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Advanced Deep Learning for Engineers and Scientists

. They proceed with advanced concepts with hands-on sessions for deep learning. Engineers, scientists, researches looking for a practical;

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Deep Learning Techniques for Biomedical and Health Informatics

discovery, clinical decision support systems, disease diagnosis, prediction and monitoring Discusses Deep Learning applied to Electronic;

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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 Applied Deep Learning

algorithms that are at the core of convolutional neural networks. In Advanced Applied Deep Learning, you will study advanced topics on CNN and object;

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Advanced Deep Learning with Keras

detection, and semantic segmentation, has now been published. A comprehensive guide to advanced deep learning techniques, including autoencoders;

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Multi-faceted Deep Learning

This book covers a large set of methods in the field of Artificial Intelligence - Deep Learning applied to real-world problems. The;

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TensorFlow 2 x in the Colaboratory Cloud

offers you the opportunity to grasp deep learning in an applied manner with the only requirement being an Internet connection. Everything else;

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Statistical Process Monitoring Using Advanced Data-Driven and Deep Learning Approaches

Statistical Process Monitoring Using Advanced Data-Driven and Deep Learning Approaches tackles multivariate challenges in process;

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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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Applied Cloud Deep Semantic Recognition

and situational assessment using deep learning for anomalous detection, and how methods developed in one area can be applied in applications;

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Pro Deep Learning with TensorFlow 2.0

Intermediate-Advanced user level;

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Advanced Deep Learning with R

branch of machine learning based on a set of algorithms that attempt to model high-level abstractions in data. Advanced Deep Learning with R will;

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Deep Learning and Missing Data in Engineering Systems

Deep Learning and Missing Data in Engineering Systems uses deep learning and swarm intelligence methods to cover missing data estimation;

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Automated Deep Learning Using Neural Network Intelligence

Intermediate-Advanced user level;

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Deep Learning for Medical Image Analysis

integrate deep learning as a core component have been applied to medical image detection, segmentation and registration, and computer-aided analysis;

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Hands-On Deep Learning for Games

. Understand core concepts of DRL and the differences between discrete and continuous action environments. Use several advanced forms of learning;

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Quantum Machine Learning: An Applied Approach

intermediate-Advanced user level;

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Practical MATLAB Deep Learning

Harness the power of MATLAB for deep-learning challenges. Practical MATLAB Deep Learning, Second Edition, remains a one-of a-kind book that;

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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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Probabilistic Deep Learning

Probabilistic Deep Learning shows how probabilistic deep learning models gives readers the tools to identify and account for uncertainty;

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Deep Learning in Biometrics

Deep Learning is now synonymous with applied machine learning. Many technology giants (e.g. Google, Microsoft, Apple, IBM) as well as start;

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R Deep Learning Essentials

theoretical concepts of deep learning projects, such as model optimization, overfitting, and data augmentation, together with other advanced topics;

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Handbook of Deep Learning Applications

frameworks. Deep learning, an advanced level of machine learning technique that combines class of learning algorithms with the use of many layers;

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Introduction to Deep Learning Using R

Understand deep learning, the nuances of its different models, and where these models can be applied. The abundance of data and demand for;

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Reinforcement Learning

starts with the TensorFlow environment and gives an outline of how reinforcement learning can be applied to TensorFlow. There's also coverage;

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TensorFlow 2 Reinforcement Learning Cookbook

OpenAI Gym, model-based RL, model-free RL, and how to develop basic agents. You'll discover how to implement advanced deep reinforcement learning;

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Practical Convolutional Neural Networks

of supervised training data of labeled images is available. Later we build upon the learning achieved to build advanced vision related algorithms for;

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