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
Handling Categorical Data: Encoding Techniques
Was this section helpful?
- 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 data preprocessing techniques, including categorical feature encoding, suitable for an introduction to machine learning and deep learning with common libraries.
- sklearn.preprocessing.OneHotEncoder, scikit-learn developers, 2024 - Official documentation explaining the principles and usage of one-hot encoding for categorical features, including practical examples with the scikit-learn library.
- Deep Learning, Ian Goodfellow, Yoshua Bengio, and Aaron Courville, 2016 (MIT Press) - A foundational textbook providing a comprehensive theoretical background on deep learning, including discussions on distributed representations (embeddings) and their role in handling high-dimensional data.
- Feature Hashing for Large Scale Multitask Learning, Kilian Q. Weinberger, Anirban Dasgupta, John Langford, Alex Smola, Tony Attwood, 2009 Proceedings of the 26th International Conference on Machine Learning (ICML), Vol. 5 (ACM) DOI: 10.1145/1553374.1553516 - A seminal paper introducing feature hashing (the hashing trick) as an efficient technique for handling high-dimensional categorical features, especially in large-scale machine learning problems.