What do you get with a Packt Subscription?

Free for first 7 days. $15.99 p/m after that. Cancel any time!

Unlimited ad-free access to the largest independent learning library in tech. Access this title and thousands more!

50+ new titles added per month, including many first-to-market concepts and exclusive early access to books as they are being written.

Innovative learning tools, including AI book assistants, code context explainers, and text-to-speech.

Thousands of reference materials covering every tech concept you need to stay up to date.

Subscribe now

View plans & pricing

Key benefits

Interpret your models’ decision-making process, ensuring transparency and trust in your AI-powered solutions

Gain hands-on experience in every step of the deep learning life cycle

Explore case studies and solutions for deploying DL models while addressing scalability, data drift, and ethical considerations

Purchase of the print or Kindle book includes a free PDF eBook

Description

Deep learning enables previously unattainable feats in automation, but extracting real-world business value from it is a daunting task. This book will teach you how to build complex deep learning models and gain intuition for structuring your data to accomplish your deep learning objectives. This deep learning book explores every aspect of the deep learning life cycle, from planning and data preparation to model deployment and governance, using real-world scenarios that will take you through creating, deploying, and managing advanced solutions. You’ll also learn how to work with image, audio, text, and video data using deep learning architectures, as well as optimize and evaluate your deep learning models objectively to address issues such as bias, fairness, adversarial attacks, and model transparency. As you progress, you’ll harness the power of AI platforms to streamline the deep learning life cycle and leverage Python libraries and frameworks such as PyTorch, ONNX, Catalyst, MLFlow, Captum, Nvidia Triton, Prometheus, and Grafana to execute efficient deep learning architectures, optimize model performance, and streamline the deployment processes. You’ll also discover the transformative potential of large language models (LLMs) for a wide array of applications. By the end of this book, you'll have mastered deep learning techniques to unlock its full potential for your endeavors.

Who is this book for?

This book is for deep learning practitioners, data scientists, and machine learning developers who want to explore deep learning architectures to solve complex business problems. Professionals in the broader deep learning and AI space will also benefit from the insights provided, applicable across a variety of business use cases. Working knowledge of Python programming and a basic understanding of deep learning techniques is needed to get started with this book.

What you will learn

Use neural architecture search (NAS) to automate the design of artificial neural networks (ANNs)

Implement recurrent neural networks (RNNs), convolutional neural networks (CNNs), BERT, transformers, and more to build your model

Deal with multi-modal data drift in a production environment

Evaluate the quality and bias of your models

Explore techniques to protect your model from adversarial attacks

Get to grips with deploying a model with DataRobot AutoML

What do you get with a Packt Subscription?

Free for first 7 days. $15.99 p/m after that. Cancel any time!

Unlimited ad-free access to the largest independent learning library in tech. Access this title and thousands more!

50+ new titles added per month, including many first-to-market concepts and exclusive early access to books as they are being written.

Innovative learning tools, including AI book assistants, code context explainers, and text-to-speech.

Thousands of reference materials covering every tech concept you need to stay up to date.

Subscribe now

View plans & pricing

Frequently bought together

Causal Inference and Discovery in Python

$53.99

$49.99

$52.99

Total $ 156.97

FAQs

What is included in a Packt subscription?

A subscription provides you with full access to view all Packt and licnesed content online, this includes exclusive access to Early Access titles. Depending on the tier chosen you can also earn credits and discounts to use for owning content

How can I cancel my subscription?

To cancel your subscription with us simply go to the account page - found in the top right of the page or at https://subscription.packtpub.com/my-account/subscription - From here you will see the ‘cancel subscription’ button in the grey box with your subscription information in.

What are credits?

Credits can be earned from reading 40 section of any title within the payment cycle - a month starting from the day of subscription payment. You also earn a Credit every month if you subscribe to our annual or 18 month plans. Credits can be used to buy books DRM free, the same way that you would pay for a book. Your credits can be found in the subscription homepage - subscription.packtpub.com - clicking on ‘the my’ library dropdown and selecting ‘credits’.

What happens if an Early Access Course is cancelled?

Projects are rarely cancelled, but sometimes it's unavoidable. If an Early Access course is cancelled or excessively delayed, you can exchange your purchase for another course. For further details, please contact us here.

Where can I send feedback about an Early Access title?

If you have any feedback about the product you're reading, or Early Access in general, then please fill out a contact form here and we'll make sure the feedback gets to the right team.

Can I download the code files for Early Access titles?

We try to ensure that all books in Early Access have code available to use, download, and fork on GitHub. This helps us be more agile in the development of the book, and helps keep the often changing code base of new versions and new technologies as up to date as possible. Unfortunately, however, there will be rare cases when it is not possible for us to have downloadable code samples available until publication.

When we publish the book, the code files will also be available to download from the Packt website.

How accurate is the publication date?

The publication date is as accurate as we can be at any point in the project. Unfortunately, delays can happen. Often those delays are out of our control, such as changes to the technology code base or delays in the tech release. We do our best to give you an accurate estimate of the publication date at any given time, and as more chapters are delivered, the more accurate the delivery date will become.

How will I know when new chapters are ready?

We'll let you know every time there has been an update to a course that you've bought in Early Access. You'll get an email to let you know there has been a new chapter, or a change to a previous chapter. The new chapters are automatically added to your account, so you can also check back there any time you're ready and download or read them online.

I am a Packt subscriber, do I get Early Access?

Yes, all Early Access content is fully available through your subscription. You will need to have a paid for or active trial subscription in order to access all titles.

How is Early Access delivered?

