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The AI engineering library
Handwritten notes across 19 categories. Preview the first five pages of any note free.
17 note(s) found
Model Context Protocol (MCP)
Model Context Protocol (MCP) – Complete AI Engineering Notes
Complete 18-page handwritten MCP notes covering architecture, Python SDK, server development, AI agent integration, security, and production deployment. Includes practical code examples, diagrams, implementation workflows, and advanced interview preparation for AI Engineers.
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Python for AI Engineering
Python for AI Engineers – Advanced Practical Course
Learn practical Python engineering for building real-world AI applications, covering PyTorch, LLM APIs, Embeddings, Vector Databases, RAG and AI Agents. Master FastAPI, Async Python, AI Testing, Performance Optimization, Security and Deployment through concise handwritten revision notes.
16 orders placed
Mathematics for AI
Mathematics for Machine Learning – Handwriting Complete Notes
Master the mathematics behind Machine Learning, Deep Learning and AI with easy-to-understand handwritten revision notes covering Linear Algebra, Calculus, Probability, Statistics and Optimization. Includes essential formulas, practical AI applications, visual explanations and a complete revision cheat sheet from Beginner to Advanced level.
11 orders placed
GenAI Interview Preparation
Applied AI Engineering – Complete Interview Preparation & Revision Handbook
Prepare for Applied AI Engineer interviews with structured handwritten notes covering Python, ML, Deep Learning, LLMs, RAG, AI Agents, Production AI and System Design. Includes technical interview questions, coding concepts, real-world engineering scenarios and rapid revision.
10 orders placed
LangChain and LangGraph
LangGraph – Stateful Workflows & AI Agents (Handwritten Notes)
Learn to build stateful AI workflows and intelligent agents using LangGraph. Explore graph architecture, state management, conditional routing, agent loops, multi-agent systems, memory and production workflows. Ideal for AI Engineers, LangChain Developers and LLM Application Developers.
10 orders placed
Production AI
Production AI – Evaluation, Security & Deployment (Advanced Handwritten Notes)
Learn how to build, evaluate, secure and deploy reliable AI systems in production. Covers LLM evaluation, AI security, observability, deployment architecture, MLOps, scaling and production reliability. Designed for AI Engineers, LLM Developers and Production AI learners.
10 orders placed
Natural Language Processing
Natural Language Processing (NLP) – Complete Course Handwritten Notes
Master Natural Language Processing with structured handwritten notes covering text processing, linguistics, machine learning, Transformers, LLMs and practical NLP applications. Ideal for AI/ML students, NLP developers and aspiring AI Engineers.
10 orders placed
Embeddings, Vector Search & Vector Databases
Embeddings, Vector Search & Vector Databases – Handwritten Notes
Learn embeddings, vector search and vector databases through clear handwritten notes. Understand similarity metrics, HNSW, IVF, hybrid search, reranking and production retrieval workflows. Ideal for AI Engineers, RAG Developers and LLM learners.
10 orders placed
AI Agents and Agentic AI
AI Agents, Tools & Agentic Design Patterns – Advanced Handwritten Notes
Master AI Agents, Tool Calling and Agentic Design Patterns through structured handwritten notes. Learn ReAct, planning, multi-agent systems, MCP, memory, guardrails and production reliability. Designed for AI Engineers, LLM Developers and Generative AI learners.
10 orders placed
Machine Learning Fundamentals
AI/ML Fundamentals – Complete Handwritten Revision Notes
Build a strong foundation in Artificial Intelligence and Machine Learning with clear, structured handwritten notes. Learn core concepts, ML algorithms, model evaluation, Deep Learning, LLMs and practical AI Engineering workflows.
10 orders placed
LangChain and LangGraph
LangChain – Building LLM Applications
Explore Transformer Architecture through clear and well-structured handwritten notes. Understand self-attention, multi-head attention, transformer blocks and tensor shapes with step-by-step explanations and visual diagrams. Ideal for Deep Learning students, LLM developers and aspiring AI Engineers.
