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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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theoretical concepts of deep learning projects, such as model optimization, overfitting, and data augmentation, together with other advanced topics;
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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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learning packages and libraries in R. It will be starting with different packages in Deep Learning to neural networks and structures. You will also;
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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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Master machine learning techniques with R to deliver insights in complex projects About This Book * Understand and apply machine;
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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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that sell for over $400k because they are so human-like. Advanced Deep Learning with Keras is a comprehensive guide to the advanced deep;
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. Introduction to Deep Learning Using R provides a theoretical and practical understanding of the models that perform these tasks by building upon the;
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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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, now JavaScript developers can build deep learning apps without relying on Python or R. Deep Learning with JavaScript shows;
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Intermediate-Advanced user level;
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learning projects. Anyone looking to get to grips with advanced use cases and methodologies adopted in the deep learning domain using real-world;
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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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Examine the latest technological advancements in building a scalable machine-learning model with big data using R. This second edition;
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Master advanced techniques and algorithms for deep learning with PyTorch using real-world examples Key Features Understand how to use;
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-world projects related to text generation, sentiment analysis, fraud detection, and more Get to grips with R's impressive range of Deep Learning;
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; Cover various advanced neural network architecture such as ResNet, Inception, DenseNet and more with practical examples; Book DescriptionDeep;
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Solve real-world data problems with R and machine learning Key Features Third edition of the bestselling, widely acclaimed R machine;
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frameworks. Deep learning, an advanced level of machine learning technique that combines class of learning algorithms with the use of many layers;
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DescriptionThis Learning Path is your complete guide to quickly getting to grips with popular machine learning algorithms. You'll be introduced to the;
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foundation in neural networks and deep learning with Python libraries Explore advanced deep learning techniques and their applications across;
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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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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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Understand basic to advanced deep learning algorithms, the mathematical principles behind them, and their practical applications. Key;
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