Deep Learning with R Cookbook

Tackle the complex challenges faced while building end-to-end deep learning models using modern R libraries

▶What You Will Learn
⦁ Work with different datasets for image classification using CNNs
⦁ Apply transfer learning to solve complex computer vision problems
⦁ Use RNNs and their variants such as LSTMs and Gated Recurrent Units (GRUs) for sequence data generation and classification
⦁ Implement autoencoders for DL tasks such as dimensionality reduction, denoising, and image colorization
⦁ Build deep generative models to create photorealistic images using GANs and VAEs
⦁ Use MXNet to accelerate the training of DL models through distributed computing

▶Key Features
⦁ Understand the intricacies of R deep learning packages to perform a range of deep learning tasks
⦁ Implement deep learning techniques and algorithms for real-world use cases
⦁ Explore various state-of-the-art techniques for fine-tuning neural network models

▶Who This Book Is For
>This deep learning book is for data scientists, machine learning practitioners, deep learning researchers and AI enthusiasts who want to learn key tasks in deep learning domains using a recipe-based approach. A strong understanding of machine learning and working knowledge of the R programming language is mandatory.

▶What this book covers
⦁ Chapter 1, Understanding Neural Networks and Deep Neural Networks, will show us how to set up a deep learning environment to train models. The readers are then introduced to neural networks, starting from how neural networks work, what hidden layers are, what backpropagation is, and what activation functions are. This chapter uses the keras library to demonstrate the recipes.

⦁ Chapter 2, Working with Convolutional Neural Networks, will show us CNNs and will explain how they can be used to train models for image recognition and natural language processing based tasks. This chapter also covers various hyperparameters and optimizers used with CNNs.

⦁ Chapter 3, Recurrent Neural Networks in Action, will show us the fundamentals of RNNs with real-life implementation examples. We will also introduce LSTMs and gated recurrent units (GRUs), an extension of RNNs, and take a detailed walk-through of LSTM hyper-parameters. In addition to this, readers will learn how to build a bi-directional RNN model using Keras.

⦁ Chapter 4, Implementing Autoencoders with Keras, will introduce the implementation of various types of autoencoders using the keras library as the backend. Readers will also learn about various applications of autoencoders, such as dimensionality reduction and image coloring.

⦁ Chapter 5, Deep Generative Models, will show us the architecture of another method of deep neural networks, generative adversarial networks (GANs). We will demonstrate how to train a GAN model comprising of two pitting nets—a generator and a discriminator. This chapter also covers the practical implementation of variational autoencoders and compares them with GANs.

⦁ Chapter 6, Handling Big Data Using Large-Scale Deep Learning, contains case studies on highperformance computation involving large datasets utilizing GPUs. Readers will also be introduced to the parallel computation capabilities in R and libraries such as MXNet, which is designed for efficient GPU computing and state-of-the-art deep learning.

⦁ Chapter 7, Working with Text and Audio for NLP, contains case studies on various topics involving sequence data, including natural language processing (NLP) and speech recognition. The readers will implement end-to-end deep learning algorithms using various deep learning libraries.

⦁ Chapter 8, Deep Learning for Computer Vision, will provide end-to-end case studies on object detection and face identification.

⦁ Chapter 9, Implementing Reinforcement Learning, will walk us through the concepts of reinforcement learning step by step. Readers will learn about various methods, such as Markov Decision Processes, Q-Learning, and experience replay, and implement these methods in R using examples. Readers will also implement an end-to-end reinforcement learning example using R packages such as MDPtoolbox and Reinforcementlearning.