---
product_id: 169894079
title: "Deep Learning with PyTorch: Build, train, and tune neural networks using Python tools"
price: "SAR 34"
currency: SAR
in_stock: true
reviews_count: 13
url: https://www.desertcart.com.sa/products/169894079-deep-learning-with-pytorch-build-train-and-tune-neural-networks
store_origin: SA
region: Saudi Arabia
---

# Deep Learning with PyTorch: Build, train, and tune neural networks using Python tools

**Price:** SAR 34
**Availability:** ✅ In Stock

## Quick Answers

- **What is this?** Deep Learning with PyTorch: Build, train, and tune neural networks using Python tools
- **How much does it cost?** SAR 34 with free shipping
- **Is it available?** Yes, in stock and ready to ship
- **Where can I buy it?** [www.desertcart.com.sa](https://www.desertcart.com.sa/products/169894079-deep-learning-with-pytorch-build-train-and-tune-neural-networks)

## Best For

- Customers looking for quality international products

## Why This Product

- Free international shipping included
- Worldwide delivery with tracking
- 15-day hassle-free returns

## Description

“We finally have the definitive treatise on PyTorch! It covers the basics and abstractions in great detail. I hope this book becomes your extended reference document.” —Soumith Chintala, co-creator of PyTorch Key Features Written by PyTorch’s creator and key contributors Develop deep learning models in a familiar Pythonic way Use PyTorch to build an image classifier for cancer detection Diagnose problems with your neural network and improve training with data augmentation Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About The Book Every other day we hear about new ways to put deep learning to good use: improved medical imaging, accurate credit card fraud detection, long range weather forecasting, and more. PyTorch puts these superpowers in your hands. Instantly familiar to anyone who knows Python data tools like NumPy and Scikit-learn, PyTorch simplifies deep learning without sacrificing advanced features. It’s great for building quick models, and it scales smoothly from laptop to enterprise. Deep Learning with PyTorch teaches you to create deep learning and neural network systems with PyTorch. This practical book gets you to work right away building a tumor image classifier from scratch. After covering the basics, you’ll learn best practices for the entire deep learning pipeline, tackling advanced projects as your PyTorch skills become more sophisticated. All code samples are easy to explore in downloadable Jupyter notebooks. What You Will Learn Understanding deep learning data structures such as tensors and neural networks Best practices for the PyTorch Tensor API, loading data in Python, and visualizing results Implementing modules and loss functions Utilizing pretrained models from PyTorch Hub Methods for training networks with limited inputs Sifting through unreliable results to diagnose and fix problems in your neural network Improve your results with augmented data, better model architecture, and fine tuning This Book Is Written For For Python programmers with an interest in machine learning. No experience with PyTorch or other deep learning frameworks is required. About The Authors Eli Stevens has worked in Silicon Valley for the past 15 years as a software engineer, and the past 7 years as Chief Technical Officer of a startup making medical device software. Luca Antiga is co-founder and CEO of an AI engineering company located in Bergamo, Italy, and a regular contributor to PyTorch. Thomas Viehmann is a Machine Learning and PyTorch speciality trainer and consultant based in Munich, Germany and a PyTorch core developer. Table of Contents PART 1 - CORE PYTORCH 1 Introducing deep learning and the PyTorch Library 2 Pretrained networks 3 It starts with a tensor 4 Real-world data representation using tensors 5 The mechanics of learning 6 Using a neural network to fit the data 7 Telling birds from airplanes: Learning from images 8 Using convolutions to generalize PART 2 - LEARNING FROM IMAGES IN THE REAL WORLD: EARLY DETECTION OF LUNG CANCER 9 Using PyTorch to fight cancer 10 Combining data sources into a unified dataset 11 Training a classification model to detect suspected tumors 12 Improving training with metrics and augmentation 13 Using segmentation to find suspected nodules 14 End-to-end nodule analysis, and where to go next PART 3 - DEPLOYMENT 15 Deploying to production

