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Statistical and Machine Learning Approaches for Network Analysis

Explore the multidisciplinary nature of complex networks through machine learning techniques Statistical and Machine Learning Approaches;

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Fundamentals Of Deep Learning

-learning teams. For the rest of us, however, deep learning is still a pretty complex and difficult subject to grasp. If you're familiar with Python;

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MATLAB Deep Learning

, you'll be able to tackle some of today's real world big data, smart bots, and other complex data problems. You'll see how deep learning is a;

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Machine Learning for Networking

This book constitutes the thoroughly refereed proceedings of the Second International Conference on Machine Learning for Networking, MLN;

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Machine Learning in Complex Networks

This book presents the features and advantages offered by complex networks in the machine learning domain. In the first part, an overview;

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Machine Learning in Complex Networks

This book presents the features and advantages offered by complex networks in the machine learning domain. In the first part, an overview;

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Hands-on Machine Learning with Python

Here is the perfect comprehensive guide for readers with basic to intermediate level knowledge of machine learning and deep learning. It;

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Hands-on Machine Learning with Python

Here is the perfect comprehensive guide for readers with basic to intermediate level knowledge of machine learning and deep learning. It;

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Machine Learning for Networking

This book constitutes the thoroughly refereed proceedings of the Second International Conference on Machine Learning for Networking, MLN;

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Machine Learning for Networking: First International Conference, Mln 2018, Paris, France, November 27-29, 2018, Revised Selected Papers

This book constitutes the thoroughly refereed proceedings of the First International Conference on Machine Learning for Networking, MLN;

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Mastering Machine Learning Algorithms

Hidden Markov models, this machine learning book teaches you how to extract features from your dataset, perform complex dimensionality reduction;

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Dynamics On and Of Complex Networks III

physics. It contains diverse but relevant topics in statistical physics, complex systems, network theory, and machine learning. Examples of such;

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Dynamics On and Of Complex Networks III

physics. It contains diverse but relevant topics in statistical physics, complex systems, network theory, and machine learning. Examples of such;

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Python Deep Learning

computer vision and NLP Learn how a computer can navigate in complex environments with reinforcement learning Book DescriptionWith the surge;

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Deep Learning with TensorFlow 2 and Keras

Build machine and deep learning systems with the newly released TensorFlow 2 and Keras for the lab, production, and mobile devices Key;

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Deep Learning and Practice with MindSpore

collaboration, deep learning visualization, and data preparation for deep learning. To help clarify the complex topics discussed, this book includes;

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Deep Learning and Practice with MindSpore

collaboration, deep learning visualization, and data preparation for deep learning. To help clarify the complex topics discussed, this book includes;

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Optimization, Learning, and Control for Interdependent Complex Networks

This book focuses on a wide range of optimization, learning, and control algorithms for interdependent complex networks and their role;

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Machine Learning in Social Networks

This book deals with network representation learning. It deals with embedding nodes, edges, subgraphs and graphs. There is a growing;

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Machine Learning Using TensorFlow Cookbook

Comprehensive recipes to give you valuable insights on Transformers, Reinforcement Learning, and more Key Features Deep Learning;

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Go Machine Learning Projects

, Gonum, and GoCv for training models in Go Book DescriptionGo is the perfect language for machine learning; it helps to clearly describe complex;

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Federated Learning for Wireless Networks

Recently machine learning schemes have attained significant attention as key enablers for next-generation wireless systems. Currently;

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Mastering Machine Learning Algorithms

Explore and master the most important algorithms for solving complex machine learning problems. Key Features Discover high-performing;

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Engineering Applications of Neural Networks

and selected from 39 submissions. The papers are organized in topical sections on activity recognition, deep learning, extreme learning;

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Neural Networks with R

learning models for solving complex computational problems efficiently. Neural networks are used to solve wide range of problems in different areas;

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The Deep Learning Workshop

Take a hands-on approach to understanding deep learning and build smart applications that can recognize images and interpret text Key;

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TensorFlow Machine Learning Cookbook

Skip the theory and get the most out of Tensorflow to build production-ready machine learning models Key Features Exploit the features;

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