Introduction to Neural Networks
Chapter 1: Foundations of Neural Networks
From Biological to Artificial Neurons
Weights and Biases: The Network's Parameters
Activation Functions: Introducing Non-Linearity
Structuring Networks: Layers and Connections
A Simple Feedforward Network Example
Practice: Calculating Neuron Output
Quiz for Chapter 1
Chapter 2: Preparing Data for Neural Networks
Understanding Input Data Representation
Feature Scaling: Normalization and Standardization
Handling Categorical Data: Encoding Techniques
Creating Data Batches for Training
Splitting Data: Training, Validation, and Test Sets
Hands-on Practical: Preprocessing Sample Data
Quiz for Chapter 2
Chapter 3: Forward Propagation: Generating Predictions
The Flow of Information in a Network
Linear Transformation: Weighted Sum Calculation
Applying Activation Functions Layer-wise
Matrix Operations for Efficient Computation
Calculating the Final Output Prediction
Hands-on Practical: Implementing Forward Propagation
Quiz for Chapter 3
Chapter 4: Training: Backpropagation and Gradient Descent
Measuring Performance: Loss Functions
The Concept of Gradient Descent
Backpropagation: Calculating Gradients Efficiently
Updating Weights and Biases
Learning Rate and Its Importance
Stochastic Gradient Descent (SGD) and Variants
Practice: Calculating Gradients Manually
Quiz for Chapter 4
Chapter 5: Building and Training a Basic Neural Network
Setting up the Network Architecture
Initializing Weights and Biases
The Training Loop Structure
Implementing the Training Step
Monitoring Training Progress
Introduction to Deep Learning Frameworks (TensorFlow/PyTorch)
Hands-on Practical: Training a Simple Classifier
Quiz for Chapter 5
Chapter 6: Improving Network Performance and Generalization
Understanding Overfitting and Underfitting
The Role of Validation Sets
Regularization Techniques: L1 and L2
Dropout: Randomly Deactivating Neurons
Early Stopping: Halting Training Optimally
Hyperparameter Tuning Strategies
Practice: Applying Regularization
Quiz for Chapter 6
Backpropagation: Calculating Gradients Efficiently
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Sep 5, 2025
Updated explanation to be 'simpler'.
- Learning representations by back-propagating errors, David E. Rumelhart, Geoffrey E. Hinton, Ronald J. Williams, 1986 Nature, Vol. 323 DOI: 10.1038/323533a0 - This seminal paper popularized the backpropagation algorithm for training multi-layer neural networks.
- Neural Networks and Deep Learning, Michael Nielsen, 2019 - An accessible online book providing an intuitive and step-by-step explanation of the backpropagation algorithm, suitable for beginners.
- CS231n Convolutional Neural Networks for Visual Recognition: Optimization (Part 2), Andrej Karpathy, Justin Johnson, Fei-Fei Li (Stanford CS231n Course Staff), 2023 - Lecture notes offering a clear and concise explanation of backpropagation, emphasizing gradient flow and practical implementation details.