Building Transformer Models with Attention

Main
Building Transformer Models with...

Building Transformer Models with Attention

Stefania Cristina and Mehreen Saeed

5.0 / 5.0

Quanto ti piace questo libro?

Qual è la qualità del file?

Scarica il libro per la valutazione della qualità

Qual è la qualità dei file scaricati?

Create Your Own Transformer from Scratch with Keras

Anno:

2022

Lingua:

english

File:

PDF, 7.40 MB

IPFS:

,

english, 2022

Leggi Online

La conversione in è in corso

La conversione in non è riuscita

Puoi essere interessati a

Jason Brownlee — Ensemble Machine Learning With Python: 7-Day Mini-Course

Uday Kamath — Transformers for Machine Learning A Deep Dive

Jason Brownlee — Basics For Linear Algebra For Machine Learning, Discover The Mathematical Language Of Data In Python

Daniel Voigt Godoy — Deep Learning with PyTorch Step-by-Step: A Beginner’s Guide

Magnus Ekman — Learning Deep Learning: Theory and Practice of Neural Networks, Computer Vision, NLP, and Transformers using TensorFlow

Mark Liu — Machine Learning, Animated (converted)

Denis Rothman — Transformers for Natural Language Processing and Computer Vision, Third Edition

machine learning algorithms — machine learning algorithms

Rayan Murray — Natural Language Processing for Beginners: Demystifying Language in the Digital Age

Mark Watson — LangChain and LlamaIndex Projects Lab Book: Hooking Large Language Models Up to the Real World

【美】罗纳德·T. 纽塞尔 — 深度学习的数学-使用Python语言

Ansari Hasanraza — Mastering TensorFlow: Unleashing the Power of Deep Learning: A Hands-On Guide to Building Neural Networks, Image Processing, and Natural Language Understanding with TensorFlow

John Michael Winn & Christopher M. Bishop & Thomas Diethe & John Guiver & Yordan Zaykov — Model-Based Machine Learning

Mark Liu — Learn Generative AI with PyTorch

Sanket Subhash Khandare — Mastering Large Language Models: Advanced techniques, applications, cutting-edge methods, and top LLMs

Krishnendu Chaudhury — Math and Architectures of Deep Learning

Nicole Koenigstein — Transformers in Action (MEAP v7) 2024

Jay Alammar, Maarten Grootendorst — Hands-On Large Language Models (for True Epub)

熊涛 — 大语言模型-基础与前沿

Savaş Yıldırım, Meysam Asgari-Chenaghlu — Mastering Transformers: The Journey from BERT to Large Language Models and Stable Diffusion

Alto, Valentina; — Building LLM Apps: Create Intelligent Apps and Agents with Large Language Models

Louis-François Bouchard — Building LLMs for Production: Enhancing LLM Abilities and Reliability with Prompting, Fine-Tuning, and RAG

Sinan Ozdemir — Quick Start Guide to Large Language Models, Second Edition

Simon J.D. Prince — Understanding Deep Learning

Antonio Torralba, Phillip Isola, William T. Freeman — Foundations of Computer Vision

Uday Kamath, Kenneth L. Graham, Wael Emara — Transformers for Machine Learning: A Deep Dive

Bhowmik, Mrinal Kanti, — Computer Vision: Object Detection In Adversarial Vision

Ali H. Sayed — Inference and Learning from Data: Volume 1: Foundations

Ali H. Sayed — Inference and Learning from Data: Volume 2: Inference

Ali H. Sayed — Inference and Learning from Data: Volume 3: Learning

Jyotika Singh — Natural Language Processing in the Real World: Text Processing, Analytics, and Classification

Cecilia Ka Yuk Chan, Tom Colloton — Generative AI in Higher Education

Lewis Tunstall, Leandro von Werra, Thomas Wolf — Natural Language Processing with Transformers, Revised Edition

Ryan Mitchell — Web Scraping with Python: Data Extraction from the Modern Web

James Phoenix, Mike Taylor — Prompt Engineering for Generative AI: Future-Proof Inputs for Reliable Al Outputs

Chris Fregly, Antje Barth, Shelbee Eigenbrode — Generative AI on AWS: Building Context-Aware Multimodal Reasoning Applications

Tom Taulli — AI-Assisted Programming: Better Planning, Coding, Testing, and Deployment

Nicole Koenigstein — Transformers: The Definitive Guide (for Raymond Rhine)

Shreyas Subramanian — Large Language Model-Based Solutions: HOW TO DELIVER VALUE WITH COST-EFFECTIVE GENERATIVE AI APPLICATIONS

Ian Khan — The Quick Guide to Prompt Engineering

Patanjali Kashyap — Machine Learning for Decision Makers: Cognitive Computing Fundamentals for Better Decision Making, 2nd

Krishnendu Chaudhury — Math and Architectures of Deep Learning (Final Release)

Termini più frequenti

Liste di libri correlati