Part 2 Practical deep learning applications
Part 2 is structured differently than part 1; it’s almost a book within a book. Part 2 focuses on practical applications of deep learning, spanning chapters 9 through 17. This section moves beyond foundational concepts to explore how deep learning is used in real-world scenarios, covering a range of tasks and domains.
Chapter 9 introduces text generation with modern language models, providing hands-on experience with generative AI for natural language. Chapter 10 shifts to image generation, exploring an entirely different architecture of models that can create visual content. Chapters 11 through 15 form an extended project on medical image analysis, guiding you through the process of building, training, and evaluating models for early detection of lung cancer using real medical imaging data. These chapters cover data preparation, model development, segmentation and classification, handling imbalanced data, and integrating models into a complete diagnostic pipeline.
Finally, chapters 16 and 17 address advanced topics in scaling and deploying deep learning solutions. Chapter 16 covers distributed training, enabling you to use multiple GPUs or machines for large-scale experiments. Chapter 17 focuses on deployment strategies, including model optimization, quantization, and serving models in production environments.