Machine Learning Vol 3 (AI and ML Reference handbooks) - Softcover

Inhaltsangabe

Machine Learning Volume 3: Deep Learning, Generative AI, and Production Machine Learning

Modern Artificial Intelligence is powered by deep learning. From computer vision and natural language processing to generative AI and autonomous systems, today's intelligent applications rely on neural networks capable of learning from massive amounts of data.

Machine Learning Volume 3: Deep Learning, Generative AI, and Production Machine Learning is the culmination of the Machine Learning series, taking readers beyond classical algorithms into the technologies driving the latest AI revolution.

Designed for AI engineers, machine learning practitioners, software developers, data scientists, researchers, and students, this volume combines theoretical foundations with practical engineering guidance for building, deploying, and maintaining real-world AI systems.

Inside this volume, you'll explore:

• Artificial Neural Networks (ANN)
• Deep Learning fundamentals
• Forward and Backpropagation
• Gradient Descent optimization
• Activation functions
• Loss functions
• Weight initialization
• Regularization techniques
• Convolutional Neural Networks (CNN)
• Recurrent Neural Networks (RNN)
• LSTM and GRU architectures
• Sequence modeling
• Attention mechanisms
• Transformer architecture
• Large Language Models (LLMs)
• Transfer Learning
• Self-Supervised Learning
• Foundation Models
• Diffusion Models
• Generative AI fundamentals
• Vision Transformers (ViT)
• Autoencoders and Variational Autoencoders (VAE)
• Generative Adversarial Networks (GANs)
• Embeddings and vector representations
• Model compression and quantization
• Distributed training
• GPU acceleration
• Production machine learning pipelines
• Model deployment strategies
• MLOps fundamentals
• Model monitoring and drift detection
• Explainable AI (XAI)
• Responsible AI principles
• AI system reliability and scalability
• Future trends in machine learning

Every chapter combines practical workflows, architecture diagrams, comparison tables, mathematical intuition, implementation guidance, and engineering best practices to help readers understand not only how modern AI models work, but also how they are deployed and maintained in production environments.

Whether you're building intelligent applications, exploring Generative AI, deploying production-scale machine learning systems, or preparing for advanced AI engineering roles, this volume provides a comprehensive technical reference that bridges research concepts with real-world implementation.

Machine Learning Volume 3 completes the AI/ML Reference Series, offering a complete progression from foundational machine learning concepts to advanced deep learning, Generative AI, and production-ready intelligent systems.

Master modern AI. Build scalable machine learning systems. Engineer the future with confidence.

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