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Strengthening Deep Neural Networks Making AI Less Susceptible to Adversarial Trickery

this adversarial input, and methods for increasing AI robustness to these attacks. If you're a data scientist developing DNN algorithms, a;

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Practical TensorFlow.js

browser, making it an ideal environment to deploy AI systems. TensorFlow.js is a well-known and battle-tested library for creating browser;

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Practical TensorFlow.js

browser, making it an ideal environment to deploy AI systems. TensorFlow.js is a well-known and battle-tested library for creating browser;

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Advanced Deep Learning with R

advanced examples. Later, you'll discover how to apply generative adversarial networks (GANs) to generate new images; autoencoder neural networks;

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Advanced Deep Learning with Keras

learning techniques that drive modern AI results Implement deep neural networks, autoencoders, GANs, VAEs, and deep reinforcement learning A wide;

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Advanced Deep Learning with Python

everything from how neural networks work to training CNN models. In this book, you'll discover newly developed deep learning models, methodologies;

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Deep Learning with Keras

you are a data scientist with experience in machine learning or an AI programmer with some exposure to neural networks, you will find this;

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Hands-On Neural Networks

Features Explore neural network architecture and understand how it functions Learn algorithms to solve common problems using back propagation and;

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Deep Learning and Practice with MindSpore

AI computing framework. Divided into 14 chapters, the book covers deep learning, deep neural networks (DNNs), convolutional neural networks;

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Deep Learning and Practice with MindSpore

AI computing framework. Divided into 14 chapters, the book covers deep learning, deep neural networks (DNNs), convolutional neural networks;

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Reinforcement Learning for Finance

generative adversarial networks with examples using the TensorFlow Python library. It also serves as a quick hands-on guide to TensorFlow programming;

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Proceedings of the 21st EANN (Engineering Applications of Neural Networks) 2020 Conference

International Neural Networks Society (INNS). Artificial Intelligence (AI) has been following a unique course, characterized by alternating growth;

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Artificial Intelligence for Business

with the right problems, and avoid common AI/ML project mistakes Use neural networks to automate decision-making and identify unexpected;

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Mastering PyTorch

is driving the AI revolution, and PyTorch is making it easier than ever before for anyone to build deep learning applications. This PyTorch book will;

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Artificial Intelligence in the Age of Neural Networks and Brain Computing

applications of AI is a development of the unique attributes of neural networks, mainly machine learning, distributed architectures, massive parallel;

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Explainable AI with Python

guide the design of these agents are understood, most of the current deep-learning models are opaque to human understanding. Explainable AI;

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MATLAB Deep Learning

, then move on to neural networks, deep learning, and then convolutional neural networks. In a blend of fundamentals and applications, MATLAB;

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Python Deep Learning

second edition of Python Deep Learning will get you up to speed with deep learning, deep neural networks, and how to train them with high;

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Neural Information Processing

The three-volume set of LNCS 11953, 11954, and 11955 constitutes the proceedings of the 26th International Conference on Neural Information;

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Handbook of Decision Support Systems for Neurological Disorders

recent computational approaches, different types of neurological disorders, deep convolution neural networks, generative adversarial networks;

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Efficient Processing of Deep Neural Networks

. Therefore, techniques that enable efficient processing of deep neural networks to improve metrics-such as energy-efficiency, throughput, and latency;

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Efficient Processing of Deep Neural Networks

, techniques that enable efficient processing of deep neural networks to improve key metrics-such as energy-efficiency, throughput, and latency-without;

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State of the Art in Neural Networks and Their Applications

networks, AI, machine learning, and deep learning techniques to a variety of imaging technologies Provides in-depth technical coverage;

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Artificial Intelligence By Example

learning neural networks Build quantum computing circuits Who this book is forDevelopers and those interested in AI, who want to understand the;

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AI and Deep Learning in Biometric Security

convolutional neural networks in face, hand, iris, gait, fingerprint, vein, and medical biometric traits. It also provides a step-by-step guide to;

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TensorFlow 1.x Deep Learning Cookbook

and implement tricky neural networks using Google's TensorFlow 1.x * An easy-to-follow guide that lets you explore reinforcement learning;

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Deep Learning with Microsoft Cognitive Toolkit Quick Start Guide

Learn how to train popular deep learning architectures such as autoencoders, convolutional and recurrent neural networks while discovering;

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