Search icon Close icon
Search icon CANCEL
Subscription
0
Cart icon
Your Cart (0 item)
Close icon
You have no products in your basket yet
Save more on your purchases! discount-offer-chevron-icon
Savings automatically calculated. No voucher code required.
Checkout

Change country

Modal Close icon
Country selected Country selected
country flag United States
Country selected
country flag Great Britain
Country selected
country flag India
Country selected
country flag Germany
Country selected
country flag France
Country selected
country flag Canada
Country selected
country flag Russia
Country selected
country flag Spain
Country selected
country flag Brazil
Country selected
country flag Australia
Country selected
country flag Singapore
Country selected
country flag Hungary
Country selected
country flag Ukraine
Country selected
country flag Luxembourg
Country selected
country flag Estonia
Country selected
country flag Lithuania
Country selected
country flag South Korea
Country selected
country flag Turkey
Country selected
country flag Switzerland
Country selected
country flag Colombia
Country selected
country flag Taiwan
Country selected
country flag Chile
Country selected
country flag Norway
Country selected
country flag Ecuador
Country selected
country flag Indonesia
Country selected
country flag New Zealand
Country selected
country flag Cyprus
Country selected
country flag Denmark
Country selected
country flag Finland
Country selected
country flag Poland
Country selected
country flag Malta
Country selected
country flag Czechia
Country selected
country flag Austria
Country selected
country flag Sweden
Country selected
country flag Italy
Country selected
country flag Egypt
Country selected
country flag Belgium
Country selected
country flag Portugal
Country selected
country flag Slovenia
Country selected
country flag Ireland
Country selected
country flag Romania
Country selected
country flag Greece
Country selected
country flag Argentina
Country selected
country flag Netherlands
Country selected
country flag Bulgaria
Country selected
country flag Latvia
Country selected
country flag South Africa
Country selected
country flag Malaysia
Country selected
country flag Japan
Country selected
country flag Slovakia
Country selected
country flag Philippines
Country selected
country flag Mexico
Country selected
country flag Thailand
Country selected
Arrow left icon
Explore Products
Best Sellers
New Releases
Books
Videos
Audiobooks
Learning Hub
Newsletter Hub
Free Learning
Arrow right icon
timer SALE ENDS IN
0 Days
:
00 Hours
:
00 Minutes
:
00 Seconds
Home > Business & Other > Deep Learning > Mastering PyTorch
Mastering PyTorch
Mastering PyTorch

Mastering PyTorch: Create and deploy deep learning models from CNNs to multimodal models, LLMs, and beyond , Second Edition

Arrow left icon
Profile Icon Ashish Ranjan Jha
Arrow right icon
€38.99
Full star icon Full star icon Full star icon Full star icon Half star icon 4.7 (20 Ratings)
Paperback May 2024 558 pages 2nd Edition
eBook
€27.99 €31.99
Paperback
€38.99
Subscription
Free Trial
Renews at €18.99p/m
Arrow left icon
Profile Icon Ashish Ranjan Jha
Arrow right icon
€38.99
Full star icon Full star icon Full star icon Full star icon Half star icon 4.7 (20 Ratings)
Paperback May 2024 558 pages 2nd Edition
eBook
€27.99 €31.99
Paperback
€38.99
Subscription
Free Trial
Renews at €18.99p/m
eBook
€27.99 €31.99
Paperback
€38.99
Subscription
Free Trial
Renews at €18.99p/m

What do you get with Print?

Product feature icon Instant access to your digital copy whilst your Print order is Shipped
Product feature icon Paperback book shipped to your preferred address
Product feature icon Redeem a companion digital copy on all Print orders
Product feature icon Access this title in our online reader with advanced features
Product feature icon DRM FREE - Read whenever, wherever and however you want
OR

Contact Details

Modal Close icon
Payment Processing...
tick Completed

Shipping Address

Billing Address

Shipping Methods
Table of content icon View table of contents Preview book icon Preview Book

Mastering PyTorch

Deep CNN Architectures

In this chapter, we will first briefly review the evolution of Convolutional Neural Network (CNN) architectures, and then we will study the different CNN architectures in detail. We will implement these CNN architectures using PyTorch, and in doing so, we aim to exhaustively explore the tools (modules and built-in functions) that PyTorch has to offer in the context of building Deep CNNs. Gaining strong CNN expertise in PyTorch will enable us to solve a number of deep learning problems involving CNNs. This will also help us in building more complex deep learning models or applications of which CNNs are a part.

This chapter will cover the following topics:

  • Why are CNNs so powerful?
  • Evolution of CNN architectures
  • Developing LeNet from scratch
  • Fine-tuning the AlexNet model
  • Running a pretrained VGG model
  • Exploring GoogLeNet and Inception v3
  • Discussing ResNet and DenseNet architectures
  • Understanding EfficientNets and the future of CNN architectures

All the code files for this chapter can be found at https://github.com/arj7192/MasteringPyTorchV2/tree/main/Chapter03.

Let us start by discussing the key features of CNNs.

Why are CNNs so powerful?

