I am a machine learning and artificial intelligence enthusiast. I am more intereseted in AI applications and want to be a AI developer in the future which would help me to increase my knowledge in this field.
I am a pursuing MS in CS from USC Viterbi School of Engineering. Before this I worked as Associate Data Scientist in Cognizant.
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Implemented classification, regression, clustering algorithms along with metrics, preprocessing helper functions from scratch using numpy to get the better understanding of algorithms.
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Implemented U-Net from the paper "U-Net: Convolutional Networks for Biomedical Image Segmentation" to segment brain tumors in given MRI images of the brain.
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Trained an LSTM based seq2seq model to tag all words of the paragraph to their Named Entity or Part of Speech. Used Flask and docker to deploy the model.
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Implementation of 'merge' architecture for generating image captions from paper "What is the Role of Recurrent Neural Networks (RNNs) in an Image Caption Generator?" using Keras.
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This project is inspired by https://thispersondoesnotexist.com. I trained a Deep Convolution Generative Adversarial Network on 100k celebrities photos. After training, the GAN generator were able to generate faces. View Project
This is a kaggle Challenge. Given a pair of faces we have to determine whether they are related or not. Here I have used a Siamese network over VGG-facenet to tackle this problem.
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A Reinforcement Learning agent trained using Deep Q Learning on a Dueling Network Architecture with Prioritized Experience Replay to play Flappy Bird Game, implemented using Pytorch. View Project
This is a Kaggle competition on Image Segmentation. We have to identify pixels in seismic images with salt sediment present. Here I have used UNet (Encoder and Decoder) to tackle the challenge. View Project
A Face Recognition Siamese Network implemented using Keras. Siamese Network is used for one shot learning which do not require extensive training samples for image recognition. View Project
Attention model is one of the most sophisticated sequence to sequence models which is used for different tasks such as Machine Translation, Image Captioning etc. View Blog
Here we will read about the state of the art Proximal Policy Optimization Algorithm which has become the new industry standard since its release by the OpenAI. View Blog
Lets discuss 'merge' architecture to generate Image Caption which is different from the tradtional view of Image Captioning where image features are 'injected' in RNN. View Blog
Here we will see some drawbacks of Deep Q learning and will come up with the new approach known as Policy Gradient method. Later we will improve this to get A2C Algotirhm. View Blog
Here we will use Deep Neural Network to perform Q-Learning and will build an agent which will be able to play Flappy Bird game. We will also discuss some improvements in DQN. View Blog
In reinforcement learning, we always do not have complete knowledge of MDP environment, there are different techniques to learn in unknown environment. Let's discuss them. View Blog
In reinforcement learning, an agent has to follow an optimal policy, there are various techniques to optimize a policy in known MDP environment. Let's discuss few of them. View Blog
In NLP application, we have to convert word into some sort of numerical representation. There are various ways to do it. It is called Word Embedding. Lets explore it. View Blog
Long Short Term Memory networks – usually just called “LSTMs” – are a special kind of RNN, capable of learning long-term dependencies. Let's demystify it. View Blog
Los Angeles, California.
Bengaluru, Karnataka, India.
Pune, Maharashtra, India.