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English | 2026 | ASIN: B0GMRLLDTL | 478 pages | EPUB | 7.04 MB [/center]
Dive into the world of Language Models (SLMs/LLMs) with "The Complete Hands-On LLM Playbook", the ultimate guide crafted for learners eager to grasp AI fundamentals and supercharge their projects. Whether you're new to coding or tackling advanced coursework, this book demystifies complex concepts like transformers, attention mechanisms, and model scaling with implementations using python
Core Foundations
Start with crystal-clear breakdowns of Language models evolution-from early NLP to cutting-edge architectures like BERT, GPT, Llama, and T5. No prior experience? No problem. Visual diagrams, real-world analogies, and step-by-step Python setups (using Hugging Face, PyTorch) get you coding instantly. Explore tokenizers, embeddings, encoders, decoders, and self-attention through intuitive "hands-on" sections that build your intuition without overwhelming math.
Master transformer architectures, attention mechanisms, and scaling laws
Explore encoder-only, decoder-only, and encoder-decoder model families
Fine-tune models using SFT, RLHF, and parameter-efficient techniques such as LORA and adapters
Build LLMs from scratch, including pretraining workflows and architectural decision.
Evaluate models using both technical and business-aligned metrics.
Optimize and scale inference with quantization, KV-cache optimization, distillation, and complier-backed techniques
Project-Ready Skills for Standout Portfolios
Packed with end-to-end projects tailored for student assignments and capstone work
Build QA systems, sentiment analyzers, and chatbots using encoder-only models like BERT.
Create text generators and customer support bots with decoder architectures (GPT-style)
Fine-tune for summarization and translation via encoder-decoder powerhouses like T5 and BART.
From full fine-tuning and PEFT (LoRA, Adapters) to training your own mini-LLMs from scratch, every chapter delivers copy-paste code, evaluation metrics, and deployment tips for GitHub-ready demos.
From Theory to Production Mastery
Go beyond basics: Learn cost-smart decisions (self-hosting vs. APIs), quantization for efficient inference, KV-cache optimizations, and scaling for real apps. Evaluate models with business and technical metrics, and efficient inferencing
Why Students Choose This Playbook
No-Code-to-Pro Path: Enterprise insights from authors Sai Dheeraj Gummadi and Niladri Sen (ML experts with patents and Fortune 500 experience) make you interview-ready.
Interactive & Practical: 200+ pages of code, checklists, decision matrices, and visuals-no fluff, all impact.
Future-Proof: Covers 2026 trends like hybrid strategies, ethical AI, and open-source scaling.
Grab your copy and transform curiosity into creation-build AI projects that impress professors, land jobs, and shape tomorrow's tech!
