This project was completed as a part of the Honors portion of the Neural Networks and Deep Learning Course on Coursera.
Credit to DeepLearning.AI and the Coursera platform for providing the course materials and guidance.
This report details the development of a straightforward image recognition algorithm aimed at accurately classifying pictures as either cat or non-cat. The dataset provided, named "data.h5," comprises two sets: a training set with m_train images labeled as cat (y=1) or non-cat (y=0), and a test set with m_test images similarly labeled.
Each image in the dataset has a shape of (num_px, num_px, 3), where the number 3 represents the three channels (RGB). This means that each image is square, with the height and width both equal to num_px.
Throughout this project, I successfully constructed the image-recognition algorithm, enabling it to classify images with impressive accuracy, distinguishing between cat and non-cat images effectively. The methodology involved in this process, along with the results achieved, demonstrates the algorithm's capability and showcases my proficiency in image recognition techniques.
