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Statistical Learning Using Neural Networks

Statistical Learning using Neural Networks: A Guide for Statisticians and Data Scientists with Python introduces artificial neural networks;

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Statistical Learning Using Neural Networks

Statistical Learning using Neural Networks: A Guide for Statisticians and Data Scientists with Python introduces artificial neural networks;

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Neural Networks with R

in neural networks with R, to implement them in your applications * Build smart systems using the power of deep learning * Real-world case studies;

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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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Neural Networks and Statistical Learning

This book provides a broad yet detailed introduction to neural networks and machine learning in a statistical framework. A single;

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Handbook on Neural Information Processing

Approximation capabilities of neural networks Semi-supervised learning Statistical relational learning Kernel methods for structured data Multiple;

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New Developments In Artificial Neural Networks Research

neural network based visual servo system; modelling of computer-assisted learning using artificial neural networks; prediction of hole quality;

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Statistical Mechanics of Neural Networks

This book highlights a comprehensive introduction to the fundamental statistical mechanics underneath the inner workings of neural networks;

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Training Systems Using Python Statistical Modeling

, build, and deploy enterprise-grade statistical models for machine learning using Python and its rich ecosystem of libraries for predictive;

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Kinematic Control of Redundant Robot Arms Using Neural Networks

specific level, it deals with the dynamic-neural-network based kinematic control of redundant robot arms by using theoretical tools and simulations;

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Evolutionary Machine Learning Techniques

, and the training algorithms for machine learning using classical and modern nature-inspired techniques. It also investigates the pros and cons;

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Evolutionary Machine Learning Techniques

, literature reviews, and the training algorithms for machine learning using classical and modern nature-inspired techniques. It also investigates the;

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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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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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R Deep Learning Projects

mathematicians for statistical analysis, and is popularly used for deep learning. Deep Learning, as we all know, is one of the trending topics today, and;

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Reinforcement Learning for Finance

This book introduces reinforcement learning with mathematical theory and practical examples from quantitative finance using the TensorFlow;

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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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Neural Networks with Keras Cookbook

book will take you from the basics of neural networks to advanced implementations of architectures using a recipe-based approach. We will learn;

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Deep Neural Networks and Applications

structures using deep learning.;

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Pattern Recognition and Neural Networks

Ripley brings together two crucial ideas in pattern recognition: statistical methods and machine learning via neural networks. He brings;

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Recurrent Neural Networks

treatment of the subject, with a view toward using coding and deep learning computational frameworks, e.g., Python and Tensorflow-Keras. Recurrent;

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Hands-On Deep Learning with Go

Apply modern deep learning techniques to build and train deep neural networks using Gorgonia Key Features Gain a practical understanding;

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Hands-On Neural Networks

Design and create neural networks with deep learning and artificial intelligence principles using OpenAI Gym, TensorFlow, and Keras Key;

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TensorFlow 2.0 Quick Start Guide

traditional supervised and unsupervised machine learning applications Understand image recognition techniques using TensorFlow Perform neural style;

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Artificial Intelligence By Example

and Internet of Things (IoT), and develop emotional quotient in chatbots using neural networks such as recurrent neural networks (RNNs) and;

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Java Deep Learning Cookbook

Deeplearning4j to implement deep learning models from scratch Explore recipes for developing, training, and fine-tuning your neural network models;

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Principal Component Neural Networks

of the mathematical, algorithmic, application and architectural aspects of principal component neural networks. Using a unified formulation, the;

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