Sai Sriram Yerubandi's repositories
flask-with-database
Flask application with postgres database.
LogisticRegression
Classification Using Logistic Regression by Making a Neural Network Model. This project also includes comparison of Model performance when different regularization techniques are used
Reinforcement-Learning
The aim of the project is to perform Q-learning on a historical stock price for Nvidia for the last 5 years dataset which is reinforcement learning. I have trained the agent by using temporal difference and performed Q - Learning . I was able to successfully construct a Q -Table at the end of training . I Used this Q-table to evaluate the algorithm by considering the initial capital 100000 and Total account value at the end. We can see that we ended up in profits and we can say that our algorithm is good
unsupervised-learning
The aim of the project is to perform K-means clustering on the Cifar 10 dataset which is unsupervised learning. In this phase of the project our aim is to use autoencoders neural network architecture and generate encoded and decoded data. The sparse data generated by encoding part of the neural network model is used to generate clusters by using K-means algorithm. We even check how accurately we are able to generate the input image at the output layer in autoencoder architecture.