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Hardware Architectures for Deep Learning

performance-efficiency. Hardware Architectures for Deep Learning provides an overview of this new field, from principles to;

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Hardware Accelerator Systems for Artificial Intelligence and Machine Learning

Machine Learning, Deep Learning with GPUs, Edge Computing Optimization of Deep Learning Models for Specialized Tensor Processing Architectures;

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Deep In-memory Architectures for Machine Learning

This book describes the recent innovation of deep in-memory architectures for realizing AI systems that operate at the edge of energy;

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Deep In-memory Architectures for Machine Learning

This book describes the recent innovation of deep in-memory architectures for realizing AI systems that operate at the edge of energy;

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Computer Vision Metrics

-of-the-art survey for fundamental computer vision methods and deep learning. With over 800 essential references, as well as chapter-by-chapter;

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Thinking Machines

computing and neural networks. This is the first book that focuses on machine learning accelerators and hardware development for machine learning;

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Embedded Deep Learning

the computational cost of deep learning algorithms. The impact of these techniques is displayed in four silicon prototypes for embedded deep;

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Deep Learning for Computer Architects

This is a primer written for computer architects in the new and rapidly evolving field of deep learning. It reviews how machine learning;

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Deep Learning on Edge Computing Devices

learning, including neural networks. The title helps researchers maximize the performance of Edge-deep learning models for mobile computing and;

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Deep Learning with PyTorch

CNN architectures such as ResNet, Inception, Densenet, and learn how to use them for transfer learning Learn how to mix multiple models for a;

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Caffe2 Quick Start Guide

with other deep learning frameworks on Caffe2 Integrate Caffe2 with Android or iOS and implement deep learning models for mobile devices;

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Pro Deep Learning with TensorFlow

required to invent new deep learning architectures and solutions on your own. Pro Deep Learning with TensorFlow provides practical, hands-on;

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Handbook of Deep Learning in Biomedical Engineering

composite information. Handbook for Deep Learning in Biomedical Engineering: Techniques and Applications gives readers a complete overview of the;

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Concepts and Real-Time Applications of Deep Learning

concentration on advances of deep learning architectures. The book discusses various artificial intelligence (AI) techniques based on deep learning;

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Advanced Deep Learning for Engineers and Scientists

applications. This book introduces a broad range of topics in deep learning. The authors start with the fundamentals, architectures, tools needed for;

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Emerging Deep Learning Applications of Short Range Radars

neural network (DCNN), popular DCNN architectures for computer vision and their features are also introduced. The book presents other deep;

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Deep Learning for Biometrics

This timely text/reference presents a broad overview of advanced deep learning architectures for learning effective feature representation;

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Digital Health in Focus of Predictive, Preventive and Personalised Medicine

* Wearable & connected devices * Communication infrastructures, architectures and standards Blockchain for e-Health;

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Hands-On One-shot Learning with Python

few-shot learning Work with different deep learning architectures for one-shot learning Understand when to use one-shot and transfer learning;

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Mastering PyTorch

neural network (RNN) architectures and transformers for sentiment analysis. As you advance, you'll apply deep learning across different domains;

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Applied Deep Learning and Computer Vision for Self-Driving Cars

This book teaches you the different techniques and methodologies associated while implementing deep learning solutions in self-driving cars;

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Deep Learning Applications

solve real-world problems, and also offers insights into deep learning architectures and algorithms, making it an essential reference guide for;

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Deep Learning Applications Volume 2

. Also offering insights into deep learning architectures and algorithms, it is an essential reference guide for academic researchers;

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Advanced Deep Learning with R

help you understand popular deep learning architectures and their variants in R, along with providing real-life examples for them. This deep;

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Deep Learning: Concepts and Architectures

This book introduces readers to the fundamental concepts of deep learning and offers practical insights into how this learning paradigm;

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ReRAM-based Machine Learning

such large amounts of data sets has led to the adoption of machine learning (ML) and deep learning (DL) methods in a wide range of applications;

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VLSI Architectures for Modern Error-Correcting Codes

high complexity. VLSI Architectures for Modern Error-Correcting Codes serves as a bridge connecting advancements in coding theory to;

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