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Deep Learning for Search

Description Deep Learning for Search teaches readers how to leverage neural networks, NLP, and deep learning techniques to improve;

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Automated Deep Learning Using Neural Network Intelligence

learning model development. The first chapters of this book cover the basics of NNI toolkit usage and methods for solving hyper-parameter;

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Deep Reinforcement Learning with Python

learning algorithms, along with Monte Carlo tree search (MCTS), which played a key role in the success of AlphaGo. The final chapters conclude with;

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Introduction to Deep Learning Business Applications for Developers

for controlling robotics, exploring the DeepQ learning algorithm with Monte Carlo Tree search (used to beat humans in the game of Go), and;

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In Search of Deeper Learning

creativity? In Search of Deeper Learning takes a deep dive into the state of our schools and lays out an inspiring new vision for American;

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Mastering PyTorch

Master advanced techniques and algorithms for deep learning with PyTorch using real-world examples Key Features Understand how to use;

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Deep Learning with TensorFlow

learning algorithms for machine learning systems and integrate them into your product offerings, including search, image recognition, and language;

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Practical Deep Learning for Cloud and Mobile RealWorld AI Computer Vision Projects Using Python, Keras TensorFlow

learning applications for the cloud and mobile using a hands-on approach. Relying on years of industry experience transforming deep-learning;

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Advanced Deep Learning for Engineers and Scientists

This book provides a complete illustration of deep learning concepts with case-studies and practical examples useful for real time;

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Deep Learning for Data Analytics

of domains including data science, data analytics and biomedical engineering. Deep Learning for Data Analytics: Foundations, Biomedical Applications;

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Learn Keras for Deep Neural Networks

machine learning, the choices of frameworks for deep learning, and the Keras ecosystem. You will cover a real-life business problem that can be;

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Deep Learning in Science

building it up, this is essential reading for any scientists, instructors, and students interested in artificial intelligence and deep learning. It;

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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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Deep Learning

detection * Experiment with deep learning concepts, algorithms, and the toolbox for deep learning * Select and split data sets into training, test;

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Practical MATLAB Deep Learning

Harness the power of MATLAB for deep-learning challenges. Practical MATLAB Deep Learning, Second Edition, remains a one-of a-kind book that;

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Practical MATLAB Deep Learning

Harness the power of MATLAB for deep-learning challenges. Practical MATLAB Deep Learning, Second Edition, remains a one-of a-kind book that;

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R Deep Learning Essentials

Implement neural network models in R 3.5 using TensorFlow, Keras, and MXNet Key Features Use R 3.5 for building deep learning models for;

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Hands-On Deep Learning for Images with TensorFlow

Explore TensorFlow's capabilities to perform efficient deep learning on images Key Features Discover image processing for machine vision;

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Deep Learning For Dummies

Take a deep dive into deep learning Deep learning provides the means for discerning patterns in the data that drive online business;

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R Deep Learning Cookbook

from text, image and speech * Guide to set-up deep learning models using CPU and GPU Who This Book Is For Data science professionals or;

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TensorFlow Deep Learning Projects

Leverage the power of Tensorflow to design deep learning systems for a variety of real-world scenarios Key Features Build efficient deep;

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Deep Learning Cookbook

cookbook, you'll learn how to solve deep-learning problems for classifying and generating text, images, and music. Each chapter consists;

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Data-Driven Evolutionary Optimization

Intended for researchers and practitioners alike, this book covers carefully selected yet broad topics in optimization, machine learning;

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Deep Learning and Missing Data in Engineering Systems

Deep Learning and Missing Data in Engineering Systems uses deep learning and swarm intelligence methods to cover missing data estimation;

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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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Machine Learning for Text

This second edition textbook covers a coherently organized framework for text analytics, which integrates material drawn from the;

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