https://github.com/moustafamohamed01/dl-projects
A curated collection of deep learning projects showcasing applications of neural networks, CNNs, RNNs, and more, built with TensorFlow, PyTorch, and Python.
https://github.com/moustafamohamed01/dl-projects
ann cnn deep-learning keras python pytorch rnn tensorflow
Last synced: 3 months ago
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A curated collection of deep learning projects showcasing applications of neural networks, CNNs, RNNs, and more, built with TensorFlow, PyTorch, and Python.
- Host: GitHub
- URL: https://github.com/moustafamohamed01/dl-projects
- Owner: MoustafaMohamed01
- Created: 2025-05-24T03:04:43.000Z (about 1 year ago)
- Default Branch: main
- Last Pushed: 2025-06-22T20:48:22.000Z (about 1 year ago)
- Last Synced: 2025-06-22T21:18:09.715Z (about 1 year ago)
- Topics: ann, cnn, deep-learning, keras, python, pytorch, rnn, tensorflow
- Language: Jupyter Notebook
- Homepage:
- Size: 971 KB
- Stars: 0
- Watchers: 0
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# Deep Learning Projects Portfolio
Welcome to my portfolio of deep learning projects, A curated collection of deep learning projects implemented using **TensorFlow**, **Keras**, and **PyTorch**. This repository demonstrates practical applications of neural networks in domains such as image classification, generative modeling, and medical diagnostics, emphasizing clean code, reproducibility, and performance evaluation.
---
## Repository Structure
Each subfolder within this repository contains an independent deep learning project, complete with source code, dataset details, training instructions, and results visualization.
| Project | Framework | Domain | Key Topics |
| --------------------------------------------------------------------------------------------------- | ---------------- | --------------------- | --------------------------------------- |
| [Fruit & Vegetable Image Classification](#1-fruit--vegetable-image-classification) | TensorFlow/Keras | Image Classification | CNNs, Image Processing |
| [Breast Cancer Prediction using Neural Networks](#2-breast-cancer-prediction-using-neural-networks) | PyTorch | Binary Classification | Feedforward Neural Networks, Medical AI |
| [Fashion MNIST Image Classification](#3-fashion-mnist-image-classification) | TensorFlow/Keras | Image Classification | CNNs, Regularization |
| [Face Mask Detection](#4-face-mask-detection) | TensorFlow/Keras | Binary Classification | Real-Time Detection, CNN, OpenCV |
| [MNIST GAN – Digit Generation](#5-mnist-gan--digit-generation) | TensorFlow | Generative Modeling | GANs, Image Synthesis |
| [Anime Face Generator (DCGAN)](#6-anime-face-generator-dcgan) | TensorFlow | Generative Modeling | DCGAN, Image Generation, Anime Faces |
---
## 1. Fruit & Vegetable Image Classification
A convolutional neural network (CNN) designed to classify images of fruits and vegetables.
**Technologies:** TensorFlow, Keras, Python
**Dataset:** [Kaggle - Fruit and Vegetable Image Recognition](https://www.kaggle.com/datasets/kritikseth/fruit-and-vegetable-image-recognition)
### Highlights
* Data preprocessing and augmentation
* CNN architecture with Dropout and MaxPooling
* Real-time prediction support with confidence scores
* Training and validation performance visualization
📁 Folder: [Fruits-and-Vegetables-Image-Recognition-Dataset](./Fruits-and-Vegetables-Image-Recognition-Dataset)
---
## 2. Breast Cancer Prediction using Neural Networks
A binary classification model developed using PyTorch to predict tumor malignancy from the Breast Cancer Wisconsin dataset.
**Technologies:** PyTorch, Scikit-learn, Matplotlib
**Dataset:** [sklearn.datasets.load_breast_cancer](https://scikit-learn.org/stable/modules/generated/sklearn.datasets.load_breast_cancer.html)
### Highlights
* FFNN architecture
* Binary cross-entropy loss with Adam optimizer
* Training curves and evaluation metrics
* Available in Jupyter and standalone script formats
📁 Folder: [breast-cancer-prediction](./breast-cancer-prediction)
---
## 3. Fashion MNIST Image Classification
A CNN-based image classifier trained on Fashion MNIST dataset, classifying clothing items into 10 categories.
**Technologies:** TensorFlow, Keras
**Dataset:** [`tensorflow.keras.datasets.fashion_mnist`](https://www.tensorflow.org/datasets/catalog/fashion_mnist)
### Highlights
* Batch Normalization and Dropout layers
* EarlyStopping and ModelCheckpoint callbacks
* Accuracy up to 91–93% with tuning
* Stylish metric visualizations
📁 Folder: [Fashion-MNIST-Image-Classification](./Fashion-MNIST-Image-Classification)
---
## 4. Face Mask Detection
A real-time CNN classifier that detects whether a person is wearing a face mask or not.
**Technologies:** TensorFlow, Keras, OpenCV, Python
**Dataset:** [Kaggle - Face Mask Dataset](https://www.kaggle.com/datasets/omkargurav/face-mask-dataset)
### Highlights
* Binary classification: With Mask 😷 vs Without Mask 😐
* Data augmentation and preprocessing
* Real-time prediction from user-provided images
* Model saved and reloadable (`.h5`)
* GPU support and training visualizations
📁 Folder: [face-mask-detection](./face-mask-detection)
---
## 5. MNIST GAN – Digit Generation
A Generative Adversarial Network (GAN) that synthesizes realistic handwritten digits from the MNIST dataset.
**Technologies:** TensorFlow, Python
**Dataset:** [`tensorflow.keras.datasets.mnist`](https://www.tensorflow.org/datasets/catalog/mnist)
### Highlights
* Fully functional GAN (Generator + Discriminator)
* Saves generated digit images every epoch
* GAN-stabilization tricks: label smoothing, custom beta values
* Available in both `.py` and `.ipynb` formats
📁 Folder: [mnist-gan](./mnist-gan)
---
## 6. Anime Face Generator (DCGAN)
A Deep Convolutional GAN (DCGAN) trained on anime character faces to generate high-quality synthetic images.
**Technologies:** TensorFlow, Keras, Python
**Dataset:** [Kaggle - Anime Face Dataset](https://www.kaggle.com/splcher/animefacedataset)
### Highlights
* DCGAN architecture with Conv2DTranspose and LeakyReLU
* Trained on 64×64 anime face images
* Outputs generated image grids every 50 epochs
* Modular code: available in both notebook and script formats
* Excellent visual results for anime face synthesis
📁 Folder: [anime-gan](./anime-gan)
---
## Installation & Setup
1. **Clone the Repository**
```bash
git clone https://github.com/MoustafaMohamed01/DL-Projects.git
cd DL-Projects
````
2. **Install Dependencies**
Each project includes a `requirements.txt`. To install dependencies:
```bash
pip install -r requirements.txt
```
3. **Run Projects**
Navigate to the relevant folder and follow its README to train or run inference.
---
## Contributing
Contributions are welcome! If you’d like to improve a project or add a new one:
1. Fork the repository
2. Create a new branch
3. Submit a pull request
Ideas, feedback, and improvements are always appreciated.
---
## Connect With Me
* **LinkedIn:** [Moustafa Mohamed](https://www.linkedin.com/in/moustafamohamed01/)
* **GitHub:** [MoustafaMohamed01](https://github.com/MoustafaMohamed01)
* **Kaggle:** [moustafamohamed01](https://www.kaggle.com/moustafamohamed01)
* **Portfolio:** [moustafamohamed.netlify.app](https://moustafamohamed.netlify.app/)
---