Deep Neural Network for Image Classification

This project implements a deep L-layer neural network for image classification tasks, specifically focusing on a "cat vs. non-cat" classification problem. It demonstrates key techniques in deep learning, such as building and training multi-layer neural networks, implementing preprocessing pipelines, and visualizing results.

Features

• L-Layer Neural Network Architecture: Build and train a deep neural network using modular and reusable functions.
• Data Preprocessing: Efficient normalization and reshaping of image data for training.
• Training and Optimization: Gradient descent optimization techniques applied to minimize loss.
• Visualization: Clear visualizations of learning curves, predictions, and performance metrics using Matplotlib.

Project Structure

• Model Development:
  The deep learning model is implemented with reusable functions for forward propagation, backward propagation, and parameter updates. The architecture includes:
  • A 2-layer neural network
  • An L-layer deep neural network
• Dataset Processing:
  The dataset includes labeled images for binary classification. Preprocessing steps include resizing and normalizing pixel values.
• Results Analysis:
  After training, the model's predictions are compared to ground truth labels, and results are analyzed using visualization techniques.

Key Libraries

The project makes use of the following libraries:

• numpy: For numerical computations and data manipulation.
• matplotlib: For creating visualizations of the training process and results.
• scipy: For advanced mathematical operations.

Setup and Requirements

To run the project, ensure you have Python 3.x installed along with the required libraries. Install the dependencies using the following command:

pip install numpy matplotlib scipy