Build a Neural Network from Scratch in R

Overview

Go beyond black boxes! Build Neural Networks from scratch with R. Master every detail, from neurons to backpropagation, and apply your own library to real-world ML challenges.

Syllabus

Course 1: Neural Networks Fundamentals: Neurons and Layers in R
Course 2: The MLP Architecture: Activations & Initialization in R
Course 3: Training Neural Networks: the Backpropagation Algorithm
Course 4: Building and Applying Your Neural Network Library

Courses

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2 hours
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This course introduces the core building blocks of neural networks. You'll learn what a neuron is, how it processes information, the role of activation functions, and how neurons are organized into layers. By the end, you'll implement a single dense layer from scratch using R and base R matrix operations.
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2 hours
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This course builds upon single layers to construct a complete Multi-Layer Perceptron (MLP). You'll learn to stack layers, explore different activation functions like ReLU and Softmax, and understand the importance of weight initialization for effective training.
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3 hours
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This course dives into how neural networks learn from data. You'll implement loss functions to measure prediction errors, understand the intuition and mechanics of gradient descent, master the backpropagation algorithm to calculate gradients, and use an optimizer to update network weights.
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2 hours
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This course focuses on transforming your code into a reusable R package and applying it to a real-world problem. You'll refactor your existing components into a structured package, build a Model R6 class for easier network definition and training, and finally, train your neural network on the Boston Housing dataset for a regression task.