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Book Overview & Buying
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Table Of Contents
The Deep Learning Architect's Handbook
By :
The Deep Learning Architect's Handbook
By:
Overview of this book
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.
Table of Contents (25 chapters)
Preface
Part 1 – Foundational Methods
Free Chapter
Chapter 1: Deep Learning Life Cycle
Chapter 2: Designing Deep Learning Architectures
Chapter 3: Understanding Convolutional Neural Networks
Chapter 4: Understanding Recurrent Neural Networks
Chapter 5: Understanding Autoencoders
Chapter 6: Understanding Neural Network Transformers
Chapter 7: Deep Neural Architecture Search
Chapter 8: Exploring Supervised Deep Learning
Chapter 9: Exploring Unsupervised Deep Learning
Part 2 – Multimodal Model Insights
Chapter 10: Exploring Model Evaluation Methods
Chapter 11: Explaining Neural Network Predictions
Chapter 12: Interpreting Neural Networks
Chapter 13: Exploring Bias and Fairness
Chapter 14: Analyzing Adversarial Performance
Part 3 – DLOps
Chapter 15: Deploying Deep Learning Models to Production
Chapter 16: Governing Deep Learning Models
Chapter 17: Managing Drift Effectively in a Dynamic Environment
Chapter 18: Exploring the DataRobot AI Platform
Chapter 19: Architecting LLM Solutions
Index
