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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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Demystifying Big Data, Machine Learning, and Deep Learning for Healthcare Analytics

Demystifying Big Data, Machine Learning, and Deep Learning for Healthcare Analytics presents the changing world of data utilization;

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Advanced Deep Learning Applications in Big Data Analytics

in connectivity, scalability, and privacy since its birth. The application of deep learning algorithms has helped process those challenges and remains a;

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Deep Learning from Scratch Building with Python from First Principles

With the resurgence of neural networks in the 2010s, deep learning has become essential for machine learning practitioners and even many;

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Deep Learning Techniques and Optimization Strategies in Big Data Analytics

, however, and there's a need for research on the various applications and techniques of deep learning in the field of computing. Deep Learning;

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Python Advanced Guide to Artificial Intelligence: Advanced Guide to Artificial Intelligence: Expert machine learning systems and intelligent agents us

supervised, unsupervised, and semi-supervised ML algorithms and their implementation Build deep learning models for object detection, image;

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Advanced Deep Learning with Python

Gain expertise in advanced deep learning domains such as neural networks, meta-learning, graph neural networks, and memory augmented neural;

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Artificial Intelligence Trends for Data Analytics Using Machine Learning and Deep Learning Approaches

and deep learning approaches, presents data analytics and mining for decision-support applications, and includes case-based reasoning, natural;

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Deep Learning with PyTorch

Build neural network models in text, vision and advanced analytics using PyTorch Key Features Learn PyTorch for implementing cutting-edge;

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Practical MATLAB Deep Learning

Who This Book Is For Engineers, data scientists, and students wanting a book rich in examples on deep learning using MATLAB.;

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Practical MATLAB Deep Learning

for spacecraft MATLAB Bluetooth data acquisition applied to dance physics What You Will LearnExplore deep learning using MATLAB and compare it;

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Handbook of Deep Learning Applications

processing, hierarchical deep learning networks as game-playing artefacts using regret matching, and building GPU-accelerated deep learning;

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Computational Methods for Deep Learning

explains the mechanism of deep learning using advanced mathematics. In particular, it emphasizes how to use TensorFlow and the latest MATLAB deep;

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Computational Methods for Deep Learning

explains the mechanism of deep learning using advanced mathematics. In particular, it emphasizes how to use TensorFlow and the latest MATLAB deep;

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Machine Learning for Speaker Recognition

This book will help readers understand fundamental and advanced statistical models and deep learning models for robust speaker recognition;

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Advanced Deep Learning with R

Discover best practices for choosing, building, training, and improving deep learning models using Keras-R, and TensorFlow-R libraries Key;

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Mission Oriented Sensor Networks and Systems Art and Science

processing, hierarchical deep learning networks as game-playing artefacts using regret matching, and building GPU-accelerated deep learning;

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Modern Deep Learning Design and Application Development

helpful tips and best practices for making the most of the framework. Next, yoüll learn how to train models effectively with transfer learning and;

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Hands-On Deep Learning with Go

of libraries and communities for deep learning Get to grips with Neural Networks, their history, and how they work Design and implement Deep Neural;

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Machine Learning and Deep Learning in Real-Time Applications

have proven to be vital solutions to numerous real-world issues are machine learning and deep learning. These intelligent agents unlock higher;

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R Deep Learning Essentials

Implement neural network models in R 3.5 using TensorFlow, Keras, and MXNet Key Features Use R 3.5 for building deep learning models for;

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Deep Learning for Cancer Diagnosis

. Accordingly, the objective of this book is to highlight recent advanced applications of deep learning for diagnosing different types of cancer. The;

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Deep Learning for Cancer Diagnosis

. Accordingly, the objective of this book is to highlight recent advanced applications of deep learning for diagnosing different types of cancer. The;

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Advanced Machine Learning Approaches in Cancer Prognosis

This book introduces a variety of advanced machine learning approaches covering the areas of neural networks, fuzzy logic, and hybrid;

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PyTorch Deep Learning Hands-On

Hands-on projects cover all the key deep learning methods built step-by-step in PyTorch Key Features Internals and principles of PyTorch;

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Data Science Solutions with Python

learning is unpacked. This book is for intermediate-level data scientists and machine learning engineers who want to learn how to apply key;

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Deep Learning for Biomedical Applications

recent view of research works on essential, and advanced topics. The book offers detailed information on the application of Deep Learning for;

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