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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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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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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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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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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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Artificial Intelligence, Machine Learning, and Deep Learning

learning architectures such as CNNs, RNNs,LSTMs, and auto encoders. Keras-based code samples are included to supplementthe theoretical discussion;

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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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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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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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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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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 for Biometrics

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

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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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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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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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Recent Trends in Learning from Data: Tutorials from the Inns Big Data and Deep Learning Conference (Innsbddl2019)

respective chapters cover advanced neural networks, deep architectures, and supervised and reinforcement machine learning models. They describe;

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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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Data Science

This book targets an audience with a basic understanding of deep learning, its architectures, and its application in the multimedia domain;

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Deep Neural Networks and Applications

. Also discussed in the book is the optimization of deep learning for the best performance of e-learning data, the methodology and the research;

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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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Convolutional Neural Networks in Visual Computing

This book covers the fundamentals in designing and deploying techniques using deep architectures. It is intended to serve as a beginner's;

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Convolutional Neural Networks in Visual Computing

This book covers the fundamentals in designing and deploying techniques using deep architectures. It is intended to serve as a beginner's;

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Recent Trends in Learning From Data

respective chapters cover advanced neural networks, deep architectures, and supervised and reinforcement machine learning models. They describe;

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Deep Learning

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

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Hands-On Deep Learning with R

Deepnet Key Features Understand deep learning algorithms and architectures using R and determine which algorithm is best suited for a specific;

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