This repository is dedicated to essential deep learning experiments and implementations. The goal is to provide a comprehensive guide and reference for various deep learning tasks using different architectures and techniques.
- Train a deep neural network for an image classification task.
- Apply convolutional neural network for the same image classification dataset and compare DNN and CNN in terms of parameters and performance.
- Construct an object detector using a convolutional neural network.
- Develop an image segmentation model using a fully convolutional network.
- Demonstrate the use of an autoencoder for dimensionality reduction.
- Train a convolutional autoencoder for image reconstruction.
- Apply a denoising autoencoder for noise removal and obtain clean images.
- Implement image captioning using a recurrent neural network.
- Design an LSTM-based handwriting recognition model.
- Compare the performance of RNN, LSTM, and GRU in the prediction of time series data.
- Train a generative adversarial network with a sample image dataset and analyze the generated images.
- Implement a deep reinforcement learning algorithm for dynamic prediction.
To get started with any of the experiments, follow the instructions below:
- Python 3.7 or higher
- TensorFlow 2.x or PyTorch
- NumPy
- Pandas
- Matplotlib
- Jupyter Notebook
You can install the required packages using:
pip install -r requirements.txtEach experiment is located in its own directory and includes a Jupyter Notebook that demonstrates the implementation and results. To run an experiment, navigate to the respective directory and open the Jupyter Notebook.
cd experiment-directory
jupyter notebookFeel free to fork this repository, make improvements, and submit a pull request. Contributions are welcome!
This project is licensed under the BSD-3 License - see the LICENSE file for details.
- Deep learning frameworks like TensorFlow and PyTorch
- Open-source datasets used for the experiments
Happy experimenting!