An implementation of the paper "Reinforcement Learning for Solving the Vehicle Routing Problem" by Nazari (et al) presented at NIPS 2018. The paper introduces an deep reinforcement learning algorithm to solve the VRP using the actor critic algorithm. This implementations uses python and the Pytorch framework .

An end-to-end VQA system implemented using the Keras framework.Visual Question Answering (VQA) is a challenge which requires high-level scene interpretation from images combined with language modelling of relevant QA. Given an image and a natural language question about the image, the task is to provide an accurate natural language answer. This is an implementation of the model proposed by the original VQA paper by Agrawal et. al.

An implementation of the paper "Learning a Probabilistic Latent Space of Object Shapes via 3D Generative Adversial Modelling" by Wu (et al) presented at NIPS 2016. The paper introduces 3D GANs, which leverages volumetric convolutional networks and vanilla GANs to produce 3D objects from a probabilistic space. This implementations uses python and the Keras framework to build the 3D GAN Architecture.

An implementation of neural architecture search using the REINFORCE algorithm.A recurrent network is used to generate the model descriptions of neural networks. This RNN is trained with reinforcement learning to maximize the expected accuracy of the generated architectures on a validation set. This algorithm is tested on the CIFAR-10 dataset. The project is inspired from the work presented in the paper "Neural Architecture Search with Reinforcement Learning" by Barret et al from Google Brain.

An implementation of a stock trading bot using an Actor Critic algorithm. The trading environment is converted into an MDP with the state being the stock values and actions of the agent is to either HOLD, SELL or BUY. The agent is trained to maximize the overall revenue in the simulated Trading environment.

This project aims to perform reliable facial recognition using minimal training data. An example of few-shot learning, this algorithm is able to distinguish between faces by using a dataset having only 10 instances of each face. This project uses Siamese networks to compute the similarity between 2 facial images and classify them accordingly. The algorithm implemented can be easily extended to many real world use cases like signature verification, text similarity with only minimal data points.

An python implementation of a Automated Image Captioning with Visual Attention. The Image Captioning is done through an encoder-decoder architecture where the encoder is a deep CNN and the decoder is a RNN unit. A time based visual attention as described in neural machine translation systems is used by the RNN to produce very realistic captions for the images. This work is inspired by the paper " Show, Attend and Tell: Neural Image Caption Generation with Visual Attention" by Xu et. al.