CNNs are among the most powerful machine learning models at solving challenging problems such as image classification, object detection, object segmentation, video processing, natural language processing, and speech recognition. Their success is attributed to various factors, such as the following:

  • Weight sharing: This makes CNNs parameter-efficient; that is, different features are extracted using the same set of weights or parameters. Features are the high-level representations of input data that the model generates with its parameters.
  • Automatic feature extraction: Multiple feature extraction stages help a CNN to automatically learn feature representations in a dataset.
  • Hierarchical learning: The multi-layered CNN structure helps CNNs to learn low-, mid-, and high-level features.
  • The ability to explore both spatial and temporal correlations in the data, such as in video-processing tasks.

Besides these pre-existing fundamental characteristics, CNNs have advanced over the years with the help of improvements in the following areas:

  • The use of better activation and loss functions, such as using ReLU to overcome the vanishing gradient problem.
  • Parameter optimization, such as using an optimizer based on Adaptive Momentum (Adam) instead of simple stochastic gradient descent.
  • Regularization: Applying dropouts and batch normalization besides L2 regularization.

FAQ – What is the vanishing gradient problem?

Backpropagation in neural networks works on the basis of the chain rule of differentiation. According to the chain rule, the gradient of the loss function with respect to the input layer parameters can be written as a product of gradients at each layer. If these gradients are all less than 1 – and worse still, tending toward 0 – then the product of these gradients will be a vanishingly small value. The vanishing gradient problem can cause serious trouble in the optimization process by preventing the network parameters from changing their values, which is equivalent to stunted learning.

But some of the most significant drivers of development in CNNs over the years have been the various architectural innovations:

  • Spatial exploration-based CNNs: The idea behind spatial exploration is using different kernel sizes in order to explore different levels of visual features in input data. The following diagram shows a sample architecture for a spatial exploration-based CNN model:
Figure 3.1 – Spatial exploration-based CNN

Figure 2.1: Spatial exploration-based CNN

  • Depth-based CNNs: The depth here refers to the depth of the neural network, that is, the number of layers. So, the idea here is to create a CNN model with multiple convolutional layers in order to extract highly complex visual features. The following diagram shows an example of such a model architecture:
Figure 3.2 – Depth-based CNN

Figure 2.2: Depth-based CNN

  • Width-based CNNs: Width refers to the number of channels or feature maps in the data or features extracted from the data. So, width-based CNNs are all about increasing the number of feature maps as we go from the input to the output layers, as demonstrated in the following diagram:
Figure 3.3 – Width-based CNN

Figure 2.3: Width-based CNN

  • Multi-path-based CNNs: So far, the preceding three types of architectures have had monotonicity in connections between layers; that is, direct connections exist only between consecutive layers. Multi-path CNNs brought the idea of making shortcut connections or skip connections between non-consecutive layers. The following diagram shows an example of a multi-path CNN model architecture:
Figure 3.4 – Multi-path CNN

Figure 2.4: Multi-path CNN

A key advantage of multi-path architectures is a better flow of information across several layers, thanks to the skip connections. This, in turn, also lets the gradient flow back to the input layers without too much dissipation.

Having looked at the different architectural setups found in CNN models, we will now look at how CNNs have evolved over the years ever since they were first used.

Evolution of CNN architectures

CNNs have been in existence since 1989, when the first multi-layered CNN was developed by Yann LeCun. This model could perform the visual cognition task of identifying handwritten digits. In 1998, LeCun developed an improved ConvNet model called LeNet. Due to its high accuracy in optical recognition tasks, LeNet was adopted for industrial use soon after its invention. Ever since, CNNs have been successful not only in academic research but also in practical industry use cases. The following diagram shows a brief timeline of architectural developments in the lifetime of CNNs, starting from 1989 all the way to 2020:

Figure 3.5 – CNN architecture evolution – a broad picture

Figure 2.5: CNN architecture evolution – a broad picture

As we can see, there is a significant gap between the years 1998 and 2012. This was for two reasons:

  1. There wasn’t a dataset big and suitable enough to demonstrate the capabilities of CNNs, especially deep CNNs.
  2. The available computing power was limited.

And to add to the first reason, on the existing small datasets of the time such as MNIST, classical machine learning models such as SVMs were starting to beat CNN’s performance.

The above two limitations were alleviated as we transitioned from 1998 to 2012 and beyond. Firstly, we had an exponential growth in digital data thanks to the advent of the internet and access to affordable devices such as digital cameras and smartphones. Secondly, we saw an enormous increase in our computational capabilities including the arrival of GPUs.

These changes led to a few CNN developments. The ReLU activation function was developed in order to deal with the gradient explosion and decay problem during backpropagation. Non-random initialization of network parameter values proved to be crucial. Max pooling was invented as an effective method for subsampling. GPUs were getting popular for training neural networks, especially CNNs, at scale.

Finally, and most importantly, a large-scale dedicated dataset of annotated images called ImageNet [1] was created by a research group at Stanford. This dataset is still one of the primary benchmarking datasets for CNN models to date.

