What this book covers
Chapter 1, Deep Learning Life Cycle, introduces the key stages of a deep learning project, focusing on planning and data preparation, and sets the stage for a comprehensive exploration of the deep learning life cycle throughout the book.
Chapter 2, Designing Deep Learning Architectures, dives into the foundational aspects of deep learning architectures, including MLPs, and discusses their role in advanced neural networks, as well as the importance of backpropagation and regularization.
Chapter 3, Understanding Convolutional Neural Networks, provides an in-depth look at CNNs, their applications in image processing, and various model families within the CNN domain.
Chapter 4, Understanding Recurrent Neural Networks, explores the structure and variations of RNNs and their ability to process sequential data effectively.
Chapter 5, Understanding Autoencoders, examines the fundamentals of autoencoders as a method for representation learning and their applications across different data modalities.
Chapter 6, Understanding Neural Network Transformers, delves into the versatile nature of transformers, capable of handling diverse data modalities without explicit data-specific biases, and their potential applications in various tasks and domains.
Chapter 7, Deep Neural Architecture Search, introduces the concept of NAS as a way to automate the design of advanced neural networks and discusses its applications and limitations in different scenarios.
Chapter 8, Exploring Supervised Deep Learning, covers various supervised learning problem types, techniques for implementing and training deep learning models, and practical implementations using popular deep learning frameworks.
Chapter 9, Exploring Unsupervised Deep Learning, discusses the contributions of deep learning to unsupervised learning, particularly highlighting the unsupervised pre-training method. Harnessing the vast amounts of freely available data on the internet, this approach improves model performance for downstream supervised tasks and paves the way toward general Artificial Intelligence (AI).
Chapter 10, Exploring Model Evaluation Methods, provides an overview of model evaluation techniques, metric engineering, and strategies for optimizing against evaluation metrics.
Chapter 11, Explaining Neural Network Predictions, delves into the prediction explanation landscape, focusing on the integrated gradients technique and its practical applications for understanding neural network predictions.
Chapter 12, Interpreting Neural Networks, delves into the nuances of model understanding and showcases techniques for uncovering patterns detected by neurons. By exploring real images and generating images through optimization to activate specific neurons, you will gain valuable insights into the neural network’s decision-making process.
Chapter 13, Exploring Bias and Fairness, addresses the critical issue of bias and fairness in machine learning models, discussing various types, metrics, and programmatic methods for detecting and mitigating bias.
Chapter 14, Analyzing Adversarial Performance, examines the importance of adversarial performance analysis in identifying vulnerabilities and weaknesses in machine learning models, along with practical examples and techniques for analysis.
Chapter 15, Deploying Deep Learning Models in Production, focuses on key components, requirements, and strategies for deploying deep learning models in production environments, including architectural choices, hardware infrastructure, and model packaging.
Chapter 16, Governing Deep Learning Models, explores the fundamental pillars of model governance, including model utilization, model monitoring, and model maintenance, while providing practical steps for monitoring deep learning models.
Chapter 17, Managing Drift Effectively in a Dynamic Environment, discusses the concept of drift and its impact on model performance, along with strategies for detecting, quantifying, and mitigating drift in deep learning models.
Chapter 18, Exploring the DataRobot AI Platform, showcases the benefits of AI platforms, specifically DataRobot, in streamlining and accelerating the deep learning life cycle, and highlights various features and capabilities of the platform.
Chapter 19, Architecting LLM Solutions, delves into LLMs and the potential applications, challenges, and strategies for creating effective, contextually aware solutions using LLMs.
