The ReinforcementLearning and RBM packages differ from the libraries already covered in two important ways: first, they are specialized packages that have functions for only one specific deep learning task instead of attempting to support myriad deep learning options, and second, they are completely written in R and have no additional language dependencies. This can be an advantage as the complexity of the previous libraries means that the packages can break when changes happen outside the package. The support pages for these libraries are full of examples of installation FAQs and troubleshooting instructions, as well as some cases where a given package may suddenly stop working or become deprecated. In these cases, we encourage you to continue searching CRAN and other sites as the R community is well known for its dynamic, evolving, and...
Hands-On Deep Learning with R
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Hands-On Deep Learning with R
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Overview of this book
Deep learning enables efficient and accurate learning from a massive amount of data. This book will help you overcome a number of challenges using various deep learning algorithms and architectures with R programming.
This book starts with a brief overview of machine learning and deep learning and how to build your first neural network. You’ll understand the architecture of various deep learning algorithms and their applicable fields, learn how to build deep learning models, optimize hyperparameters, and evaluate model performance. Various deep learning applications in image processing, natural language processing (NLP), recommendation systems, and predictive analytics will also be covered. Later chapters will show you how to tackle recognition problems such as image recognition and signal detection, programmatically summarize documents, conduct topic modeling, and forecast stock market prices. Toward the end of the book, you will learn the common applications of GANs and how to build a face generation model using them. Finally, you’ll get to grips with using reinforcement learning and deep reinforcement learning to solve various real-world problems.
By the end of this deep learning book, you will be able to build and deploy your own deep learning applications using appropriate frameworks and algorithms.
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Current Title:
Hands-On Deep Learning with R
Table of Contents (16 chapters)
Preface
Section 1: Deep Learning Basics
Free Chapter
Machine Learning Basics
Setting Up R for Deep Learning
Artificial Neural Networks
Section 2: Deep Learning Applications
CNNs for Image Recognition
Multilayer Perceptron for Signal Detection
Neural Collaborative Filtering Using Embeddings
Deep Learning for Natural Language Processing
Long Short-Term Memory Networks for Stock Forecasting
Generative Adversarial Networks for Faces
Section 3: Reinforcement Learning
Reinforcement Learning for Gaming
Deep Q-Learning for Maze Solving
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