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Paperback. Zustand: New. Take a hands-on approach to understanding deep learning and build smart applications that can recognize images and interpret textKey FeaturesUnderstand how to implement deep learning with TensorFlow and KerasLearn the fundamentals of computer vision and image recognitionStudy the architecture of different neural networksBook DescriptionAre you fascinated by how deep learning powers intelligent applications such as self-driving cars, virtual assistants, facial recognition devices, and chatbots to process data and solve complex problems? Whether you are familiar with machine learning or are new to this domain, The Deep Learning Workshop will make it easy for you to understand deep learning with the help of interesting examples and exercises throughout.The book starts by highlighting the relationship between deep learning, machine learning, and artificial intelligence and helps you get comfortable with the TensorFlow 2.0 programming structure using hands-on exercises. You'll understand neural networks, the structure of a perceptron, and how to use TensorFlow to create and train models. The book will then let you explore the fundamentals of computer vision by performing image recognition exercises with convolutional neural networks (CNNs) using Keras. As you advance, you'll be able to make your model more powerful by implementing text embedding and sequencing the data using popular deep learning solutions. Finally, you'll get to grips with bidirectional recurrent neural networks (RNNs) and build generative adversarial networks (GANs) for image synthesis.By the end of this deep learning book, you'll have learned the skills essential for building deep learning models with TensorFlow and Keras.What you will learnUnderstand how deep learning, machine learning, and artificial intelligence are differentDevelop multilayer deep neural networks with TensorFlowImplement deep neural networks for multiclass classification using KerasTrain CNN models for image recognitionHandle sequence data and use it in conjunction with RNNsBuild a GAN to generate high-quality synthesized imagesWho this book is forIf you are interested in machine learning and want to create and train deep learning models using TensorFlow and Keras, this workshop is for you. A solid understanding of Python and its packages, along with basic machine learning concepts, will help you to learn the topics quickly.…

Paperback. Zustand: New.

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PAP. Zustand: New. New Book. Shipped from UK. Established seller since 2000.

Zustand: New. In English.

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PAP. Zustand: New. New Book. Shipped from UK. Established seller since 2000.

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Paperback. Zustand: New. This book explores how modern technologies-especially data analytics, machine learning (ML), and the Internet of Things (IoT)-are transforming supply chain and manufacturing operations. Bridging academic research and industrial practice, this book presents data as a strategic asset driving agility, efficiency, and resilience.Structured around four themes, it covers:Foundational analytics and optimizationPredictive and prescriptive analytics for proactive decision-makingReal-time IoT data for workflow monitoring and controlDigital Twins and Natural Language Processing (NLP) for modeling and interactionChapters include mathematical modeling, case studies, and implementation frameworks, with coverage spanning stochastic forecasting, reinforcement learning, anomaly detection, and semantic parsing of logistics documentation.Key benefits include its emphasis on integrated intelligence-blending ML, IoT, and simulation for real-time, predictive insights. It also highlights scalability across industries, with tools adaptable to sectors like automotive, healthcare, and aerospace. Each chapter concludes with open problems and future directions, offering a roadmap for innovation in intelligent operations.…

Paperback. Zustand: New. This book explores how modern technologies-especially data analytics, machine learning (ML), and the Internet of Things (IoT)-are transforming supply chain and manufacturing operations. Bridging academic research and industrial practice, this book presents data as a strategic asset driving agility, efficiency, and resilience.Structured around four themes, it covers:Foundational analytics and optimizationPredictive and prescriptive analytics for proactive decision-makingReal-time IoT data for workflow monitoring and controlDigital Twins and Natural Language Processing (NLP) for modeling and interactionChapters include mathematical modeling, case studies, and implementation frameworks, with coverage spanning stochastic forecasting, reinforcement learning, anomaly detection, and semantic parsing of logistics documentation.Key benefits include its emphasis on integrated intelligence-blending ML, IoT, and simulation for real-time, predictive insights. It also highlights scalability across industries, with tools adaptable to sectors like automotive, healthcare, and aerospace. Each chapter concludes with open problems and future directions, offering a roadmap for innovation in intelligent operations.…

Zustand: New. In English.

Zustand: New.

PAP. Zustand: New. New Book. Shipped from UK. Established seller since 2000.

PAP. Zustand: New. New Book. Shipped from UK. Established seller since 2000.

PAP. Zustand: New. New Book. Shipped from UK. Established seller since 2000.

Zustand: New.

Paperback. Zustand: Brand New. 10.00x7.00 inches. In Stock.

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Zustand: New.

Zustand: New.

PAP. Zustand: New. New Book. Shipped from UK. Established seller since 2000.

PAP. Zustand: New. New Book. Shipped from UK. Established seller since 2000.

PAP. Zustand: New. New Book. Shipped from UK. Established seller since 2000.

Zustand: NEW.

Taschenbuch. Zustand: Neu. Neuware - This book explores how modern technologies-especially data analytics, machine learning (ML), and the Internet of Things (IoT)-are transforming supply chain and manufacturing operations. Bridging academic research and industrial practice, this book presents data as a strategic asset driving agility, efficiency, and resilience.Structured around four themes, it covers: - Foundational analytics and optimization - Predictive and prescriptive analytics for proactive decision¿making - Real¿time IoT data for workflow monitoring and control - Digital Twins and Natural Language Processing (NLP) for modeling and interaction Chapters include mathematical modeling, case studies, and implementation frameworks, with coverage spanning stochastic forecasting, reinforcement learning, anomaly detection, and semantic parsing of logistics documentation.Key benefits include its emphasis on integrated intelligence-blending ML, IoT, and simulation for real¿time, predictive insights. It also highlights scalability across industries, with tools adaptable to sectors like automotive, healthcare, and aerospace. Each chapter concludes with open problems and future directions, offering a roadmap for innovation in intelligent operations.…