Deep Learning Models Exploration

Project Overview

This repository explores various deep learning models and their applications in different tasks, including sentiment analysis, image classification, and natural language processing. The following models and techniques are covered:

• Recurrent Neural Networks (RNNs): Used for sequential data analysis, particularly in time series and natural language processing.
• Long Short-Term Memory (LSTM): A type of RNN designed to learn long-term dependencies, ideal for tasks involving sequences.
• Artificial Neural Networks (ANN): The foundation of many machine learning applications, used for general classification tasks.
• Convolutional Neural Networks (CNNs): Applied for image processing and classification tasks, especially in the context of computer vision.
• Backpropagation: The algorithm used for training neural networks, allowing models to learn from errors.
• Overfitting: Techniques and strategies to prevent overfitting in neural networks and improve generalization.
• Sine Wave Classification: Exploring the ability of models to classify sine wave patterns.
• Animal Classification: Utilizing CNNs for classifying images of animals.
• AlexNet: A landmark architecture in deep learning for image classification tasks.
• Transfer Learning: Leveraging pre-trained models to improve performance on new tasks.
• Review Classification: Applying deep learning techniques to classify text reviews.
• Word Embedding: Techniques for representing words in a continuous vector space for NLP tasks.

Features

• Jupyter notebooks for each model, demonstrating implementation and results.
• Detailed explanations and visualizations of concepts.
• Datasets and resources for training and evaluation.

How to Use

1. Clone the repository:

   git clone https://github.com/karampanah927/DeepLearningModelsExploration.git
   cd DeepLearningModelsExploration

2. Install dependencies

  pip install -r requirements.txt

3. Open Jupyter Notebooks

  jupyter notebook

Contributing

Contributions are welcome! If you have ideas for new models, improvements, or additional datasets, feel free to open an issue or submit a pull request.