LibGuides: CSC453: Module 1 Resources

Module 1 Required Resources

Neural Network Design (2nd ed., 2014)
Hagan, M. T., Demuth, H. B., Beale, M. H., & De Jesús, O. (2014). Neural network design (2nd ed.). Martin Hagan Publishing.
These chapters build core vocabulary and mental models for neuron components, activation functions, and basic feedforward architectures, then introduce the perceptron learning rule and its limits. The readings align with Module 1 MLOs and prepare learners for the Week 1 quiz and the mini MLP assignment.
Chapter 1 – Introduction
Chapter 2 – Neuron Model & Network Architectures
Chapter 3 – Illustrative Example
Chapter 4 – Perceptron Learning Rule

TensorFlow Playground
TensorFlow. (n.d.). TensorFlow Playground. https://playground.tensorflow.org
An interactive visualization tool for experimenting with neural networks right in your browser. Great for getting a hands-on feel for how network architecture and parameters affect learning.
Neural Networks and Deep Learning (Ch. 1 to 3)
Nielsen, M. (n.d.). Neural networks and deep learning (Chs. 1–3). http://neuralnetworksanddeeplearning.com
A beginner-friendly online book introducing the core concepts behind neural networks, including perceptrons, backpropagation, and gradient descent.
scikit-learn User Guide: MLPClassifier
Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., ... Duchesnay, É. (2011). Scikit-learn: Machine learning in Python. Journal of Machine Learning Research, 12, 2825–2830. https://scikit-learn.org/stable/modules/generated/sklearn.neural_network.MLPClassifier.html
Documentation page for the MLPClassifier in scikit-learn, explaining how to use neural networks for classification tasks with this powerful Python library.