10 orders placed
Neural Networks
Neural Networks – Complete Handwritten Notes & AI/ML Revision Guide
Master Neural Networks and Deep Learning fundamentals with 19 pages of structured handwritten revision notes. Topics Covered: • Neural Network Architecture & Forward Propagation • Activation Functions & Loss Functions • Backpropagation & Gradient Descent • Optimizers & Weight Initialization • Batch Normalization & Regularization • Overfitting & Training Diagnostics • Neural Network Architectures (CNN, RNN, LSTM, Transformer) • PyTorch Implementation • Model Evaluation & Error Analysis • Scaling, Efficiency & Deployment • Advanced Interview Questions & Revision Includes: Visual diagrams, important formulas, practical examples, PyTorch code, interview questions and revision cheat sheets. Ideal For: AI/ML Students, Deep Learning Learners, Python Developers and Aspiring AI Engineers. Format: PDF | Pages: 19 | Language: English
10 orders placed
Transformers and Attention
Transformers & Attention – Advanced Handwritten Notes
Master Transformer Architecture and Attention Mechanisms with 16 pages of handwritten advanced revision notes. Topics Covered: • Transformer Architecture & Mental Model • Tokens, Embeddings & Positional Encoding • Query, Key & Value (QKV) • Scaled Dot-Product Attention • KV Cache & Inference Optimization • Multi-Head Attention (MHA) • Transformer Blocks, Residuals & FFN • Encoder, Decoder & Model Families • Attention Masks & Causal Attention • Training vs Generation • Advanced Attention Variants (MHA, MQA, GQA) • Complexity, Performance & FlashAttention • Technical Interview Questions Includes: Visual diagrams, key formulas, practical examples, interview preparation and rapid revision cheat sheets. Format: PDF | 16 Pages | English
10 orders placed
RAG and Vector Databases
RAG Engineering – Advanced Handwritten Notes
Master Advanced Retrieval-Augmented Generation (RAG) concepts with 16 pages of structured handwritten AI Engineering notes. Topics Covered: • Advanced RAG Architecture & Mental Models • Document Ingestion, Chunking & Indexing • Embeddings, Vector Search & HNSW • Hybrid Retrieval & Reranking • Query Transformation & Routing • Context Assembly & Prompt Grounding • Generation Quality & Hallucination Control • Multi-Hop, Graph, Agentic & Iterative RAG • RAG Evaluation & Debugging • Performance Engineering & Optimization • Security, Governance & Production RAG Includes: Architecture diagrams, workflows, evaluation metrics, practical engineering examples, interview questions and rapid revision sheets. Ideal For: AI Engineers, LLM Developers, Python Developers and learners with basic RAG knowledge. Format: PDF | Pages: 16 | Language: English
11 orders placed
RAG and Vector Databases
RAG Engineering – Basic Handwritten Notes
Learn Retrieval-Augmented Generation (RAG) from scratch with 12 pages of structured handwritten notes. Topics Covered: • RAG Fundamentals & Architecture • Document Loading & Chunking • Embeddings & Vector Storage • Retrieval & Ranking • Prompt Construction & Generation • Citations, Hallucinations & Abstention • Basic RAG Implementation • RAG Evaluation & Debugging • RAG Security & Production Includes: Visual diagrams, workflows, practical AI engineering examples and a rapid revision cheat sheet. Ideal For: Beginners, Python developers, AI/ML students and aspiring AI Engineers. Format: PDF | Pages: 12 | Language: English
10 orders placed
Python for AI Engineering
Python for AI Engineers – Complete Handwritten Notes & Quick Revision Guide
Master Python programming for AI Engineering with these 17 pages of handwritten revision notes. Topics Covered: • Python Fundamentals & Data Structures • Functions, OOP & Exception Handling • Iterators, Generators & Decorators • NumPy & Pandas • APIs, Async Python & AI Integration • Testing, Debugging & Best Practices Includes: Practical code examples, visual diagrams, quick revision sheet and final learning checklist. Perfect for: Python learners, AI/ML students and aspiring AI Engineers. Format: PDF | Pages: 17 | Language: English
10 orders placed
Large Language Models
LLM, Prompt Engineering & APIs
Master LLM, Prompt Engineering & APIs, and API integration with 14 pages of structured handwritten revision notes. Topics Covered: • LLM Inference & Tokenization • Prompt Engineering Framework • Zero-shot, Few-shot & Advanced Prompting • System Instructions & Context Management • Temperature, Top-P & Generation Parameters • LLM API Architecture & Python Integration • Structured Outputs & JSON • Tool Calling & Function Integration • API Reliability, Retries & Error Handling • Security & Prompt Injection • LLM Evaluation, Cost & Optimization Includes: Visual diagrams, practical Python examples, API workflows, important concepts and rapid revision cheat sheets. Ideal For: AI Engineers, Python Developers, LLM Developers and Generative AI Learners. Format: PDF | 14 Pages | English
11 orders placed
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