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Neural Network Programming with Java

discussed in this book:-Understanding Neural Networks-Learning in Neural Networks-Java Lego Robots Neural Network-Convolutional Neural Networks;

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Neural Information Processing

follows: adversarial networks and learning; convolutional neural networks; deep neural networks; embeddings and feature fusion; human centred;

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Image Texture Analysis

convolutional neural networks. This introductory text on image texture analysis is ideally suitable for senior undergraduate and first-year;

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Deep Neural Networks in a Mathematical Framework

gradient descent algorithms in a unified way for several neural network structures, including multilayer perceptrons, convolutional neural networks;

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Deep Learning from Scratch Building with Python from First Principles

neural networks, convolutional neural networks, and recurrent neural networks from the ground up. With a thorough understanding of how neural;

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Tree-Based Convolutional Neural Networks

This book proposes a novel neural architecture, tree-based convolutional neural networks (TBCNNs),for processing tree-structured data;

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Neural Network Methods in Natural Language Processing

, including 1D convolutional neural networks, recurrent neural networks, conditioned-generation models, and attention-based models. These;

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Neural Network Methods in Natural Language Processing

, including 1D convolutional neural networks, recurrent neural networks, conditioned-generation models, and attention-based models. These;

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Convolutional Neural Networks in Visual Computing

neural networks (CNN) from scratch. The book focuses explicitly on convolutional neural networks, filtering out other material that co-occur;

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Convolutional Neural Networks in Visual Computing

neural networks (CNN) from scratch. The book focuses explicitly on convolutional neural networks, filtering out other material that co-occur;

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Hands-on Machine Learning with Python

third section explains complex neural network architectures with details on internal working and implementation of convolutional neural networks;

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Deep Convolutional Neural Network for The Prognosis of Diabetic Retinopathy

neural network and visualizations techniques, this work develops a prognosis system used to automatically detect the diabetic retinopathy disease;

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Hands-on Machine Learning with Python

third section explains complex neural network architectures with details on internal working and implementation of convolutional neural networks;

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Deep Learning for Natural Language Processing

Explore convolutional and recurrent neural networks and long short-term memory networks Book DescriptionApplying deep learning approaches to;

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Hardware Accelerator Systems for Artificial Intelligence and Machine Learning

, Architecture of NPU for DNN, Hardware Architecture for Convolutional Neural Network for Image Processing, FPGA based Neural Network Accelerators, and;

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New Classification Method Based on Modular Neural Networks with the LVQ Algorith

In this book a new model for data classification was developed. This new model is based on the competitive neural network Learning Vector;

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Deep Learning with PyTorch Quick Start Guide

Introduction to deep learning and PyTorch by building a convolutional neural network and recurrent neural network for real-world use cases;

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PyTorch Recipes

PyTorch. This is followed by a deep dive on building models with convolutional neural networks, deep neural networks, and recurrent neural;

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The Deep Learning with PyTorch Workshop

neural networks (ANNs) Handle computer vision problems with convolutional neural networks (CNNs) Perform language translation tasks using;

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Advanced Deep Learning with R

speed with artificial neural networks, recurrent neural networks, convolutional neural networks, long short-term memory networks, and more using;

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Neural Networks with Keras Cookbook

multiple applications, including a self-driving car using Convolutional Neural Networks. We will generate images while leveraging GANs and also by;

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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 Information Processing

techniques across different domains. The first volume, LNCS 11301, is organized in topical sections on deep neural networks, convolutional neural;

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A Pattern Recognition Approach to Electromyography Data

and time performance is given by spectral and temporal extraction features along with neural network recognition algorithm. Ideas from this;

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Practical TensorFlow.js

applications.You'll work with deep learning algorithms such as feedforward neural networks, convolutional neural networks (CNN), recurrent neural networks;

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Practical TensorFlow.js

'll work with deep learning algorithms such as feedforward neural networks, convolutional neural networks (CNN), recurrent neural networks;

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Einde inhoud

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