Sparse Autoencoders with K-Means Clustering (MNIST) README.md Content: Project Title: Sparse Autoencoders with K-Means Clustering for MNIST Dataset
Description: Briefly describe the project and its purpose. Explain that it implements sparse autoencoders on the MNIST dataset and performs k-means clustering on the embeddings.
Dependencies: List the dependencies required to run the project, including Python libraries like TensorFlow, NumPy, and scikit-learn.
Installation: Provide instructions on how to install the necessary dependencies.
Usage: use a Google collab to run it and attach MNIST file in "/content/sample_data/Mnist''the file in nin.
Results: Summarize the results, including the accuracy of k-means clustering using the available labels in the MNIST dataset.
License: Specify the license under which the project is distributed (e.g., MIT License).
Author: Ritesh kumar.
Generative Adversarial Network (GAN) with Frey Face Dataset README.md Content: Project Title: Generative Adversarial Network (GAN) with Frey Face Dataset
Description: Provide an overview of the project, mentioning that it implements a GAN on the Frey Face dataset to generate new face images.
Dependencies: List the dependencies required to run the project, such as TensorFlow, NumPy, and Matplotlib.
Installation: Explain how to install the necessary dependencies.
Usage: use a Google collab to run it and attach file in '/content/sample_data/frey_rawface.mat'the file in nin.
Results: Optionally, include any observations or comments on the quality of the generated faces.
License: Specify the project's license.
Author: Provide information about the author or author
****Variational Autoencoders (VAE) with Frey Face Dataset Overview: This project implements Variational Autoencoders (VAE) using the Frey Face dataset. The trained network learns a probabilistic latent space representation of the face images. It allows sampling from the learned distribution by varying different latent variables, demonstrating that the network has learned meaningful latent variables.
Dependencies: TensorFlow NumPy Matplotlib scipy.io Installation: Ensure you have Python installed on your system. You can install the required dependencies using pip:
bash Copy code pip install tensorflow numpy matplotlib scipy Usage: Download the Frey Face dataset from the provided link and place it in the project directory. Usage: use a Google collab to run it and attach file in '/content/sample_data/frey_rawface.mat'the file in nin. After training, sample points from the learned distribution by varying different latent variables to visualize the generated face images. Results: The project demonstrates the effectiveness of Variational Autoencoders in learning a meaningful latent space representation of face images. It showcases the ability to generate new face samples by sampling from the learned distribution and varying different latent variables.
License: This project is licensed under the MIT License.
Author: Ritesh