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 Python and NumPy.
Overview
Syllabus
- Unit 1: Neural Networks Fundamentals
- Neural Network Fundamentals Quiz Application
- Unit 2: Building a Simple Neuron
- Initialize Neuron Parameters
- Adding Input Validation to Your Neuron
- Implementing the Forward Pass Calculation
- Testing Your First Neuron Implementation
- Unit 3: Activation Functions in Neurons
- Applying Sigmoid Activation to Complete the Neuron Forward Pass
- Fix the Order of Operations in Neuron Forward Pass
- Implementing the Sigmoid Activation Function
- Unit 4: Building Dense Neural Layers
- Scaling Down Random Weight Initialization in Dense Layer
- Fix Dense Layer Bias Initialization
- Initialize Dense Layer Weights and Biases
- Creating and Inspecting Dense Layer Objects
- Counting Parameters in a Dense Layer
- Unit 5: Forward Propagation in Layers
- Complete the Bias Addition in Dense Layer Forward Pass
- Fix the Matrix Multiplication in Forward Propagation
- Complete the Forward Pass with Sigmoid Activation
- Implementing Forward Propagation in a Dense Neural Network Layer
- Implementing Forward Propagation in a Dense Layer
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