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CodeSignal

Neural Networks Fundamentals: Neurons and Layers in R

via CodeSignal

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Overview

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.

Syllabus

  • 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

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