https://github.com/ChitrangiBhatnagar/deep_learning
This repository contains multiple deep learning projects covering natural language processing (NLP), computer vision, audio processing, and object detection using TensorFlow/Keras and PyTorch.
https://github.com/ChitrangiBhatnagar/deep_learning
deep deeplearning keras-tensorflow machine-learning pytorch tensorflow
Last synced: 6 months ago
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This repository contains multiple deep learning projects covering natural language processing (NLP), computer vision, audio processing, and object detection using TensorFlow/Keras and PyTorch.
- Host: GitHub
- URL: https://github.com/ChitrangiBhatnagar/deep_learning
- Owner: kirazoro
- Created: 2025-02-28T06:34:05.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2025-03-09T12:52:22.000Z (over 1 year ago)
- Last Synced: 2025-03-09T13:33:07.263Z (over 1 year ago)
- Topics: deep, deeplearning, keras-tensorflow, machine-learning, pytorch, tensorflow
- Language: Python
- Homepage:
- Size: 7.81 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# Deep Learning Projects Repository
Welcome to the **Deep Learning Projects Repository**! This repository contains multiple deep learning projects covering **natural language processing (NLP)**, **computer vision**, **audio processing**, and **object detection** using **TensorFlow/Keras** and **PyTorch**.
## Table of Contents
- [Introduction](#introduction)
- [Projects](#projects)
- [Installation](#installation)
- [Usage](#usage)
- [Dataset Sources](#dataset-sources)
- [Contributing](#contributing)
## Introduction
This repository contains various deep learning applications, including **text classification, image classification, object detection, and audio processing**. Each project includes a **Python implementation**, dataset usage, and model training/evaluation.
## Projects
### 1. Fake News Detection (NLP - LSTM)
- **Task:** Classify news articles as **real or fake** using LSTMs.
- **Tech Stack:** TensorFlow/Keras, NLP, LSTM.
- **Dataset:** Fake News dataset (CSV format with text and labels).
### 2. Image-Based Disease Diagnosis (CNN for X-ray Classification)
- **Task:** Classify chest X-ray images as **normal or pneumonia**.
- **Tech Stack:** TensorFlow/Keras, CNN, Image Processing.
- **Dataset:** Chest X-ray dataset (image dataset with binary classification).
### 3. Handwritten Digit Recognition (CNN for MNIST)
- **Task:** Recognize digits (0-9) from the **MNIST dataset**.
- **Tech Stack:** TensorFlow/Keras, CNN.
- **Dataset:** MNIST (28x28 grayscale images).
### 4. Music Genre Classification (CNN for Spectrograms)
- **Task:** Classify music files into different **genres**.
- **Tech Stack:** TensorFlow/Keras, CNN, Spectrogram Processing.
- **Dataset:** GTZAN music dataset (spectrogram images).
### 5. Object Detection for Autonomous Driving (YOLOv5)
- **Task:** Detect objects in images/videos using **YOLOv5**.
- **Tech Stack:** PyTorch, YOLOv5.
- **Dataset:** COCO dataset / Custom dataset.
## Installation
### **Prerequisites**
Ensure you have the following installed:
- Python 3.8+
- TensorFlow/Keras
- PyTorch (for YOLOv5)
- OpenCV, Pandas, NumPy
### **Setup**
Clone the repository:
```sh
$ git clone https://github.com/your-username/deep-learning-projects.git
$ cd deep-learning-projects
```
Install required dependencies:
```sh
$ pip install -r requirements.txt
```
## Usage
Each project is in its respective folder. Navigate to the folder and run the script.
Example (Run Fake News Detection):
```sh
$ cd fake-news-detection
$ python fake_news_classifier.py
```
## Dataset Sources
- Fake News Dataset: [Kaggle](https://www.kaggle.com/clmentbisaillon/fake-and-real-news-dataset)
- Chest X-ray Dataset: [NIH Dataset](https://www.kaggle.com/paultimothymooney/chest-xray-pneumonia)
- MNIST Handwritten Digits: [TensorFlow Dataset](https://www.tensorflow.org/datasets/catalog/mnist)
- GTZAN Music Dataset: [Marsyas](http://marsyas.info/downloads/datasets.html)
- COCO Dataset for Object Detection: [COCO](https://cocodataset.org/)
## Contributing
Contributions are welcome! Feel free to fork the repository and submit a pull request.
---
Happy Coding! 🚀