English | 2023 | ISBN: 1803243791 | 516 pages | PDF
The Deep Learning Architect’s Handbook: Build and deploy production-ready DL solutions leveraging the latest Python techniques
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
Book Description
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.
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. Working knowledge of Python programming and a basic understanding of deep learning techniques is needed to get started with this book.