With all of these developments compounding over the years, in 2012, a different architectural design brought about a massive improvement in CNN performance on the ImageNet dataset. This network was called AlexNet (named after the creator, Alex Krizhevsky). AlexNet, along with having various novel aspects such as random cropping and pretraining, established the trend of uniform and modular convolutional layer design. The uniform and modular layer structure was taken forward by repeatedly stacking such modules (of convolutional layers), resulting in very deep CNNs also known as VGGs.

Another approach of branching the blocks/modules of convolutional layers and stacking these branched blocks on top of each other proved extremely effective for tailored visual tasks. This network was called GoogLeNet (as it was developed at Google) or Inception v1 (inception being the term for those branched blocks). Several variants of the VGG and Inception networks followed, such as VGG16, VGG19, Inception v2, Inception v3, and so on.

The next phase of development began with skip connections. To tackle the problem of gradient decay while training CNNs, non-consecutive layers were connected via skip connections lest information dissipate between them due to small gradients. It should be noted that skip connections are essentially a special case of multi-path-based CNNs discussed earlier. A popular type of network that emerged with this trick, among other novel characteristics such as batch normalization, was ResNet.

A logical extension of ResNet was DenseNet, where layers were densely connected to each other; that is, each layer gets the input from all the previous layers’ output feature maps. Furthermore, hybrid architectures were then developed by mixing successful architectures from the past such as Inception-ResNet and ResNeXt, where the parallel branches within a block were increased in number.

Lately, the channel boosting technique has proven useful in improving CNN performance. The idea here is to learn novel features and exploit pre-learned features through transfer learning. Most recently, automatically designing new blocks and finding optimal CNN architectures has been a growing trend in CNN research. Examples of such CNNs are MnasNets and EfficientNets. The approach behind these models is to perform a neural architecture search to deduce an optimal CNN architecture with a uniform model scaling approach.

In the next section, we will go back to one of the earliest CNN models and take a closer look at the various CNN architectures developed since. We will build these architectures using PyTorch, training some of the models on real-world datasets. We will also explore PyTorch’s pretrained CNN models repository, popularly known as model-zoo. We will learn how to fine-tune these pretrained models as well as running predictions on them.

Left arrow icon

Page 1 of 11

Right arrow icon
Download code icon Download Code

Key benefits

  • Understand how to use PyTorch to build advanced neural network models
  • Get the best from PyTorch by working with Hugging Face, fastai, PyTorch Lightning, PyTorch Geometric, Flask, and Docker
  • Unlock faster training with multiple GPUs and optimize model deployment using efficient inference frameworks

Description

PyTorch is making it easier than ever before for anyone to build deep learning applications. This PyTorch deep learning book will help you uncover expert techniques to get the most out of your data and build complex neural network models. You’ll build convolutional neural networks for image classification and recurrent neural networks and transformers for sentiment analysis. As you advance, you'll apply deep learning across different domains, such as music, text, and image generation, using generative models, including diffusion models. You'll not only build and train your own deep reinforcement learning models in PyTorch but also learn to optimize model training using multiple CPUs, GPUs, and mixed-precision training. You’ll deploy PyTorch models to production, including mobile devices. Finally, you’ll discover the PyTorch ecosystem and its rich set of libraries. These libraries will add another set of tools to your deep learning toolbelt, teaching you how to use fastai to prototype models and PyTorch Lightning to train models. You’ll discover libraries for AutoML and explainable AI (XAI), create recommendation systems, and build language and vision transformers with Hugging Face. By the end of this book, you'll be able to perform complex deep learning tasks using PyTorch to build smart artificial intelligence models.

Who is this book for?

This deep learning with PyTorch book is for data scientists, machine learning engineers, machine learning researchers, and deep learning practitioners looking to implement advanced deep learning models using PyTorch. This book is ideal for those looking to switch from TensorFlow to PyTorch. Working knowledge of deep learning with Python is required.

What you will learn

  • Implement text, vision, and music generation models using PyTorch
  • Build a deep Q-network (DQN) model in PyTorch
  • Deploy PyTorch models on mobile devices (Android and iOS)
  • Become well versed in rapid prototyping using PyTorch with fastai
  • Perform neural architecture search effectively using AutoML
  • Easily interpret machine learning models using Captum
  • Design ResNets, LSTMs, and graph neural networks (GNNs)
  • Create language and vision transformer models using Hugging Face
Estimated delivery fee Deliver to Bulgaria

Premium delivery 7 - 10 business days

€25.95
(Includes tracking information)

Product Details

Country selected
Publication date, Length, Edition, Language, ISBN-13
Publication date : May 31, 2024
Length: 558 pages
Edition : 2nd
Language : English
ISBN-13 : 9781801074308
Category :
Business & Other
Concepts :
Deep Learning
Tools :
PyTorch

What do you get with Print?

