Deep Learning with PyTorch Step-by-Step: A Beginner’s Guide

Main
Computers - Computer Science
Deep Learning with PyTorch...

Deep Learning with PyTorch Step-by-Step: A Beginner’s Guide

Daniel Voigt Godoy

5.0 / 5.0

How much do you like this book?

What’s the quality of the file?

Download the book for quality assessment

What’s the quality of the downloaded files?

Categories:

Computers - Computer Science

Language:

english

File:

PDF, 22.30 MB

IPFS:

,

english0

Read Online

Conversion to is in progress

Conversion to is failed

You may be interested in

Kevin P. Murphy — Probabilistic Machine Learning: Advanced Topics - Draft

Aston Zhang, Zachary C. Lipton, Mu Li, and Alexander J. Smola — Dive into Deep Learning

SEL, TAM — PYTORCH BASICS: FOR ABSOLUTE BEGINNERS

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

SEL, TAM [SEL, TAM] — PYTORCH BASICS: FOR ABSOLUTE BEGINNERS

Jason Brownlee — Deep Learning for Computer Vision - Image Classification, Object Detection and Face Recognition in Python

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

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

Ian Pointer — Programming PyTorch for Deep Learning

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

Michael Nielsen — Neural Networks and Deep Learning

Andrew Ng — Machine Learning Yearning (Draft Version)

Venelin Valkov — Get Sh*t Done With Pytorch: Solve Real-World Machine Learning Problems

Andrew Ng — Machine Learning Yearning

Harrison Kinsley, Daniel Kukieła — Neural Networks from Scratch in Python

John D. Kelleher, Brian Mac Namee, Aoife D'Arcy — Fundamentals of Machine Learning for Predictive Data Analytics: Algorithms, Worked Examples, and Case Studies

Kevin P. Murphy — Probabilistic Machine Learning: An Introduction

Simon J.D. Prince — Understanding Deep Learning

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

Cathy O’Neil — Weapons of Math Destruction

Valliappa Lakshmanan, Martin Görner, Ryan Gillard — Practical Machine Learning for Computer Vision: End-to-End Machine Learning for Images

Lewis Tunstall, Leandro von Werra, Thomas Wolf — Natural Language Processing with Transformers: Building Language Applications with Hugging Face

Lewis Tunstall, Leandro von Werra, Thomas Wolf — Natural Language Processing with Transformers: Building Language Applications with Hugging Face

Hala Nelson — Essential Math for AI: Next-Level Mathematics for Efficient and Successful AI Systems

Chip Huyen — Designing Machine Learning Systems: An Iterative Process for Production-Ready Applications (Third Early Release)

Valliappa Lakshmanan, Sara Robinson, Michael Munn — Machine Learning Design Patterns: Solutions to Common Challenges in Data Preparation, Model Building, and MLOps

Nikhil Ketkar; Jojo Moolayil — Deep Learning with Python: Learn Best Practices of Deep Learning Models with PyTorch

Delip Rao, Brian McMahan — Natural Language Processing with PyTorch: Build Intelligent Language Applications Using Deep Learning

Seth Weidman — Deep Learning from Scratch

Emmanuel Ameisen — Building Machine Learning Powered Applications: Going from Idea to Product

Ian Pointer — Programming PyTorch for Deep Learning: Creating and Deploying Deep Learning Applications

Jeremy Howard, Sylvain Gugger — Deep Learning for Coders With Fastai and Pytorch: AI Applications Without a PhD

Sowmya Vajjala, Bodhisattwa Majumder, Anuj Gupta, Harshit Surana — Practical Natural Language Processing: A Comprehensive Guide to Building Real-World NLP Systems

Joe Papa — PyTorch Pocket Reference: Building and Deploying Deep Learning Models

Francois Chollet — Deep Learning with Python, 2nd Edition (Final Release)

Eli Stevens, Luca Antiga, Thomas Viehmann — Deep Learning With Pytorch

Mohamed Elgendy — Deep Learning for Vision Systems

Chris A. Mattmann — Machine Learning with TensorFlow

Edward Raff — Inside Deep Learning: Math, Algorithms, Models (Final Release)

Ben Wilson — Machine Learning Engineering in Action (Final Release)

Sebastian Raschka — Build a Large Language Model (From Scratch) (MEAP)

Ronald T. Kneusel — Math for Deep Learning: What You Need to Know to Understand Neural Networks

Most frequently terms