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Adversarial Robustness for Machine Learning

Adversarial Robustness for Machine Learning summarizes the recent progress on this topic and introduces popular algorithms on;

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Machine Learning for Cyber Security

This three volume book set constitutes the proceedings of the Third International Conference on Machine Learning for Cyber Security, ML4CS;

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Machine Learning for Cyber Security

This three volume book set constitutes the proceedings of the Third International Conference on Machine Learning for Cyber Security, ML4CS;

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Machine Learning for Cyber Security

This three volume book set constitutes the proceedings of the Third International Conference on Machine Learning for Cyber Security, ML4CS;

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Challenges in Machine Generation of Analytic Products from Multi-Source Data

, with special attention to learning from small data, using multi-source data, adversarial learning, and understanding the human-machine;

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Engineering Dependable and Secure Machine Learning Systems

This book constitutes the revised selected papers of the Third International Workshop on Engineering Dependable and Secure Machine Learning;

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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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Deployable Machine Learning for Security Defense

This book constitutes selected papers from the First International Workshop on Deployable Machine Learning for Security Defense, MLHat 2020;

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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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Machine Learning in Marketing

training data, looking for generalizability. Thus, one of the potential contributions of machine learning to marketing lies in its robustness for;

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Machine Learning for Finance

A guide to advances in machine learning for financial professionals, with working Python code Key Features Explore advances in machine;

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Mastering Machine Learning for Penetration Testing

generative adversarial networks using Python libraries Work on threat hunting with machine learning and the ELK stack Explore the best practices for;

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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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Explainable AI with Python

are opaque. Using examples from Computer Vision, the authors then look at explainable models for Deep Learning and prospective methods for;

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Layered Learning in Multiagent Systems

adversarial environments. The book makes four main contributions to the fields of machine learning and multiagent systems. First, it describes an;

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Introduction to Machine Learning with Applications in Information Security

Long Short-Term Memory (LSTM), Generative Adversarial Networks (GAN), Extreme Learning Machines (ELM), Residual Networks (ResNet), Deep Belief;

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Python Machine Learning

Applied machine learning with a solid foundation in theory. Revised and expanded for TensorFlow 2, GANs, and reinforcement learning;

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Quality, Reliability, Security and Robustness in Heterogeneous Systems

and scheduling, machine learning, telecommunication systems, and network management.;

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Artificial Neural Networks and Machine Learning ICANN 2020

and selected from 249 submissions. They were organized in 2 volumes focusing on topics such as adversarial machine learning, bioinformatics;

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Artificial Neural Networks and Machine Learning ICANN 2020

and selected from 249 submissions. They were organized in 2 volumes focusing on topics such as adversarial machine learning, bioinformatics;

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Machine Learning in Medical Imaging

in medical imaging. Topics dealt with are: deep learning, generative adversarial learning, ensemble learning, sparse learning, multi-task learning;

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Deep Learning with TensorFlow 2 and Keras

(convolutional neural networks) and how they are essential for deep learning systems such as image classifiers Use GANs (generative adversarial networks;

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Machine Learning for Computer Scientists and Data Analysts

This textbook introduces readers to the theoretical aspects of machine learning (ML) algorithms, starting from simple neuron basics;

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Machine Learning for Future Fiber-Optic Communication Systems

Machine Learning for Future Fiber-Optic Communication Systems provides a comprehensive and in-depth treatment of machine learning;

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Introduction to Machine Learning

convolutional and generative adversarial networks; new material in the chapter on reinforcement learning that covers the use of deep networks, the;

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Applied Machine Learning

Machine learning methods are now an important tool for scientists, researchers, engineers and students in a wide range of areas. This book;

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Reinforcement Learning for Finance

library. Reinforcement Learning for Finance begins by describing methods for training neural networks. Next, it discusses CNN and RNN - two;

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