Neural Networks Fundamentals: Neurons and Layers in R

סקירה כללית

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.

סילבוס

• Unit 1: Neural Networks Fundamentals: Neurons and Layers (R Edition)
Neural Networks Fundamentals Quiz
• Unit 2: Implementing an Artificial Neuron from Scratch in R
Initializing a Neuron in R
Adding Input Validation to a Neuron's Forward Function
Implementing the Forward Pass for an Artificial Neuron in R
Using a Neuron to Process Data in R
• Unit 3: Adding Activation Functions to Our Neuron (R Edition)
Applying the Sigmoid Activation Function to a Neuron
Fixing the Order of Operations in a Neuron's Forward Pass
Implementing the Sigmoid Activation Function in R
• Unit 4: Building a Dense Layer in Neural Networks with R
Initializing Small Random Weights in a Dense Layer
Fixing the Biases Vector in a Dense Layer
Initializing Weights and Biases for a Dense Layer in R
Creating and Inspecting Dense Layers in R
Counting Parameters in a Dense Layer
• Unit 5: Forward Propagation in Dense Layers with R
Implementing Bias Addition in Forward Propagation for a Dense Layer
Fix Matrix Multiplication in Forward Propagation
Completing the Forward Pass with Sigmoid Activation
Implementing Forward Propagation in a Dense Layer with R
Implementing Forward Propagation with a Dense Layer in R

---

נושאים

Artificial Intelligence