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Hands-on Machine Learning with R provides a practical and applied approach to learning and developing intuition into today's most popular;
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Hands-on projects cover all the key deep learning methods built step-by-step in PyTorch Key Features Internals and principles of PyTorch;
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Incorporate deep learning into your development projects through hands-on coding and the latest versions of deep learning software, such as;
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quickly and easily gain insight from your data. Machine Learning with R, Third Edition provides a hands-on, readable guide to applying machine;
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layers of complexity can be processed. Hands-On Deep Learning with Apache Spark addresses the sheer complexity of technical and analytical parts;
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intricacies of R deep learning packages such as mxnet & tensorflow * Learn application on deep learning in different domains using practical examples;
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with R programming. This book starts with a brief overview of machine learning and deep learning and how to build your first neural network;
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Use R for efficient data mining, text analytics and feature engineering Become a thorough data science professional with the help of hands-on;
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branch of machine learning based on a set of algorithms that attempt to model high-level abstractions in data. Advanced Deep Learning with R will;
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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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learning. You'll then learn about high-performance computation using GPUs, along with learning about parallel computation capabilities in R. Later;
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through deep learning and convolutional neural networks, deep learning with recurrent neural networks, big data with Hadoop (R), Spark (TM) and;
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fast, efficient, and accurate deep learning tasks. Hands-On Deep Learning for Images with TensorFlow shows you the practical implementations;
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Incorporate deep learning into your development projects through hands-on coding and the latest versions of deep learning software, such as;
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Explore the world of neural networks by building powerful deep learning models using the R ecosystem Key Features Get to grips with the;
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. They proceed with advanced concepts with hands-on sessions for deep learning. Engineers, scientists, researches looking for a practical;
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Master the practical aspects of implementing deep learning solutions with PyTorch, using a hands-on approach to understanding both theory;
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Master the practical aspects of implementing deep learning solutions with PyTorch, using a hands-on approach to understanding both theory;
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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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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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intelligence and machine learning services available on AWS. With this book, you'll work through hands-on exercises and learn to use these services to;
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text Offers hands-on activities to make learning easier Shows you how to use Deep Learning more effectively with the right tools This;
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deep learning models for real-world datasets Build your experience and confidence with hands-on exercises and activities Book;
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real-world case studies with code to provide hands-on experience. Many books focus on deep learning theory or deep learning for NLP-specific;
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real-world case studies with code to provide hands-on experience. Many books focus on deep learning theory or deep learning for NLP-specific;
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other previously impossible applications. Deep Learning with R is for developers and data scientists with some R experience who;
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edition of the bestselling introduction to deep reinforcement learning, expanded with six new chapters Learn advanced exploration techniques;
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Einde inhoud
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