Amazon Fashion Recommender Used Amazon Product Advertising API to gather data locally and used those files for Recommendation
Used Two Approaches Natural Language Processing on Product Description Convolutional Neural Network on Product Images Look at the Notebooks for more details
Personalized Medicine Redefining Cancer Treatment A lot has been said during the past several years about how precision medicine and, more concretely, how genetic testing is going to disrupt the way diseases like cancer are treated.
This repository contains my work for Udacity’s Deep Reinforcement Learning Nanodegree For this project, we will work with the Tennis environment.
In this environment, two agents control rackets to bounce a ball over a net.
This project repository contains my work for the Udacity’s Deep Reinforcement Learning Nanodegree Project 2: Continuous Control.
Project’s goal In this environment, a double-jointed arm can move to target locations. A reward of +0.
Abstract This project discuss the transferability of state of the art defense techniques for adversarial examples for deep learning systems in the physical domain. The paper explores using adversarial attacks using the Fast Gradient Sign Method (FGSM), Carlini & Wagner (CW) and DeepFool attacks to generate adversarial images that are given to the classifier as a digital and physically transformed image.
Social network Graph Link Prediction - Facebook Challenge Problem statement: Given a directed social graph, have to predict missing links to recommend users (Link Prediction in graph)
Data Overview Taken data from facebook’s recruting challenge on kaggle https://www.
Problem Description: Clickthrough rate (CTR) __ is a ratio showing how often people who see your ad end up clicking it. Clickthrough rate (CTR) can be used to gauge how well your keywords and ads are performing.
This project is to build a model that predicts the human activities such as Walking, Walking_Upstairs, Walking_Downstairs, Sitting, Standing or Laying.
This dataset is collected from 30 persons(referred as subjects in this dataset), performing different activities with a smartphone to their waists.
Abstract We used supervised training to create a series of chess engines based on humans play at different levels of skill. We compared them to other engines and to human players and found that self-play trained engines would sometimes behave more human-like than the supervised ones, although we believe this may be due to improper hyperparameter selection.
Abstract A multi-task learning convolutional neural network for the purpose of performing landmark localization and other correlated tasks is studied and analysed in this project. A different and more challenging task around landmark localization than the one implemented originally is studied using a HyperFace architecture.