Deep_learning

This repository contains a collection of deep learning projects that explore various machine learning models and techniques. The current projects focus on sentiment analysis using Hugging Face's Transformers and image classification tasks.

Table of Contents

• Overview
• Projects
  • Sentiment Analysis with Hugging Face
  • Image Classifier
• Installation
• Usage
• Contact

Overview

This repository is structured into several independent deep learning projects. Each project is self-contained with its own set of scripts, models, and datasets. The current projects are:

1. Sentiment Analysis with Hugging Face: A sentiment analysis model fine-tuned on text data using a pre-trained BERT model.
2. Image Classifier: Projects focusing on classifying images from different datasets, including CIFAR-10 and sc-islet images.

Projects

Sentiment Analysis with Hugging Face

• Folder: Sentiment_analysis_huggingface
• Description: This project uses Hugging Face's Transformers to fine-tune a BERT model for sentiment analysis, classifying text data into positive, negative, or neutral sentiments.
• Key Files:
  • huggingface_sentiment_analysis.ipynb: The main notebook containing all the code for the sentiment analysis task.

Image Classifier

This section contains two sub-projects:

1. CIFAR-10 Image Classifier

   • Folder: Image_classifier/Cifar10
   • Description: A convolutional neural network (CNN) model for classifying images in the CIFAR-10 dataset.
   • Key Files:
     • Torch_image_classification.ipynb: The main notebook containing the code for CIFAR-10 image classification.

2. sc-islet Functionality Classifier

   • Folder: Image_classifier/sc-islet_classifier
   • Description: A model that classifies iPSC-derived sc-islets into functional or non-functional categories based on brightfield images using a Transformer-based model.
   • Key Files:
     • transformer_classifier.ipynb: The main notebook containing the code for training the model.
     • streamlit_app.py: A Streamlit app for predicting sc-islet functionality from uploaded images.

Installation

General Requirements

• Python 3.x
• Additional dependencies as listed in the respective project folders.

Usage

Each project has its own usage instructions, detailed in the respective README.md files located in each project's folder. Generally, you'll need to: Clone the repository:

git clone https://github.com/iichelhadi/Deep_learning.git
cd Deep_learning

Navigate to the specific project folder you are interested in and follow the instructions in the README.md of that folder.

You can install the general requirements using pip:

pip install torch torchvision transformers pandas numpy matplotlib pillow streamlit notebook

Contact

For any questions or suggestions, feel free to reach out:

Name: Elhadi Iich Email: [email protected]