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Machine Learning Vol 3 (AI and ML Reference handbooks) - Softcover
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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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Paperback. Zustand: new. Paperback. Machine Learning Volume 3: Deep Learning, Generative AI, and Production Machine LearningModern 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 fundamentalsForward and BackpropagationGradient Descent optimizationActivation functionsLoss functionsWeight initializationRegularization techniquesConvolutional Neural Networks (CNN)Recurrent Neural Networks (RNN)LSTM and GRU architecturesSequence modelingAttention mechanismsTransformer architectureLarge Language Models (LLMs)Transfer LearningSelf-Supervised LearningFoundation ModelsDiffusion ModelsGenerative AI fundamentalsVision Transformers (ViT)Autoencoders and Variational Autoencoders (VAE)Generative Adversarial Networks (GANs)Embeddings and vector representationsModel compression and quantizationDistributed trainingGPU accelerationProduction machine learning pipelinesModel deployment strategiesMLOps fundamentalsModel monitoring and drift detectionExplainable AI (XAI)Responsible AI principlesAI system reliability and scalabilityFuture trends in machine learningEvery 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. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability. Bestandsnummer des Verkäufers 9798187857371
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