Deep Learning with R: Building and Predicting Neural Networks

Deep learning is driving advancements in artificial intelligence, but you do not need a complex setup to start building your own models. By leveraging the power of R, you can design, train, and deploy neural networks using clean, readable code. This text-based course guides you from the fundamental principles of neural networks to building and evaluating your own deep learning models. You will learn how to preprocess data, configure model layers, and make accurate predictions on real-world datasets. What you'll learn: Understand the core mathematical concepts behind neural networks and deep learning; Prepare and preprocess structured data using modern R data manipulation packages; Build and compile neural network architectures using the R interface for Keras; Train deep learning models and fine-tune hyperparameters to prevent overfitting; Evaluate model performance using key metrics and visualization techniques in R; Apply trained neural networks to generate predictions on new datasets. You will start with foundational definitions and key deep learning terminology before moving on to practical step-by-step code implementations. Through written explanations and clear code snippets, you will progress from data preparation to model deployment. Designed for beginners in data science and R programming, this course requires no prior experience with deep learning or advanced mathematics. Start reading today to unlock the potential of deep learning using R.