Product feature icon Instant access to your digital copy whilst your Print order is Shipped
Product feature icon Paperback book shipped to your preferred address
Product feature icon Redeem a companion digital copy on all Print orders
Product feature icon Access this title in our online reader with advanced features
Product feature icon DRM FREE - Read whenever, wherever and however you want
OR

Contact Details

Modal Close icon
Payment Processing...
tick Completed

Shipping Address

Billing Address

Shipping Methods
Estimated delivery fee Deliver to Bulgaria

Premium delivery 7 - 10 business days

€25.95
(Includes tracking information)

Product Details

Publication date : May 31, 2024
Length: 558 pages
Edition : 2nd
Language : English
ISBN-13 : 9781801074308
Category :
Business & Other
Concepts :
Deep Learning
Tools :
PyTorch

Packt Subscriptions

See our plans and pricing
Modal Close icon
€18.99 billed monthly
Feature tick icon Unlimited access to Packt's library of 7,000+ practical books and videos
Feature tick icon Constantly refreshed with 50+ new titles a month
Feature tick icon Exclusive Early access to books as they're written
Feature tick icon Solve problems while you work with advanced search and reference features
Feature tick icon Offline reading on the mobile app
Feature tick icon Simple pricing, no contract
START FREE TRIAL
BUY NOW
€189.99 billed annually
Feature tick icon Unlimited access to Packt's library of 7,000+ practical books and videos
Feature tick icon Constantly refreshed with 50+ new titles a month
Feature tick icon Exclusive Early access to books as they're written
Feature tick icon Solve problems while you work with advanced search and reference features
Feature tick icon Offline reading on the mobile app
Feature tick icon Choose a DRM-free eBook or Video every month to keep
Feature tick icon PLUS own as many other DRM-free eBooks or Videos as you like for just €5 each
Feature tick icon Exclusive print discounts
START FREE TRIAL
BUY NOW
€264.99 billed in 18 months
Feature tick icon Unlimited access to Packt's library of 7,000+ practical books and videos
Feature tick icon Constantly refreshed with 50+ new titles a month
Feature tick icon Exclusive Early access to books as they're written
Feature tick icon Solve problems while you work with advanced search and reference features
Feature tick icon Offline reading on the mobile app
Feature tick icon Choose a DRM-free eBook or Video every month to keep
Feature tick icon PLUS own as many other DRM-free eBooks or Videos as you like for just €5 each
Feature tick icon Exclusive print discounts
START FREE TRIAL
BUY NOW

Frequently bought together

Mastering PyTorch
Mastering PyTorch
Read more
May 2024 558 pages
Full star icon 4.7 (20)
eBook
eBook
  • eBook eBook €27.99
  • Paperback Paperback €38.99
€27.99 €31.99
€38.99
Machine Learning with PyTorch and Scikit-Learn
Machine Learning with PyTorch and Scikit-Learn
Read more
Feb 2022 774 pages
Full star icon 4.4 (95)
eBook
eBook
  • eBook eBook €28.99
  • Paperback Paperback €41.99
€28.99 €32.99
€41.99
Modern Computer Vision with PyTorch
Modern Computer Vision with PyTorch
Read more
Jun 2024 746 pages
Full star icon 4.2 (20)
eBook
eBook
  • eBook eBook €32.99
  • Paperback Paperback €45.99
€32.99 €36.99
€45.99
Stars icon
Total € 126.97
Mastering PyTorch
€38.99
Machine Learning with PyTorch and Scikit-Learn
€41.99
Modern Computer Vision with PyTorch
€45.99
Total € 126.97 Stars icon

