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Development and Analysis of Deep Learning Architectures

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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Development and Analysis of Deep Learning Architectures

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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Deep Learning: Concepts and Architectures

analysis of deep learning topologies, the book offers practical guidelines and presents competitive solutions to various areas of language modeling;

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Deep Learning for Hyperspectral Image Analysis and Classification

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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Deep Learning for Hyperspectral Image Analysis and Classification

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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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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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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Concepts and Real-Time Applications of Deep Learning

, concentrating on advances of deep learning architectures; Includes a survey of deep learning problems and solutions, identifying the main open issues;

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Advanced Deep Learning for Engineers and Scientists

through various projects; Discusses topics such as deep learning in smart grids and renewable energy & sustainable development; Explains how to;

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Deep Neural Networks and Applications

framework, development of the algorithms that quicken the data processing over complex network architectures and the optimization of database query;

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Deep Learning

analysis of deep learning topologies, the book offers practical guidelines and presents competitive solutions to various areas of language modeling;

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Emerging Deep Learning Applications of Short Range Radars

learning architectures, such as long-short term memory (LSTM), auto-encoders, variational auto-encoders (VAE), and generative adversarial networks;

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Computer Vision Metrics

learning assignments, both students and researchers can dig deeper into core computer vision topics and deep learning architectures. The survey;

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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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Neural Networks with Keras Cookbook

to choose and customize multiple neural network architectures for various deep learning problems you might encounter. What you will learn;

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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

gathered through biomedical and clinical information sensing devices necessitates the development of machine learning and AI techniques such as;

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Deep Learning for Data Analytics

analysis. Deep learning transforms data through a cascade of layers, helping systems analyze and process complex data sets. Deep learning;

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Hands-On Natural Language Processing with PyTorch 1.x

architectures to build models that will allow you to classify and translate text and perform sentiment analysis. Finally, you'll learn how to build;

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Deep Learning and Data Labeling for Medical Applications

and Expert Label Synthesis, LABELS 2016, and the Second International Workshop on Deep Learning in Medical Image Analysis, DLMIA 2016. The 28;

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Case-Based Reasoning Research and Development

. They deal with AI and related research focusing on comparison and integration of CBR with other AI methods such as deep learning architectures;

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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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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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Deep Learning for Biometrics

, and provides comparison with popular CNN-based architectures for face recognition; examines deep learning for state-of-the-art latent;

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Hands-On One-shot Learning with Python

learning process with one-shot learning Use Python and PyTorch to build state-of-the-art one-shot learning models Explore architectures such as;

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Advanced Deep Learning with Python

networks using the Python ecosystem Key Features Get to grips with building faster and more robust deep learning architectures Investigate and;

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Deep Learning with PyTorch

problems. This book explains the concepts of various state-of-the-art deep learning architectures, such as ResNet, DenseNet, Inception, and Seq2Seq;

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