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Embedded Deep Learning

This book covers algorithmic and hardware implementation techniques to enable embedded deep learning. The authors describe synergetic;

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Deep Learning on Edge Computing Devices

Deep Learning on Edge Computing Devices: Design Challenges of Algorithm and Architecture focuses on hardware architecture and embedded deep;

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Hardware Architectures for Deep Learning

neural networks. The rapid growth of server, desktop, and embedded applications based on deep learning has brought about a;

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Embedded Computer Systems: Architectures, Modeling, and Simulation

This book constitutes the refereed proceedings of the 19th International Conference on Embedded Computer Systems: Architectures, Modeling;

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Tinyml Machine Learning with Tensorflow Lite on Arduino and UltraLowPower Microcontrollers

--small enough to run on a microcontroller. With this practical book you'll enter the field of TinyML, where deep learning and embedded systems;

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Job-Embedded Professional Development

is written for teachers, teacher leaders, and school and district leaders who want to support job-embedded learning, a powerful form of professional;

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Job-Embedded Professional Development

is written for teachers, teacher leaders, and school and district leaders who want to support job-embedded learning, a powerful form of professional;

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Conference Proceedings of ICDLAIR2019

algorithm, reinforcement learning, natural language processing, social computing, user customization, embedded computation, automotive design and;

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International Conference on Communication Computing and Electronics Systems

, held at the PPG Institute of Technology, Coimbatore, India, on 21-22 October 2020. The book covers topics such as automation, VLSI, embedded;

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Deep Learning and Practice with MindSpore

This book systematically introduces readers to the theory of deep learning and explores its practical applications based on the MindSpore;

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Deep Learning and Practice with MindSpore

This book systematically introduces readers to the theory of deep learning and explores its practical applications based on the MindSpore;

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Practical MATLAB Deep Learning

Harness the power of MATLAB for deep-learning challenges. Practical MATLAB Deep Learning, Second Edition, remains a one-of a-kind book that;

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Practical MATLAB Deep Learning

Harness the power of MATLAB for deep-learning challenges. Practical MATLAB Deep Learning, Second Edition, remains a one-of a-kind book that;

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Artificial Intelligence for IoT Cookbook

, NLP, and embedded machine learning Build, maintain, and deploy machine learning systems to extract key insights from IoT data Book;

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R Deep Learning Cookbook

Powerful, independent recipes to build deep learning models in different application areas using R libraries About This Book * Master;

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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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Deep Learning for Data Analytics

Deep learning, a branch of Artificial Intelligence and machine learning, has led to new approaches to solving problems in a variety;

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Deep Reinforcement Learning with Python

Deep reinforcement learning is a fast-growing discipline that is making a significant impact in fields of autonomous vehicles, robotics;

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Concepts and Real-Time Applications of Deep Learning

This book provides readers with a comprehensive and recent exposition in deep learning and its multidisciplinary applications, with a;

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Pro Deep Learning with TensorFlow

Deploy deep learning solutions in production with ease using TensorFlow. You'll also develop the mathematical understanding and intuition;

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Hands-On Deep Learning with Apache Spark

Speed up the design and implementation of deep learning solutions using Apache Spark Key Features Explore the world of distributed deep;

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Advanced Deep Learning for Engineers and Scientists

This book provides a complete illustration of deep learning concepts with case-studies and practical examples useful for real time;

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Deep Learning with Structured Data

identifying cats in YouTube videos. Powerful deep learning techniques can efficiently extract insight from the kind of structured data collected by;

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Learn Keras for Deep Neural Networks

on an end-to-end approach to developing supervised learning algorithms in regression and classification with practical business-centric use;

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R Deep Learning Essentials

Implement neural network models in R 3.5 using TensorFlow, Keras, and MXNet Key Features Use R 3.5 for building deep learning models for;

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Advanced Deep Learning with R

Discover best practices for choosing, building, training, and improving deep learning models using Keras-R, and TensorFlow-R libraries Key;

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Facilitating Deep Learning

Deep learning is a committed approach to learning. It is a process of constructing and interpreting new knowledge in light of prior;

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