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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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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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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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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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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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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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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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R Deep Learning Essentials

Build automatic classification and prediction models using unsupervised learning About This Book * Harness the ability to build;

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Memristive Devices for Brain-Inspired Computing

Learning, and Spiking Neural Networks reviews the latest in material and devices engineering for optimizing memristive devices beyond storage;

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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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Embedded Deep Learning

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

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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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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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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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Deep Neuro-Fuzzy Systems with Python

architectures and models that have been developed, and how rules and reasoning have been defined to make the architectures possible. The book then;

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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

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 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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Hands-On Deep Learning with Go

Apply modern deep learning techniques to build and train deep neural networks using Gorgonia Key Features Gain a practical understanding;

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Deep Learning for Natural Language Processing

sophisticated architectures such as RNN, LSTM, and Seq2seq, using Python tools: TensorFlow, and Keras. Deep Learning for Natural Language Processing;

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Practical Convolutional Neural Networks

datasets and different CNN architectures to build efficient and smart ConvNet models. Basic knowledge of deep learning concepts and Python;

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The Deep Learning with PyTorch Workshop

complexity of neural network architectures. The Deep Learning with PyTorch Workshop starts with an introduction to deep learning and its applications;

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Programming PyTorch for Deep Learning Creating and Deploying Deep Learning Applications

creation of neural architectures that facilitate operations on images, sound, text, and more through deep dives into each element. He also covers;

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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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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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