Review: Manning rules. - While the content of the books published by Manning varies, the vast majority of their books are excellent, and I value their policies. I typically buy the eBook + print, starting to read and learn immediately while the paperback arrives for my collection "whenever". Better yet, Manning's books are decently priced and the publisher also provides early access to books as they are being written. As the eBook progresses and you read, you can be certain that a printed copy/final eBook arrives "when done". This is extremely important in the fast-paced topics (e.g., machine learning) that these books address, and I have recommended several books of their catalogue to students in the past. This book, as well as many others of their catalogue, are pretty much hands-on and come with complementary code examples (Manning Live Book). This provides a way to get going quickly and reproduce the examples from the book without hassle.
Review: Great start for deep learning - This book starts off slow, but goes into detail about PyTorch, tensors, back propagation, etc. It is a great introduction to the field and helps to understand convolutions, resnets, etc. One large basic component that it is currently lacking is a chapter on language models and attention. Hopefully the second edition will include this information down the line. Finally, the networks here are mostly sequential. The final example that takes part in the last half of the book is not incredibly useful in my opinion, but it does help to see a DL project all the way through. A few chapters about branching networks, combining 1D/2D/3D information, cross attention, and some of the current interesting complexity in the field would be welcome.

## Technical Specifications

| Specification | Value |
|---------------|-------|
| Best Sellers Rank | #563,925 in Books ( See Top 100 in Books ) #192 in Computer Neural Networks #205 in Data Processing #404 in Python Programming |
| Customer Reviews | 4.5 out of 5 stars 154 Reviews |

## Images

![Deep Learning with PyTorch: Build, train, and tune neural networks using Python tools - Image 1](https://m.media-amazon.com/images/I/71aOEsvpsIL.jpg)

## Customer Reviews

### ⭐⭐⭐⭐⭐ Manning rules.
*by J***R on July 31, 2023*

While the content of the books published by Manning varies, the vast majority of their books are excellent, and I value their policies. I typically buy the eBook + print, starting to read and learn immediately while the paperback arrives for my collection "whenever". Better yet, Manning's books are decently priced and the publisher also provides early access to books as they are being written. As the eBook progresses and you read, you can be certain that a printed copy/final eBook arrives "when done". This is extremely important in the fast-paced topics (e.g., machine learning) that these books address, and I have recommended several books of their catalogue to students in the past. This book, as well as many others of their catalogue, are pretty much hands-on and come with complementary code examples (Manning Live Book). This provides a way to get going quickly and reproduce the examples from the book without hassle.

### ⭐⭐⭐⭐⭐ Great start for deep learning
*by D***D on September 16, 2022*

This book starts off slow, but goes into detail about PyTorch, tensors, back propagation, etc. It is a great introduction to the field and helps to understand convolutions, resnets, etc. One large basic component that it is currently lacking is a chapter on language models and attention. Hopefully the second edition will include this information down the line. Finally, the networks here are mostly sequential. The final example that takes part in the last half of the book is not incredibly useful in my opinion, but it does help to see a DL project all the way through. A few chapters about branching networks, combining 1D/2D/3D information, cross attention, and some of the current interesting complexity in the field would be welcome.

### ⭐⭐⭐⭐⭐ Boost your understanding, you skills and save you tons of time!
*by J***Z on September 9, 2020*

I purchased this book quite a few days ago and I cannot stop reading it! Although I am somewhat experienced with both PyTorch and Deep Learning, I took a course in Deep Learning and read various articles online, I cannot emphasize more how much I like this book. It organizes both PyTorch and Deep Learning material in a nice and understandable way reaching a broad audience. It is not spoon fed but it is not too technical either. It is exactly what I needed it. I strongly recommend this book and guarantee its value, just buy it and read it as soon as possible.

## Frequently Bought Together

- Deep Learning with PyTorch: Build, train, and tune neural networks using Python tools
- Machine Learning with PyTorch and Scikit-Learn: Develop machine learning and deep learning models with Python
- Build a Large Language Model (From Scratch)

---

## Why Shop on Desertcart?

- 🛒 **Trusted by 1.3+ Million Shoppers** — Serving international shoppers since 2016
- 🌍 **Shop Globally** — Access 737+ million products across 21 categories
- 💰 **No Hidden Fees** — All customs, duties, and taxes included in the price
- 🔄 **15-Day Free Returns** — Hassle-free returns (30 days for PRO members)
- 🔒 **Secure Payments** — Trusted payment options with buyer protection
- ⭐ **TrustPilot Rated 4.5/5** — Based on 8,000+ happy customer reviews

**Shop now:** [https://www.desertcart.com.sa/products/169894079-deep-learning-with-pytorch-build-train-and-tune-neural-networks](https://www.desertcart.com.sa/products/169894079-deep-learning-with-pytorch-build-train-and-tune-neural-networks)

---

*Product available on Desertcart Saudi Arabia*
*Store origin: SA*
*Last updated: 2026-06-03*