Notebooks

This repository contains a series of Jupyter Notebooks I've worked on throughout the years, focusing on AI/ML, data science, computer vision, and NLP.

Machine Learning

Regression

• Univariate Linear Regression Implementation with Batch Gradient Descent from Scratch
• Bivariate Linear Regression Implementation with Mini-Batch Gradient Descent from Scratch

Classification

• Logistic Regression Implementation from Scratch
• Logistic Regression Implementation Using TensorFlow and Keras
• Decision Tree Classification Implementation Using Scikit-Learn
• Random Forest Classification Implementation Using Scikit-Learn
• Naïve Bayes Classification Implementation Using Scikit-Learn
• k-Nearest Neighbors Classification Implementation from Scratch
• Support Vector Machine (SVM) Implementation Using Scikit-Learn

Computer Vision

• Implementing 2-D Convolution from Scratch
• Implementing 2-D Max-Pooling from Scratch
• Visualizing MNIST Digit Averages
• Fine-Tuning DenseNet121 for Image Classification
• MNIST Digit Classification with a Fully-Connected Neural Network
• MNIST Digit Classification with a Convolutional Neural Network (CNN)
• MNIST Fashion Item Classification with a Fully-Connected Neural Network
• MNIST Fashion Item Classification with a Convolutional Neural Network (CNN)

Natural Language Processing (NLP)

• IMDB Sentiment Classification with a Fully-Connected Neural Network

Miscellaneous

• Implementing Matrix Multiplication with NumPy
• Visualizing Bivariate Functions with Matplotlib