Table of Contents

20 Chapters
Overview of Deep Learning Using PyTorch Chevron down icon Chevron up icon
Overview of Deep Learning Using PyTorch
A refresher on deep learning
Optimization schedule
Exploring the PyTorch library in contrast to TensorFlow
Summary
Reference list
Deep CNN Architectures Chevron down icon Chevron up icon
Deep CNN Architectures
Why are CNNs so powerful?
Evolution of CNN architectures
Developing LeNet from scratch
Fine-tuning the AlexNet model
Running a pretrained VGG model
Exploring GoogLeNet and Inception v3
Discussing ResNet and DenseNet architectures
Understanding EfficientNets and the future of CNN architectures
Summary
References
Combining CNNs and LSTMs Chevron down icon Chevron up icon
Combining CNNs and LSTMs
Building a neural network with CNNs and LSTMs
Building an image caption generator using PyTorch
Summary
References
Deep Recurrent Model Architectures Chevron down icon Chevron up icon
Deep Recurrent Model Architectures
Exploring the evolution of recurrent networks
Training RNNs for sentiment analysis
Building a bidirectional LSTM
Discussing GRUs and attention-based models
Summary
References
Advanced Hybrid Models Chevron down icon Chevron up icon
Advanced Hybrid Models
Building a transformer model for language modeling
Developing a RandWireNN model from scratch
Summary
References
Graph Neural Networks Chevron down icon Chevron up icon
Graph Neural Networks
Introduction to GNNs
Types of graph learning tasks
Reviewing prominent GNN models
Training a GAT model with PyTorch Geometric
Summary
Reference list
Music and Text Generation with PyTorch Chevron down icon Chevron up icon
Music and Text Generation with PyTorch
Building a transformer-based text generator with PyTorch
Using GPT models as text generators
Generating MIDI music with LSTMs using PyTorch
Summary
References
Neural Style Transfer Chevron down icon Chevron up icon
Neural Style Transfer
Understanding how to transfer style between images
Implementing neural style transfer using PyTorch
Summary
References
Deep Convolutional GANs Chevron down icon Chevron up icon
Deep Convolutional GANs
Defining the generator and discriminator networks
Training a DCGAN using PyTorch
Using GANs for style transfer
Summary
References
Image Generation Using Diffusion Chevron down icon Chevron up icon
Image Generation Using Diffusion
Understanding image generation using diffusion
Training a diffusion model for image generation
Understanding text-to-image generation using diffusion
Using the Stable Diffusion model to generate images from text
Summary
Reference list
Deep Reinforcement Learning Chevron down icon Chevron up icon
Deep Reinforcement Learning
Reviewing RL concepts
Discussing Q-learning
Understanding deep Q-learning
Building a DQN model in PyTorch
Summary
Reference list
Model Training Optimizations Chevron down icon Chevron up icon
Model Training Optimizations
Distributed training with PyTorch
Distributed training on GPUs with CUDA
Summary
Reference list
Operationalizing PyTorch Models into Production Chevron down icon Chevron up icon
Operationalizing PyTorch Models into Production
Model serving in PyTorch
Building a basic model server
Creating a model microservice
Serving a PyTorch model using TorchServe
Exporting universal PyTorch models using TorchScript and ONNX
Running a PyTorch model in C++
Using ONNX to export PyTorch models
Serving PyTorch models in the cloud
Summary
PyTorch on Mobile Devices Chevron down icon Chevron up icon
PyTorch on Mobile Devices
Deploying a PyTorch model on Android
Using the phone camera in the Android app to capture images
Building PyTorch apps on iOS
Summary
Reference list
Rapid Prototyping with PyTorch Chevron down icon Chevron up icon
Rapid Prototyping with PyTorch
Using fastai to set up model training in a few minutes
Training models on any hardware using PyTorch Lightning
Profiling MNIST model inference using PyTorch Profiler
Summary
Reference list
PyTorch and AutoML Chevron down icon Chevron up icon
PyTorch and AutoML
Finding the best neural architectures with AutoML
Using Optuna for hyperparameter search
Summary
Reference list
PyTorch and Explainable AI Chevron down icon Chevron up icon
PyTorch and Explainable AI
Model interpretability in PyTorch
Using Captum to interpret models
Summary
Reference List
Recommendation Systems with PyTorch Chevron down icon Chevron up icon
Recommendation Systems with PyTorch
Using deep learning for recommendation systems
Understanding and processing the MovieLens dataset
Training and evaluating a recommendation system model
Building a recommendation system using the trained model
Summary
Reference list
PyTorch and Hugging Face Chevron down icon Chevron up icon
PyTorch and Hugging Face
Understanding Hugging Face within the PyTorch context
Using the Hugging Face Hub for pre-trained models
Using the Hugging Face Datasets library with PyTorch
Using Accelerate to speed up PyTorch model training
Using Optimum to optimize PyTorch model deployment
Summary
Reference list
Index Chevron down icon Chevron up icon
Index

Recommendations for you

Left arrow icon
Mastering PyTorch
Mastering PyTorch
Read more
May 2024 558 pages
Full star icon 4.7 (20)
eBook
eBook
  • eBook eBook €27.99
  • Paperback Paperback €38.99
€27.99 €31.99
€38.99
Microsoft 365 and SharePoint Online Cookbook
Microsoft 365 and SharePoint Online Cookbook
Read more
Feb 2024 640 pages
Full star icon 4.8 (23)
eBook
eBook
  • eBook eBook €20.99
  • Paperback Paperback €29.99
€20.99 €23.99
€29.99
Tools and Skills for .NET 8
Tools and Skills for .NET 8
Read more
Jul 2024 778 pages
Full star icon 5 (12)
eBook
eBook
  • eBook eBook €25.99
  • Paperback Paperback €35.99
€25.99 €28.99
€35.99
Information Theory
Information Theory
Read more
Nov 2024 294 pages
eBook
eBook
  • eBook eBook €6.99
€6.99 €7.99
The Music Producer's Ultimate Guide to FL Studio 21
The Music Producer's Ultimate Guide to FL Studio 21
Read more
Jun 2023 462 pages
Full star icon 5 (11)
eBook
eBook
  • eBook eBook €20.99
  • Paperback Paperback €29.99
€20.99 €23.99
€29.99
Workflow Automation with Microsoft Power Automate, Second edition
Workflow Automation with Microsoft Power Automate, Second edition
Read more
Aug 2022 424 pages
Full star icon 4.3 (36)
eBook
eBook
  • eBook eBook €26.98
  • Paperback Paperback €37.99
€26.98 €29.99
€37.99
Mastering QuickBooks 2024
Mastering QuickBooks 2024
Read more
Dec 2023 570 pages
Full star icon 4.6 (19)
eBook
eBook
  • eBook eBook €17.99
  • Paperback Paperback €26.99
€17.99 €20.99
€26.99
Programming Microsoft Dynamics 365 Business Central
Programming Microsoft Dynamics 365 Business Central
Read more
Oct 2024 466 pages
Full star icon 5 (1)
eBook
eBook
  • eBook eBook €23.99
  • Paperback Paperback €33.99
€23.99 €26.99
€33.99
Unreal Engine 5 Character Creation, Animation, and Cinematics
Unreal Engine 5 Character Creation, Animation, and Cinematics
Read more
Jun 2022 598 pages
Full star icon 4.7 (16)
eBook
eBook
  • eBook eBook €42.99
  • Paperback Paperback €59.99
€42.99 €47.99
€59.99
Power Apps Tips, Tricks, and Best Practices
Power Apps Tips, Tricks, and Best Practices
Read more
Nov 2024 430 pages
Full star icon 4.5 (2)
eBook
eBook
  • eBook eBook €23.99
  • Paperback Paperback €33.99
€23.99 €26.99
€33.99
Right arrow icon

