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David Nallapu's Projects

cnndogbreedsclassification icon cnndogbreedsclassification

This study aims to classify dog breeds. Hyperparameter tuning will be done to understand the performance of the hyperparameters. This study looks at the impact of changing the actiovation function, cost function, no. of epochs, gradient estimation, network architecture, network initialization. We will see loss, accuracy and other metrics on TensorBoard to understand performance . We will also see that certain paramerter tuning causes a dramatic difference. The findings from this study indicate that it is important to do hyper parameter tuning for Deep Learning with CNNs.

dutchauctiondapp icon dutchauctiondapp

ReactUI Solidity Hardhat Dapp of a Dutch Auction deployed on IPFS with MetaMask wallet

ecell icon ecell

A web-app that I made for E-Cell SNU. Implemented with Angular 8 and Firebase.

face-detector icon face-detector

Detects faces on input of image. Image input is done by a dialog box.

graphtheory_lesmis icon graphtheory_lesmis

Applying Graph Theory to the the weighted network of co appearances of characters in Victor Hugo's novel "Les Miserables". Finding the dominant eigenvector to find the most "popular" characters or important nodes.

ml-classification-models icon ml-classification-models

Plots the graph of a few ML Classification models like Logistic Regression, KNN, SVM, Naive Byes, Decision Trees, Random Forest. Sample data uploaded.

multiarmedbanditproblem icon multiarmedbanditproblem

The multi-armed bandit problem is to maximize our reward by balancing exploration and exploitation of allocating limited resources to competing choices. This study aims to understand and solve the multi-armed bandit problem with Thompson Sampling, UCB and 𝜖 -greedy. Random Sampling will also be used to create a benchmark to understand the performance of the other algorithm. We will see the advantages and disadvantages of various algorithms. The findings from this study indicate that it is important to compare various algorithms for the multi-armed bandit problem as they perform differently in varied environments.

nlp-on-reviews- icon nlp-on-reviews-

This script does Natural Language Processing on restaurant reviews to tell if the customer liked/disliked the restaurant. Classification done with Naive Byes.

qlearningcartpoleopenaigym icon qlearningcartpoleopenaigym

Q-learning is a model-free reinforcement learning algorithm to learn quality of actions telling an agent what action to take under what circumstances. This study aims to simulate the environment for Q-Learning by importing it from OpenAI gym and to maximize the agent's reward. Baseline hyperparameters will also be used to create a benchmark to understand the performance of the hyperparameter tuning. This study looks at the impact of changing the learning rate, discount rate, epsilon value, and epsilon decay rate. We will see avergae score metrics and a rendered environment to understand how well the agent is performing. We will also see that certain paramerter tuning causes a dramatic difference. The findings from this study indicate that it is important to do hyper parameter tuning for a reinforcement learning environment.

regression-model-comparison icon regression-model-comparison

This script plots a graph with SVR, Decision Tree Regression and Random Forest Regression models working on a sample data. Sample data has been uploaded.

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