2026.02.17
Chapter 1: What are Vector Embeddings? The Language of AI
Unlock the mystery of vector embeddings! Learn what they are, why they're vital for modern AI, and how they transform data into a language machines …
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2026.03.20
Making Every Token Count: Context Reduction & Summarization
Learn how to optimize LLM context by mastering reduction and summarization techniques, enhancing performance and reliability in production AI systems.
ACCESS_FILE >>2025.10.26
TensorFlow Guide: Guided Project 2 - Text Generation with LSTMs
Build a character-level text generation model using TensorFlow and LSTM to generate creative text based on Shakespeare's works.
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Context Engineering for LLMs Guide
Master context engineering for LLMs. Learn reduction, compression, chunking, prioritization, and multi-source pipelines to optimize AI output quality …
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Context Engineering for LLMs: A Practical Guide
Learn to design, structure, and optimize context for Large Language Models (LLMs) to improve performance, reliability, and output quality in …
ACCESS_FILE >>2026.01.05
LangExtract Practical Field Guide
Learn how to use LangExtract, a Python library for extracting structured data from text using LLMs.
ACCESS_FILE >>2025.08.22
NLP Fundamentals: Mastering Attention and Transformers for Large Language Models
A comprehensive guide to Natural Language Processing fundamentals, including text preprocessing, word embeddings, and an in-depth exploration of …
ACCESS_FILE >>2025.08.22
Retrieval-Augmented Generation (RAG): Enhancing LLMs with External Knowledge - A Practical Guide
A comprehensive and practical guide to Retrieval-Augmented Generation (RAG), covering its core components, document loading, chunking, embedding, …
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