Customer reviews

Top Reviews
  • Top Reviews
  • Most Recent
Rating distribution
Full star icon Full star icon Full star icon Full star icon Half star icon 4.7
(20 Ratings)
5 star 75%
4 star 20%
3 star 5%
2 star 0%
1 star 0%
Filter icon Filter
Top Reviews
  • Top Reviews
  • Most Recent

Filter reviews by

Franziska Kirschner Sep 02, 2024
Full star icon Full star icon Full star icon Full star icon Full star icon 5
Absolutely love this book both as a reference and to learn new techniques.I'm a ML researcher converting from Tensorflow to Pytorch and wanted a reference guide as I made the transition. The hands-on code examples were super useful to get up and running, and much more clearly explained than just trying to Google what to do.The pieces on engineering included a bunch of optimisations I hadn't considered in in the past, so I ended up learning a lot more than I anticipated. This book is very well-rounded and considers both the practical application and the theory behind it.I would highly recommend to any ML researcher or engineer!
Amazon Verified review Amazon
Fabio Milano Sep 07, 2024
Full star icon Full star icon Full star icon Full star icon Full star icon 5
If you're looking for a hands-on, comprehensive guide to modern neural network architectures and the PyTorch ecosystem, this book is a gem! The author's decade of deep learning experience shines through with practical, step-by-step instructions that truly guide you in building state-of-the-art neural networks. The balance between depth and breadth is perfect.Whether you're a data scientist or machine learning engineer wanting to upskill in the latest deep learning tools and frameworks, or a software engineer curious about modern machine learning, this book has got you covered. The clear explanations and practical examples make complex concepts easy to grasp and apply. Highly recommended!
Amazon Verified review Amazon
Arun May 31, 2024
Full star icon Full star icon Full star icon Full star icon Full star icon 5
The book is an easy-read. It is quite resourceful for anyone looking to deepen their understanding of neural network models and its practical implementation using pytorch.The concepts are well-explained with examples. I like the way the book covered different type of data: images, text, sounds, etc. There are things for everyone.Other attraction is that it adapts to emerging concepts like diffusion models and integration with huggingface leveraging off-the-shelf pretrained models. Further, it provides practical guidance on deploying models to production, including on mobile devices.Overall, Mastering PyTorch is a must-have for a deep learning enthusiast who looks into recent learning techniques and its applications. Highly recommended!
Amazon Verified review Amazon
Kelsey S. Jul 27, 2024
Full star icon Full star icon Full star icon Full star icon Full star icon 5
I've been working with PyTorch for a while now, but I really feel like this book took my skills to the next level. The explanations are clear and concise, and the code examples really helped me to understand the concepts. I especially appreciated the coverage of advanced techniques like generative models and graph neural networks, but most of the content is also completely suitable for beginners. I appreciate that the author also focuses on the modern landscape, including how to transition from TensorFlow to PyTorch, coverage of LLMs, neural networks in mobile settings (eg quantized models), autoML, integration with Hugging Face and beyond. I've already used some of the techniques from the book in my own work, and I'm really excited to see what else I can accomplish with PyTorch. Overall, I highly recommend this book to anyone who wants to learn more about PyTorch and deep learning.
Amazon Verified review Amazon
Saloni Shukla Jun 09, 2024
Full star icon Full star icon Full star icon Full star icon Full star icon 5
I enjoyed reading "Mastering PyTorch" because it offered a hands-on, practical approach to deep learning with PyTorch. The clear explanations, real-world examples, and up-to-date content kept me engaged and informed. The expert insights and comprehensive coverage made complex concepts accessible and relevant, significantly enhancing my learning experience.
Amazon Verified review Amazon

