chapters · 76 lessons
Chapter preview
  • 01Prerequisites
  • 02Introduction to Generative Models
  • 03The Forward Diffusion Process
Open volume
INPUTHIDDENOUTPUTDEEP LEARNINGNEURAL NETWORKS
№ 03
Featured
AI & Machine LearningIntermediate

Deep Learning from Scratch with PyTorch

Master deep learning from mathematical foundations to production-ready models. Cover CNNs, RNNs, Transformers, GNNs, GANs, VAEs, Reinforcement Learning, and more with PyTorch implementations.

37 chapters · 179 lessons
Chapter preview
  • 01Development Environment
  • 02Linear Algebra for Deep Learning
  • 03Calculus for Deep Learning
Open volume
σ(z)x₁w₁x₂w₂x₃w₃x₄w₄Σwx+bŷactivateFORWARDBACKWARDNEURAL NETWORKSHOW THEY WORK
№ 04
Featured
AI & Machine LearningBeginner

How Neural Networks Work

Understand neural networks from the ground up with Python and PyTorch. From single neurons to CNNs and RNNs, build intuition through code and visualizations.

20 chapters · 65 lessons
Chapter preview
  • 01What Are Neural Networks?
  • 02Python and PyTorch Essentials
  • 03Mathematics for Neural Networks
Open volume
🧠ReadWriteBashPerceive → Reason → ActAGENTIC AISYSTEMSClaude Code • Codex • Gemini
№ 05
Featured
AI & Machine LearningAdvanced

Building Agentic AI Systems

Master the art of building production-ready AI agents. Learn from Claude Code, Gemini, and Codex architectures. Build multi-agent systems with tool use, memory, and safety guardrails.

29 chapters · 175 lessons
Chapter preview
  • 01Prerequisites and Setup
  • 02The Agentic AI Revolution
  • 03Agent Architecture Fundamentals
Open volume
Failure ZoneHealthyWarningCriticalRemaining Useful LifeRUL: 42 cyclesCNNBiLSTMAttentionDEEP LEARNING FORPREDICTIVE MAINTENANCEAMNL: State-of-the-Art on NASA C-MAPSS
№ 06
Featured
AI & Machine LearningAdvanced

Gradient-Aware Multi-Task Learning for Predictive Maintenance: AMNL, GABA, and GRACE

A research-grade walkthrough of three multi-task learning strategies for Remaining Useful Life prediction — AMNL (accuracy-first), GABA (safety-first), and GRACE (balanced) — all built on a shared CNN-BiLSTM-Attention backbone. Validated across 335 experiments on NASA C-MAPSS and N-CMAPSS DS02, beating the published SOTA (DKAMFormer) on multi-condition data.

29 chapters · 121 lessons
Chapter preview
  • 01Predictive Maintenance & RUL
  • 02Benchmarks: C-MAPSS & N-CMAPSS
  • 03Mathematical Preliminaries
Open volume
ThecatsatonthematQKVhQ'K'V'h'softmax(QKᵀ/√d_k)VATTENTIONMECHANISMS
№ 07
Featured
AI & Machine LearningIntermediate

The Attention Atlas: Mechanisms That Power Modern AI

A visual, mathematical, and practical guide to all 15 attention mechanisms using the same example sentence throughout.

17 chapters · 17 lessons
Chapter preview
  • 01The Origin of Attention
  • 02Scaled Dot-Product Attention
  • 03Multi-Head Attention
Open volume
GPU 0GPU 1GPU 2GPU 3GPU 4GPU 5GPU 6GPU 7671B PARAMETERSFORGINGGIANTS
№ 08
Featured
AI & Machine LearningAdvanced

Forging Giants: Training Massive Models from Scratch

Master the hidden math, intuition, and engineering behind 671B-parameter models. From mathematical foundations through DeepSeek V3 architecture, distributed training, RLHF, and production deployment.

20 chapters · 117 lessons
Chapter preview
  • 01Mathematical Bedrock
  • 02The Transformer, Derived from First Principles
  • 03Tokenisation and Vocabularies
Open volume
AGENTENVas, rQ(s,a) ← r + γ max Q(s',·)REINFORCEMENTLEARNINGFROM SCRATCH WITH PYTORCHVOLUME I · FOUNDATIONS
№ 09
Featured
AI & Machine LearningAdvanced

Reinforcement Learning from Scratch with PyTorch

Master reinforcement learning from multi-armed bandits to DreamerV3 and MuZero. Derive every algorithm, then implement it in PyTorch: tabular methods, DQN/Rainbow, PPO, SAC, TD3, model-based RL, MCTS, AlphaZero. The classical-through-frontier foundation (Volume 1 of 2).

