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and apply methods successfully but to think critically about it. Modern Deep Learning Design and Methods is ideal for readers looking to;
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This book presents a wealth of deep-learning algorithms and demonstrates their design process. It also highlights the need for a prudent;
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design approaches on the application-, algorithmic-, computer architecture-, and circuit-level that will help in achieving the goal of reducing;
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This book presents a wealth of deep-learning algorithms and demonstrates their design process. It also highlights the need for a prudent;
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discusses the development and application of computer-aided detection and diagnosis (CAD) systems in medical imaging. Given its coverage;
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This book offers a timely reflection on the remarkable range of algorithms and applications that have made the area of deep learning so;
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This book offers a timely reflection on the remarkable range of algorithms and applications that have made the area of deep learning so;
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Thinking Machines: Machine Learning and Its Hardware Implementation covers the theory and application of machine learning, neuromorphic;
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Deep Learning provides an overview of general deep learning methodology and its applications to a variety of signal and information;
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that are relevant to modular application development * Write manageable code based on the GoF design patterns and SOLID principles * Define;
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From the component design, to the subsystem design, to the engine systems design, engine development and flight-vehicle application, this;
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Deep Network Design for Medical Image Computing: Principles and Applications covers a range of MIC tasks and discusses design;
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other applications by presenting neural network algorithms and hardware design optimization approaches for Edge-deep learning. Applications are;
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This exciting new resource presents emerging applications of artificial intelligence and deep learning in short-range radar. The book;
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architectures to solve specific tasks in different application fields. On the other hand, the authors want to target the machine learning and computer;
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architectures to solve specific tasks in different application fields. On the other hand, the authors want to target the machine learning and computer;
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. What You Will Learn * Build deep learning models in different application areas using TensorFlow, H2O, and MXnet. * Analyzing a Deep;
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Trends in Deep Learning Methodologies: Algorithms, Applications, and Systems covers deep learning approaches such as neural networks, deep;
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. The book reveals the application of pedagogical models to the design of e-learning environments by discussing theories and showing actual;
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analytic tools to guide the design and interpretation of deep learning models. The development of the theory and models is supported by a wide;
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) Common Pitfalls Application Security Programs Securing Modern Applications Software Developer Security Hygiene Alice and Bob Learn;
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Design, develop, and deploy a real-world web application by leveraging modern open source technologies. This book shows you how to use;
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engineering and teardowns offer a new better paradigm for design instruction, permitting a modern learning cycle of experience, hypothesis;
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education. Deep learning refers to learners engagement in critical and creative thinking, making inferences and transferring knowledge. Modern;
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applications using intelligent programming. MatConvNet Deep Learning and iOS Mobile App Design for Pattern Recognition: Emerging Research and;
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applications using intelligent programming. MatConvNet Deep Learning and iOS Mobile App Design for Pattern Recognition: Emerging Research and;
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Learning architectures are being increasingly used in day to day applications where traditional machine learning and deep learning algorithms were;
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