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Deep Learning Applications with Practical Measured Results in Electronics Industries

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Examining the Impact of Deep Learning and IoT on Multi-Industry Applications

technologies in various industries, such as education, health, transportation, and security, combine IoT with deep learning for complex problem solving;

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Probabilistic Deep Learning

authors have been working with deep learning methods since 2013 and have extensive experience in both teaching the topic and developing;

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Machine Learning in Industry

industries aspiring to use machine learning for solving practical industrial problems.;

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Deep Learning Illustrated

techniques and applications of deep learning. Packed with vibrant, full-color illustrations, it abstracts away much of the complexity of building;

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Renewable Energy and Future Power Systems

applications in power electronics, deep learning applications in power systems, design and simulation of multilevel inverters, solid state transformers;

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Conference Proceedings of ICDLAIR2019

includes all subareas of AI, with broad coverage of traditional topics like robotics, statistical learning and deep learning techniques. However;

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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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Reliability in Power Electronics and Electrical Machines

In modern industries, electrical energy conversion systems consist of two main parts: electrical machines and power electronic converters;

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Python Deep Learning

. What You Will Learn * Get a practical deep dive into deep learning algorithms * Explore deep learning further with Theano, Caffe, Keras;

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Deep Learning with Applications Using Python

required for deep learning applications. Deep Learning with Applications Using Python covers topics such as chatbots, natural language processing;

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Deep Learning for the Life Sciences

sciences in particular. This practical book teaches developers and scientists how to use deep learning for genomics, chemistry, biophysics;

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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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Deep Learning: Algorithms and Applications

alignment with the essential characteristics of the nature of learning encountered in the practical problems being tackled. Intended for readers;

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Deep Learning and IoT in Healthcare Systems

describes deep learning techniques in conjunction with IoT used by practitioners and researchers worldwide. The authors explore the convergence;

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Grokking Deep Reinforcement Learning

Written for developers with some understanding of deep learning algorithms. Experience with reinforcement learning is not required;

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Deep Learning for Coders with fastai &

, programmers comfortable with Python can achieve impressive results in deep learning with little math background, small amounts of data, and minimal;

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Deep Learning with R

other previously impossible applications. Deep Learning with R is for developers and data scientists with some R experience who;

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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 Machine Learning with Python

you how to think, design, build, and execute machine learning systems and projects successfully. Practical Machine Learning with Python;

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R Deep Learning Cookbook

intricacies of R deep learning packages such as mxnet & tensorflow * Learn application on deep learning in different domains using practical examples;

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Deep Learning Techniques and Optimization Strategies in Big Data Analytics

industries. Deep learning is a method that is transforming the world of data and analytics. Optimization of this new approach is still unclear;

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Learn Computer Vision Using Opencv: With Deep Learning Cnns and Rnns

Build practical applications of computer vision using the OpenCV library with Python. This book discusses different facets of computer;

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Practical Computer Vision Applications Using Deep Learning with CNNs

Deploy deep learning applications into production across multiple platforms. You will work on computer vision applications that use the;

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Handbook of Research on Applications and Implementations of Machine Learning Techniques

platform in which they can keep up with machine learning trends and their developments in the real world. The Handbook of Research on;

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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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