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
Learning Rate and Its Importance
Was this section helpful?
- Deep Learning, Ian Goodfellow, Yoshua Bengio, and Aaron Courville, 2016 (MIT Press) - This foundational textbook for deep learning offers comprehensive coverage of optimization algorithms, including the critical role of the learning rate in gradient descent.
- CS231n: Convolutional Neural Networks for Visual Recognition - Optimization, Stanford University, 2023 (Stanford University) - Provides excellent, accessible explanations of optimization algorithms, including detailed discussions and visualizations of the learning rate's impact and strategies for its adjustment in neural network training.
- Adam: A Method for Stochastic Optimization, Diederik P. Kingma and Jimmy Ba, 2015 3rd International Conference for Learning Representations DOI: 10.48550/arXiv.1412.6980 - Introduces Adam, a widely used adaptive learning rate optimizer. This paper highlights the difficulties of setting a fixed learning rate and motivates adaptive approaches, which the section briefly mentions.