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
Applying Activation Functions Layer-wise
Was this section helpful?
- Deep Learning, Ian Goodfellow, Yoshua Bengio, and Aaron Courville, 2016 (MIT Press) - A canonical textbook covering the theoretical foundations and practical applications of deep learning, including comprehensive discussions on activation functions.
- Deep Sparse Rectifier Networks, Xavier Glorot, Antoine Bordes, and Yoshua Bengio, 2011 Proceedings of the Fourteenth International Conference on Artificial Intelligence and Statistics (AISTATS), Vol. 15 (Proceedings of Machine Learning Research (PMLR)) DOI: 10.55986/aistats2011.glorot11a - Introduces the Rectified Linear Unit (ReLU) activation function and demonstrates its benefits for deep neural networks.
- CS231n: Convolutional Neural Networks for Visual Recognition, Neural Networks Part 2: Setting up the Data and the Layers, Stanford University CS231n Course Staff, 2023 - Provides a clear and concise explanation of neural network layers, including activation functions and their role in introducing non-linearity.