Early Access is currently only available as a PDF or through our online reader. As we make changes or add new chapters, the files in your Packt account will be updated so you can download them again or view them online immediately.

How do I buy Early Access content?

Early Access is a way of us getting our content to you quicker, but the method of buying the Early Access course is still the same. Just find the course you want to buy, go through the check-out steps, and you’ll get a confirmation email from us with information and a link to the relevant Early Access courses.

What is Early Access?

Keeping up to date with the latest technology is difficult; new versions, new frameworks, new techniques. This feature gives you a head-start to our content, as it's being created. With Early Access you'll receive each chapter as it's written, and get regular updates throughout the product's development, as well as the final course as soon as it's ready.We created Early Access as a means of giving you the information you need, as soon as it's available. As we go through the process of developing a course, 99% of it can be ready but we can't publish until that last 1% falls in to place. Early Access helps to unlock the potential of our content early, to help you start your learning when you need it most. You not only get access to every chapter as it's delivered, edited, and updated, but you'll also get the finalized, DRM-free product to download in any format you want when it's published. As a member of Packt, you'll also be eligible for our exclusive offers, including a free course every day, and discounts on new and popular titles.

Filter reviews by

All

Feefo verified reviews

Amazon verified reviews

Pat Mthisi Feb 15, 2024

The books are of top quality and easy to use. They contain practical examples, and I would recommend them to anyone wanting to dive into AI.

Feefo Verified review

H2N Feb 13, 2024

This is a great deep learning book for data scientists and machine learning engineers. The book introduces how to solve complex business issues with deep learning techniques. The author discusses the fundamentals of the deep learning to advanced topics like CNNs, RNNs, Autoencoders, and Transformers using Python with neural architecture design, evaluation, bias and fairness, and deploying models, offering practical insights for leveraging AI platforms like DataRobot.

Amazon Verified review

tt0507 Feb 21, 2024

The book explores the intricacies of neural networks and machine learning, covering foundational concepts, advanced techniques, and practical applications. It provides a modern guide to model building and deployment, emphasizing key architectures such as convolutional neural networks, recurrent neural networks, autoencoders, and Transformer architecture.The book also addresses model interpretation, evaluation, bias, fairness, and adversarial performance. With practical insights into deployment (production environments, governance, drift management, and large language models), this guide is also helpful for deep learning practitioners, data scientists, and machine learning engineers whose roles include DLOps (MLOps for deep learning) tasks.Overall, the book offers a holistic view of theoretical concepts, methodologies, and emerging trends, making it a valuable resource for both novice learners and experienced practitioners if deep learning. Python libraries like PyTorch are highlighted for streamlining the deep learning process and optimizing model performance. The accompanying GitHub repository provides helpful code examples, reinforcing the practical application of concepts throughout the book. I highly recommend the book to anyone looking to deepen their understanding of neural networks and gain insight into deploying deep learning models.

Didi Feb 19, 2024

Deep learning (DL)—a subset of machine learning that utilizes deep neural networks—has taken the world by storm and made numerous significant breakthroughs throughout the last decade. These breakthroughs have revolutionized entire fields such as computer vision and natural language processing. This comprehensive book is a modern guide to DL model building and deployment, and serves as a unique and practical resource for understanding modern DL architectures, model training, and real-world deployment from the ground up.The book begins with a clear and detailed overview of foundational DL architectures, such as convolutional neural networks (NNs), recurrent NNs, autoencoders, and the Transformer architecture. The second part of the book focuses on interpreting and extracting insights from DL models, and covers model evaluation techniques, interpreting model predictions, exploring bias and fairness, and analyzing adversarial performance. The last part of the book is focused on various practical aspects of real-world model deployment (aka DLOps), including deployment in production environments, governance, drift management, and even the architecture of LLMs (large language models). The helpful code examples and diagrams that accompany the textual descriptions greatly assist in reinforcing the materials and concepts presented in the book. The accompanying GitHub repository includes all code examples, and is very useful as well.This practical guide will benefit any DL practitioner, researcher, data scientist or machine learning practitioner who wants to better understand how to build and deploy real-world DL models. Prior familiarity with DL and Python will be very helpful to fully benefit from this book.Highly recommended!

Vikram Chaudhary Feb 29, 2024

My background is an executive-level product leader at Meta's AI Infrastructure organization, and I was impressed and grateful to read this book. It is an amazing resource for learning all the different aspects of AI, especially Deep Learning techniques like Supervised Learning, Neural Networks, and all the flavors of these and other areas of AI. The book is simultaneously at an introductory level if you are technically aware, then business-relevant because it talks about the different use cases. It is also deep enough for architects looking to understand how the technology works, and finally, you have Python examples and sample code to accelerate your engineering learning. This should be your Deep Learning bible!

The Deep Learning Architect's Handbook: Build and deploy production-ready DL solutions leveraging the latest Python techniques

What do you get with a Packt Subscription?

Key benefits

Description

Who is this book for?

What you will learn

Product Details

What do you get with a Packt Subscription?

Product Details

Frequently bought together

Recommendations for you

Customer reviews

Filter reviews by

People who bought this also bought

About the author

FAQs

The Deep Learning Architect's Handbook: Build and deploy production-ready DL solutions leveraging the latest Python techniques

What do you get with a Packt Subscription?

Key benefits

Description

Who is this book for?

What you will learn

Product Details

What do you get with a Packt Subscription?

Product Details

Packt Subscriptions

Frequently bought together

Recommendations for you

Customer reviews

Filter reviews by

People who bought this also bought

About the author

FAQs