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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

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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 eBook?

Instant access to your Digital eBook purchase

Download this book in EPUB and PDF formats

Access this title in our online reader with advanced features

DRM FREE - Read whenever, wherever and however you want

AI Assistant (beta) to help accelerate your learning

Frequently bought together

Causal Inference and Discovery in Python

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Total £ 118.97

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.

Amazon Verified review

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!

Amazon Verified review

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!

Amazon Verified review

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

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