Microsoft Library

Harness the power of deep learning to drive productivity and efficiency using this practical guide covering techniques and best practices for the entire deep learning life cycle

Key Features

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

Book DescriptionDeep 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.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

Who this book is 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.

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The Deep Learning Architect's Handbook

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