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Deep Learning Applications Volume 2

This book presents selected papers from the 18th IEEE International Conference on Machine Learning and Applications (IEEE ICMLA 2019). It;

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TensorFlow 2 Reinforcement Learning Cookbook

computer vision or perception to perform actions. TensorFlow 2.x is the latest major release of the most popular deep learning framework used to;

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Deep Learning with TensorFlow 2 and Keras

Build machine and deep learning systems with the newly released TensorFlow 2 and Keras for the lab, production, and mobile devices Key;

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Beginning Deep Learning with TensorFlow

learning models using TensorFlow 2 Create classification systems and other, practical deep learning applications Who This Book Is For;

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Deep Learning

Deep Learning provides an overview of general deep learning methodology and its applications to a variety of signal and information;

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Handbook of Deep Learning Applications

This book presents a broad range of deep-learning applications related to vision, natural language processing, gene expression, arbitrary;

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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 and IoT in Healthcare Systems

This new volume discusses the applications and challenges of deep learning and the internet of things for applications in healthcare. It;

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Image Analysis and Recognition

Recognition, ICIAR 2019, held in Waterloo, ON, Canada, in August 2019. The 58 full papers presented together with 24 short and 2 poster papers were;

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TensorFlow 2 x in the Colaboratory Cloud

well suited as a platform for TensorFlow 2.x deep learning applications. You will learn Colab's default install of the most current TensorFlow;

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Machine Learning Paradigms

six parts, namely (1) Deep Learning in Sensing, (2) Deep Learning in Social Media and IOT, (3) Deep Learning in the Medical Field, (4) Deep;

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Sentimental Analysis and Deep Learning

This book gathers selected papers presented at the International Conference on Sentimental Analysis and Deep Learning (ICSADL 2021;

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Deep Learning Projects Using TensorFlow 2

Work through engaging and practical deep learning projects using TensorFlow 2.0. Using a hands-on approach, the projects in this book will;

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State of the Art in Neural Networks and Their Applications

applications across a wide range of clinical diagnoses. Advances in the role of machine learning, artificial intelligence, deep learning, cognitive;

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Deep Learning on Graphs

readers with diverse backgrounds and purposes of reading. Part 1 introduces basic concepts of graphs and deep learning; Part 2 discusses the most;

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Computer Analysis of Images and Patterns

and Patterns, CAIP 2019, held in Salerno, Italy, in September 2019: First Workshop on Deep-learning based Computer Vision for UAV, DL-UAV 2019;

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Design of Intelligent Applications using Machine Learning and Deep Learning Techniques

Ambient Applications. Some of the topics will cover industrial issues/applications and academic research into intelligent computing. 2. Deep;

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Introduction to Deep Learning Business Applications for Developers

Discover the potential applications, challenges, and opportunities of deep learning from a business perspective with technical examples;

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Deep Learning with Applications Using Python

Explore deep learning applications, such as computer vision, speech recognition, and chatbots, using frameworks such as TensorFlow and;

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Biopolymers Based Advanced Materials (Volume 2)

The book targets many advanced biopolymers-based systems and presents deep insights into their g;...

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Deep Learning

The purpose of this edited volume is to provide a comprehensive overview on the fundamentals of deep learning, introduce the widely-used;

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Mission Oriented Sensor Networks and Systems Art and Science

This book presents a broad range of deep-learning applications related to vision, natural language processing, gene expression, arbitrary;

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Deep Biometrics

This book highlights new advances in biometrics using deep learning toward deeper and wider background, deeming it Deep Biometrics . The;

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Deep Biometrics

This book highlights new advances in biometrics using deep learning toward deeper and wider background, deeming it Deep Biometrics . The;

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Handbook of Deep Learning in Biomedical Engineering

Deep Learning (DL) is a method of machine learning, running over Artificial Neural Networks, that uses multiple layers to extract high;

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Deep Learners and Deep Learner Descriptors for Medical Applications

utilized: 1) by training a deep learner from scratch (chapters provide tips for handling imbalances and other problems with the medical data); 2;

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Deep Learning Applications

This book presents a compilation of selected papers from the 17th IEEE International Conference on Machine Learning and Applications (IEEE;

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