Skip to content

README.md

Latest commit

 

History

History
27 lines (19 loc) · 1.24 KB

README.md

File metadata and controls

27 lines (19 loc) · 1.24 KB

Deep Learning Projects

This repository contains Jupyter notebooks showcasing different deep learning experiments and models. It covers preprocessing steps, artificial neural networks (ANN), convolutional neural networks (CNN), and hyperparameter tuning.

📌 Notebooks

1. Hyperparameter Tuning (Hyperparametertunig.ipynb)

  • Focused on tuning model hyperparameters for better performance.
  • Demonstrates techniques like grid search, random search, and manual tuning.

2. Artificial Neural Networks (ANN) (ANN.ipynb)

  • Title: Preprocessing → Array to Tabular Data Form
  • Covers preprocessing techniques to convert raw data into model-friendly formats.
  • Builds and trains ANN models for classification/regression tasks.

3. Convolutional Neural Networks (CNN) (CNN.ipynb)

  • Title: Model Building
  • Focuses on CNN architecture for image-related tasks.
  • Explains layers like convolution, pooling, activation, and fully connected layers.
  • Trains CNN on sample datasets to demonstrate image classification.

⚙️ Requirements

Make sure you have the following dependencies installed before running the notebooks:

pip install numpy pandas matplotlib seaborn scikit-learn tensorflow keras