California State University, Fresno
Student: Noah Wiley
This repository contains my coursework, notebooks, experiments, and projects for CSCI 167 – Introduction to Deep Learning at Fresno State.
The purpose of this repository is to document my progress, experiments, and implementations throughout the semester, focusing on neural networks and modern deep learning architectures.
CSCI 167 introduces the fundamental principles of machine learning and deep learning, including:
- Machine learning fundamentals
- Logistic regression
- Neural networks and backpropagation
- Vanishing / diminishing gradients
- Optimization techniques and normalization
- Batch normalization
- Residual Networks (ResNets)
- Convolutional Neural Networks (CNNs)
- Recurrent Neural Networks (RNNs)
- Experimental evaluation of deep learning models
The course emphasizes both theoretical understanding and hands-on experimentation using modern deep learning frameworks.
CSCI167/
│
├── notebooks/ # Programming Assignments (Jupyter Notebooks)
├── projects/ # Course Projects
├── data/ # Datasets used in assignments and projects
├── models/ # Saved models and checkpoints
└── README.md # Repository Documentation
└── csci167 Textbook - Mastering PyTorch
└── csci167 Textbook - Understanding Deep Learning.pdf
This repository may include:
- Python
- Jupyter Notebook
- NumPy
- Matplotlib / Seaborn
- PyTorch and/or TensorFlow
- scikit-learn
Throughout this course, I am developing skills in:
- Designing and training neural networks
- Implementing forward and backward propagation
- Applying optimization algorithms
- Handling overfitting and regularization
- Building CNN and RNN architectures
- Model evaluation and performance tuning
- Experimental analysis of deep learning systems
Most assignments are completed using Jupyter Notebook.
To run locally:
pip install -r requirements.txt
jupyter notebook
Some experiments may require GPU acceleration for training deep learning models.
- This repository is for academic coursework.
- Code is written for learning and experimentation.
- Large datasets and trained model files may not be included due to size limitations.
All work in this repository is my own unless otherwise stated.
This repository is maintained for academic and portfolio documentation purposes only.