Smart Python for Machine Learning and Intelligent Systems

Smart Python for Machine Learning and Intelligent Systems: Deep Learning, Transfer Learning, and AI Engineering Part 2 extends the foundations established in Part 1 by introducing the modern techniques that drive today's intelligent systems. The book provides a practical, implementation-oriented approach to deep learning with Python, covering neural networks, convolutional neural networks (CNNs), recurrent neural networks (RNNs), LSTMs, GRUs, and generative adversarial networks (GANs). It also explores transfer learning through feature reuse, fine-tuning, and domain adaptation, followed by advanced deep learning architectures including ResNet and other state-of-the-art models. The final chapters focus on AI engineering, model optimization, deployment, inference benchmarking, pruning, quantization, and the development of efficient production-ready intelligent systems. Throughout the book, theoretical concepts are reinforced with complete Python implementations, practical experiments, performance evaluation, and real-world case studies. Together with Part 1, this volume provides a comprehensive guide to modern machine learning, deep learning, and AI engineering using Python.