People who bought this also bought

Left arrow icon
Workflow Automation with Microsoft Power Automate, Second edition
Workflow Automation with Microsoft Power Automate, Second edition
Read more
Aug 2022 424 pages
Full star icon 4.3 (36)
eBook
eBook
  • eBook eBook €26.98
  • Paperback Paperback €37.99
€26.98 €29.99
€37.99
101 UX Principles – 2nd edition
101 UX Principles – 2nd edition
Read more
May 2022 454 pages
Full star icon 4.7 (60)
eBook
eBook
  • eBook eBook €23.99
  • Paperback Paperback €33.99
  • Audiobook Audiobook €31.99
€23.99 €26.99
€33.99
€31.99
Mastering PostgreSQL 15
Mastering PostgreSQL 15
Read more
Jan 2023 522 pages
Full star icon 4.9 (7)
eBook
eBook
  • eBook eBook €33.99
  • Paperback Paperback €46.99
€33.99 €37.99
€46.99
Mastering Microsoft Dynamics 365 Business Central
Mastering Microsoft Dynamics 365 Business Central
Read more
Mar 2024 684 pages
Full star icon 4.9 (17)
eBook
eBook
  • eBook eBook €26.98
  • Paperback Paperback €37.99
€26.98 €29.99
€37.99
Learn Blender Simulations the Right Way
Learn Blender Simulations the Right Way
Read more
Nov 2022 368 pages
Full star icon 4 (14)
eBook
eBook
  • eBook eBook €23.99
  • Paperback Paperback €33.99
€23.99 €26.99
€33.99
Right arrow icon

About the author

Profile icon Ashish Ranjan Jha
Ashish Ranjan Jha
Ashish Ranjan Jha received his bachelor's degree in electrical engineering from IIT Roorkee (India), a master's degree in Computer Science from EPFL (Switzerland), and an MBA degree from Quantic School of Business (Washington). He has received a distinction in all 3 of his degrees. He has worked for large technology companies, including Oracle and Sony as well as the more recent tech unicorns such as Revolut, mostly focused on artificial intelligence. He currently works as a machine learning engineer. Ashish has worked on a range of products and projects, from developing an app that uses sensor data to predict the mode of transport to detecting fraud in car damage insurance claims. Besides being an author, machine learning engineer, and data scientist, he also blogs frequently on his personal blog site about the latest research and engineering topics around machine learning.
Read more
See other products by Ashish Ranjan Jha
Get free access to Packt library with over 7500+ books and video courses for 7 days!
Start Free Trial

FAQs

What is the digital copy I get with my Print order? Chevron down icon Chevron up icon

When you buy any Print edition of our Books, you can redeem (for free) the eBook edition of the Print Book you’ve purchased. This gives you instant access to your book when you make an order via PDF, EPUB or our online Reader experience.

What is the delivery time and cost of print book? Chevron down icon Chevron up icon

Shipping Details

USA:

'

Economy: Delivery to most addresses in the US within 10-15 business days

Premium: Trackable Delivery to most addresses in the US within 3-8 business days

UK:

Economy: Delivery to most addresses in the U.K. within 7-9 business days.
Shipments are not trackable

Premium: Trackable delivery to most addresses in the U.K. within 3-4 business days!
Add one extra business day for deliveries to Northern Ireland and Scottish Highlands and islands

EU:

Premium: Trackable delivery to most EU destinations within 4-9 business days.

Australia:

Economy: Can deliver to P. O. Boxes and private residences.
Trackable service with delivery to addresses in Australia only.
Delivery time ranges from 7-9 business days for VIC and 8-10 business days for Interstate metro
Delivery time is up to 15 business days for remote areas of WA, NT & QLD.

Premium: Delivery to addresses in Australia only
Trackable delivery to most P. O. Boxes and private residences in Australia within 4-5 days based on the distance to a destination following dispatch.

India:

Premium: Delivery to most Indian addresses within 5-6 business days

Rest of the World:

Premium: Countries in the American continent: Trackable delivery to most countries within 4-7 business days

Asia:

Premium: Delivery to most Asian addresses within 5-9 business days

Disclaimer:
All orders received before 5 PM U.K time would start printing from the next business day. So the estimated delivery times start from the next day as well. Orders received after 5 PM U.K time (in our internal systems) on a business day or anytime on the weekend will begin printing the second to next business day. For example, an order placed at 11 AM today will begin printing tomorrow, whereas an order placed at 9 PM tonight will begin printing the day after tomorrow.


Unfortunately, due to several restrictions, we are unable to ship to the following countries:

  1. Afghanistan
  2. American Samoa
  3. Belarus
  4. Brunei Darussalam
  5. Central African Republic
  6. The Democratic Republic of Congo
  7. Eritrea
  8. Guinea-bissau
  9. Iran
  10. Lebanon
  11. Libiya Arab Jamahriya
  12. Somalia
  13. Sudan
  14. Russian Federation
  15. Syrian Arab Republic
  16. Ukraine
  17. Venezuela
What is custom duty/charge? Chevron down icon Chevron up icon

Customs duty are charges levied on goods when they cross international borders. It is a tax that is imposed on imported goods. These duties are charged by special authorities and bodies created by local governments and are meant to protect local industries, economies, and businesses.

Do I have to pay customs charges for the print book order? Chevron down icon Chevron up icon

The orders shipped to the countries that are listed under EU27 will not bear custom charges. They are paid by Packt as part of the order.

List of EU27 countries: www.gov.uk/eu-eea:

A custom duty or localized taxes may be applicable on the shipment and would be charged by the recipient country outside of the EU27 which should be paid by the customer and these duties are not included in the shipping charges been charged on the order.

How do I know my custom duty charges? Chevron down icon Chevron up icon

The amount of duty payable varies greatly depending on the imported goods, the country of origin and several other factors like the total invoice amount or dimensions like weight, and other such criteria applicable in your country.

