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
Hyperparameter Tuning Strategies
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- Deep Learning, Ian Goodfellow, Yoshua Bengio, Aaron Courville, 2016 (MIT Press) - Provides a foundational and comprehensive treatment of neural networks, including detailed theoretical and practical discussions on hyperparameters, optimization methods, and training strategies.
- Random Search for Hyper-Parameter Optimization, James Bergstra, Yoshua Bengio, 2012 Journal of Machine Learning Research, Vol. 13 (Journal of Machine Learning Research) DOI: 10.5555/2188385.2188395 - Introduces Random Search and demonstrates its efficiency over Grid Search for hyperparameter optimization, particularly when not all hyperparameters are equally important.
- Practical Bayesian Optimization of Machine Learning Algorithms, Jasper Snoek, Hugo Larochelle, Ryan P. Adams, 2012 Advances in Neural Information Processing Systems (NIPS) 25 (Elsevier B.V.) DOI: 10.5555/2999134.2999201 - A seminal work on applying Bayesian optimization for tuning machine learning hyperparameters, offering a more intelligent approach than exhaustive or random searches.
- Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems, Aurélien Géron, 2022 (O'Reilly Media) - A practical guide covering hyperparameter tuning strategies like Grid Search and Random Search, along with implementation details using popular deep learning frameworks. (4th edition)
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