Build a Reasoning Model (From Scratch) - Marjane Mall - Image 1
204
00DH
408.00 DH
-50%

Build a Reasoning Model (From Scratch)

Livraison

Détails
Frais de livraison à partir de :
Livraison entre le Mardi 8 septembre 2026 et le Mercredi 9 septembre 2026

À propos de cet article :

Marque : GENERIC
Vendu par HEAVENBOOKS.MA

Get the eBook free when you register your print book at Manning."An exceptional deep dive into the next frontier of AI.” —Aman Chadha, Google This book is a practical guide to understanding how modern reasoning-oriented LLMs work by building their core methods step by step. The book tells a clear engineering story: start with a conventional pre-tra...

1

Mode de paiement

Paiement par carte bancaire
Carte marocaines
Paiement à la livraison
Paiement en espèce à la livraison
Politique de retours
Note de politique de retour

Description produit

Marque
GENERIC
Titre
Build a Reasoning Model (From Scratch)
Éditeur
Manning
Type de produit
paperback
Présentation du livre
paperback
Date de sortie
8/11/2026 12:00:00 AM
Langue d'origine
English
ISBN
1633434672
Nombre de pages
440 pages
Langue
English
Résumé
Get the eBook free when you register your print book at Manning."An exceptional deep dive into the next frontier of AI.” —Aman Chadha, Google This book is a practical guide to understanding how modern reasoning-oriented LLMs work by building their core methods step by step. The book tells a clear engineering story: start with a conventional pre-trained LLM, learn how text generation works, build reliable evaluation tools, improve reasoning through inference-time methods, then move into training-based approaches such as reinforcement learning and distillation. The progression is deliberate. Early chapters establish the baseline model and explain text generation, KV caching, and evaluation with math verifiers. The middle chapters show how reasoning can be improved without changing model weights, using chain-of-thought prompting, sampling, self-consistency, response scoring, and self-refinement. Later chapters move to changing the model itself through reinforcement learning with verifiable rewards, GRPO improvements, format rewards, and finally distillation from stronger reasoning models into smaller ones. The book is especially useful because it implements the core methods from scratch rather than treating them as black-box library calls. Readers see how self-consistency, self-refinement, Best-of-N, and training-based methods actually work, including their cost and latency trade-offs. It also discusses common failure modes, including cases where refinement can make answers worse. Difficult concepts such as softmax, temperature, and top-p sampling are clarified with code-linked explanations and diagrams, and visual workflows make pipelines and scoring methods easier to follow. Reading the book feels like following a guided technical build rather than a loose survey of AI topics. Each concept is introduced because the project now needs it. Diagrams, roadmaps, code listings, exercises, and repeated workflow summaries help readers stay oriented through advanced material.
Auteur
Sebastian Raschka
Date de parution
8/11/2026 12:00:00 AM