For example:

  • If you live in Mexico, and the declared value of your ordered items is over $ 50, for you to receive a package, you will have to pay additional import tax of 19% which will be $ 9.50 to the courier service.
  • Whereas if you live in Turkey, and the declared value of your ordered items is over € 22, for you to receive a package, you will have to pay additional import tax of 18% which will be € 3.96 to the courier service.
How can I cancel my order? Chevron down icon Chevron up icon

Cancellation Policy for Published Printed Books:

You can cancel any order within 1 hour of placing the order. Simply contact customercare@packt.com with your order details or payment transaction id. If your order has already started the shipment process, we will do our best to stop it. However, if it is already on the way to you then when you receive it, you can contact us at customercare@packt.com using the returns and refund process.

Please understand that Packt Publishing cannot provide refunds or cancel any order except for the cases described in our Return Policy (i.e. Packt Publishing agrees to replace your printed book because it arrives damaged or material defect in book), Packt Publishing will not accept returns.

What is your returns and refunds policy? Chevron down icon Chevron up icon

Return Policy:

We want you to be happy with your purchase from Packtpub.com. We will not hassle you with returning print books to us. If the print book you receive from us is incorrect, damaged, doesn't work or is unacceptably late, please contact Customer Relations Team on customercare@packt.com with the order number and issue details as explained below:

  1. If you ordered (eBook, Video or Print Book) incorrectly or accidentally, please contact Customer Relations Team on customercare@packt.com within one hour of placing the order and we will replace/refund you the item cost.
  2. Sadly, if your eBook or Video file is faulty or a fault occurs during the eBook or Video being made available to you, i.e. during download then you should contact Customer Relations Team within 14 days of purchase on customercare@packt.com who will be able to resolve this issue for you.
  3. You will have a choice of replacement or refund of the problem items.(damaged, defective or incorrect)
  4. Once Customer Care Team confirms that you will be refunded, you should receive the refund within 10 to 12 working days.
  5. If you are only requesting a refund of one book from a multiple order, then we will refund you the appropriate single item.
  6. Where the items were shipped under a free shipping offer, there will be no shipping costs to refund.

On the off chance your printed book arrives damaged, with book material defect, contact our Customer Relation Team on customercare@packt.com within 14 days of receipt of the book with appropriate evidence of damage and we will work with you to secure a replacement copy, if necessary. Please note that each printed book you order from us is individually made by Packt's professional book-printing partner which is on a print-on-demand basis.

What tax is charged? Chevron down icon Chevron up icon

Currently, no tax is charged on the purchase of any print book (subject to change based on the laws and regulations). A localized VAT fee is charged only to our European and UK customers on eBooks, Video and subscriptions that they buy. GST is charged to Indian customers for eBooks and video purchases.

What payment methods can I use? Chevron down icon Chevron up icon

You can pay with the following card types:

  1. Visa Debit
  2. Visa Credit
  3. MasterCard
  4. PayPal
What is the delivery time and cost of print books? Chevron down icon Chevron up icon

Shipping Details

USA:

'

Economy: Delivery to most addresses in the US within 10-15 business days

Premium: Trackable Delivery to most addresses in the US within 3-8 business days

UK:

Economy: Delivery to most addresses in the U.K. within 7-9 business days.
Shipments are not trackable

Premium: Trackable delivery to most addresses in the U.K. within 3-4 business days!
Add one extra business day for deliveries to Northern Ireland and Scottish Highlands and islands

EU:

Premium: Trackable delivery to most EU destinations within 4-9 business days.

Australia:

Economy: Can deliver to P. O. Boxes and private residences.
Trackable service with delivery to addresses in Australia only.
Delivery time ranges from 7-9 business days for VIC and 8-10 business days for Interstate metro
Delivery time is up to 15 business days for remote areas of WA, NT & QLD.

Premium: Delivery to addresses in Australia only
Trackable delivery to most P. O. Boxes and private residences in Australia within 4-5 days based on the distance to a destination following dispatch.

India:

Premium: Delivery to most Indian addresses within 5-6 business days

Rest of the World:

Premium: Countries in the American continent: Trackable delivery to most countries within 4-7 business days

Asia:

Premium: Delivery to most Asian addresses within 5-9 business days

Disclaimer:
All orders received before 5 PM U.K time would start printing from the next business day. So the estimated delivery times start from the next day as well. Orders received after 5 PM U.K time (in our internal systems) on a business day or anytime on the weekend will begin printing the second to next business day. For example, an order placed at 11 AM today will begin printing tomorrow, whereas an order placed at 9 PM tonight will begin printing the day after tomorrow.


Unfortunately, due to several restrictions, we are unable to ship to the following countries:

  1. Afghanistan
  2. American Samoa
  3. Belarus
  4. Brunei Darussalam
  5. Central African Republic
  6. The Democratic Republic of Congo
  7. Eritrea
  8. Guinea-bissau
  9. Iran
  10. Lebanon
  11. Libiya Arab Jamahriya
  12. Somalia
  13. Sudan
  14. Russian Federation
  15. Syrian Arab Republic
  16. Ukraine
  17. Venezuela