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Deep In-memory Architectures for Machine Learning

This book describes the recent innovation of deep in-memory architectures for realizing AI systems that operate at the edge of energy;

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Deep In-memory Architectures for Machine Learning

This book describes the recent innovation of deep in-memory architectures for realizing AI systems that operate at the edge of energy;

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Hardware Accelerator Systems for Artificial Intelligence and Machine Learning

Machine Learning, Deep Learning with GPUs, Edge Computing Optimization of Deep Learning Models for Specialized Tensor Processing Architectures;

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Emerging Deep Learning Applications of Short Range Radars

learning architectures, such as long-short term memory (LSTM), auto-encoders, variational auto-encoders (VAE), and generative adversarial networks;

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Artificial Intelligence, Machine Learning, and Deep Learning

learning architectures such as CNNs, RNNs,LSTMs, and auto encoders. Keras-based code samples are included to supplementthe theoretical discussion;

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Advanced Deep Learning with R

help you understand popular deep learning architectures and their variants in R, along with providing real-life examples for them. This deep;

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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

sequences in recurrent neural networks and long short term memory Who This Book Is For Data scientists, machine learning engineers, and;

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Hands-On One-shot Learning with Python

few-shot learning Work with different deep learning architectures for one-shot learning Understand when to use one-shot and transfer learning;

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Introduction to Machine Learning with Applications in Information Security

), and Recurrent Neural Networks (RNN) are covered in depth. A broad range of advanced deep learning architectures are also presented, including;

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Deep Learning Applications

solve real-world problems, and also offers insights into deep learning architectures and algorithms, making it an essential reference guide for;

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Deep Learning Applications Volume 2

. Also offering insights into deep learning architectures and algorithms, it is an essential reference guide for academic researchers;

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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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ReRAM-based Machine Learning

such large amounts of data sets has led to the adoption of machine learning (ML) and deep learning (DL) methods in a wide range of applications;

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Deep Learning for Medical Decision Support Systems

the essentials of and highlight recent applications of deep learning architectures for medical decision support systems. The target audience;

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Deep Learning for Medical Decision Support Systems

the essentials of and highlight recent applications of deep learning architectures for medical decision support systems. The target audience;

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Machine Learning with Scala Quick Start Guide

concepts that make it easy to build scalable and complex big data applications. This book is a handy guide for machine learning developers and data;

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Pro Deep Learning with TensorFlow

required to invent new deep learning architectures and solutions on your own. Pro Deep Learning with TensorFlow provides practical, hands-on;

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Thinking Machines

computing and neural networks. This is the first book that focuses on machine learning accelerators and hardware development for machine learning;

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Hands-On Deep Learning with R

Deepnet Key Features Understand deep learning algorithms and architectures using R and determine which algorithm is best suited for a specific;

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Hands-On Machine Learning with Scikit-Learn and TensorFlow

, including convolutional nets, recurrent nets, and deep reinforcement learning Learn techniques for training and scaling deep neural nets Apply;

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Mastering PyTorch

neural network (RNN) architectures and transformers for sentiment analysis. As you advance, you'll apply deep learning across different domains;

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Neural Networks with Keras Cookbook

to choose and customize multiple neural network architectures for various deep learning problems you might encounter. What you will learn;

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R Deep Learning Essentials

algorithms for unsupervised data using deep learning concepts with R * Master the common problems faced such as overfitting of data, anomalous;

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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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Deep Learning for Biometrics

This timely text/reference presents a broad overview of advanced deep learning architectures for learning effective feature representation;

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Building Intelligent Cloud Applications Develop Scalable Models Using Serverless Architectures with Azure

programmers how to build and deploy scalable machine learning and deep learning models using serverless architectures with Microsoft Azure. You'll;

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