Hands-On Deep Learning with R: A practical guide to designing, building, and improving neural network models using R

Deep learning becomes easier to reason about when you can follow the model from prepared data to evaluated results. Hands-On Deep Learning with R takes that practical route, introducing core ideas before applying neural networks to problems including image recognition, recommendations, text, and forecasting.

Build from machine-learning foundations

The opening section reviews the groundwork: preparing data, handling missing values, choosing and evaluating models, and improving results. It then introduces R libraries used for deep learning, followed by neural-network fundamentals such as activation functions, feedforward networks, and backpropagation. This progression helps connect the mechanics of a model with the choices involved in putting one together.

Apply neural networks to varied problems

The examples move across several distinct application areas. Explore convolutional neural networks for image recognition, multilayer perceptrons for signal detection, and neural collaborative filtering with embeddings for recommender systems. The book also covers natural language processing, including text preparation, word embeddings, topic grouping, and document summarization.

From model training to evaluation

Rather than stopping at model architecture, the chapters address practical work such as preprocessing data, selecting hidden layers and neurons, training models, evaluating performance, and tuning parameters. Additional applications include stock forecasting with long short-term memory networks, face generation with generative adversarial networks, and reinforcement learning.

Who may find it useful?

This title is aimed at readers with working knowledge of R and some familiarity with machine learning who want to explore deep-learning techniques through concrete examples. Data scientists, machine-learning engineers, and developers can use its application-based structure to compare methods and see how different neural-network approaches are put to work.

Explore deep learning through R

With its movement from fundamentals to varied implementations, this book offers a hands-on way to investigate what neural networks can do—and how to build and assess them in R.