23 chapters · 121 lessons
Chapter preview
  • 01Development Environment
  • 02What Is Reinforcement Learning?
  • 03Multi-Armed Bandits
Open volume
π₂π₃humany_w ≻ y_lDPO · GRPO · RLHFADVANCEDRLALIGNMENT · MULTI-AGENT · FRONTIERVOLUME II
№ 10
Featured
AI & Machine LearningAdvanced

Advanced Reinforcement Learning: Alignment, Multi-Agent, and Frontier

The frontier half of the RL curriculum: imitation and offline RL (CQL, IQL, Decision Transformer), hierarchical and meta-RL, multi-agent (MADDPG, QMIX, MAPPO, PSRO), RL for language models (RLHF, DPO, GRPO, DAPO, DeepSeek-R1), distributed engineering, and six capstone projects. Volume 2 of 2 — assumes the foundations covered in Volume 1.

19 chapters · 92 lessons
Chapter preview
  • 01Imitation and Inverse RL
  • 02Offline Reinforcement Learning
  • 03Sequence-Modeling Reinforcement Learning
Open volume
Mathematics

3 volumes in this track.

μ-σ+σPROBABILITY& STATISTICS
№ 01
MathematicsIntermediate

Probability & Statistics for AI/ML

Master probability distributions and mathematical statistics from foundations to advanced inference. Learn Bayesian methods, information theory, and statistical learning for data science and machine learning.

31 chapters · 175 lessons
Chapter preview
  • 01Prerequisites & Mathematical Foundations
  • 02Probability Fundamentals
  • 03Random Variables
Open volume
ab∫f(x) dxdy/dxCALCULUSDERIVATIVES & INTEGRALS
№ 02
MathematicsBeginner

Interactive Calculus

Master calculus through interactive visualizations and simulations. Cover differential and integral calculus, multivariable calculus, differential equations, and PDEs with real-world applications.

46 chapters · 353 lessons
Chapter preview
  • 01Mathematical Functions - The Building Blocks
  • 02Limits - Approaching the Infinite
  • 03Continuity - No Breaks, No Jumps
Open volume
xyzv⃗a₁₁ a₁₂ a₁₃a₂₁ a₂₂ a₂₃a₃₁ a₃₂ a₃₃LINEAR ALGEBRAVECTORS & MATRICES
№ 03
MathematicsIntermediate

Linear Algebra for the AI Age

A visual journey through linear algebra with interactive visualizations. Master vectors, matrices, eigenvalues, SVD, and the mathematics behind AI, machine learning, and computer vision.

25 chapters · 134 lessons
Chapter preview
  • 01The Geometric Universe
  • 02Vectors - The Building Blocks
  • 03Linear Transformations - The Big Idea
Open volume
Computer Science

2 volumes in this track.

422163{}DATA STRUCTURES& ALGORITHMS
№ 01
Computer ScienceIntermediate

Data Structures & Algorithms

Master data structures and algorithms from fundamentals to advanced topics. Interactive visualizations, multi-language implementations (C, Python, C++, TypeScript), and real-world applications with interview preparation.

42 chapters · 261 lessons
Chapter preview
  • 01Introduction to Algorithmic Thinking
  • 02Complexity Analysis
  • 03Mathematical Foundations
Open volume
FREQUENCY (Hz)DIGITAL SIGNALPROCESSING
№ 02
Computer ScienceAdvanced

Digital Signal Processing

Master digital signal processing from fundamentals to advanced applications. Interactive visualizations, Python implementations with NumPy/SciPy, and real-world projects in audio, image, communications, and biomedical signal processing.

48 chapters · 334 lessons
Chapter preview
  • 01Introduction to Digital Signal Processing
  • 02Mathematical Foundations
  • 03Continuous-Time Signals and Systems
Open volume
Physics & Materials Science

1 volume in this track.

abcF-43mT_dDFTMn:CdSe Quantum DotsCRYSTALLOGRAPHY &ELECTRONIC STRUCTUREFirst-Principles with VASP
№ 01
Featured
Physics & Materials ScienceAdvanced

Crystallography, Electronic Structure & VASP

Master crystal symmetry, reciprocal space, and electronic structure theory. Build toward Mn-doped CdSe quantum dot simulations using VASP from first principles.

7 chapters · 70 lessons
Chapter preview
  • 01The Architecture of Crystals — Real Space
  • 02Mathematics of Symmetry — Group Theory Essentials
  • 03Reciprocal Space and Diffraction
Open volume
Cybersecurity

1 volume in this track.

VOL. 15 · BOO-AI PRESSISBN 978-1-AI-CYBRAI & CYBERSECURITYTHE INTELLIGENT DEFENDER'S HANDBOOK
№ 01
Featured
CybersecurityIntermediate

AI & Cybersecurity: The Intelligent Defender’s Handbook

From foundations to frontlines — building the next generation of AI-powered security engineers. Master offensive and defensive AI, adversarial ML, LLM security, and post-quantum cryptography with real case studies and hands-on labs.

23 chapters · 98 lessons
Chapter preview
  • 01The Cyber Battlefield in the Age of AI
  • 02Cybersecurity Fundamentals for the AI Engineer
  • 03AI & Machine Learning Fundamentals for Security
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