This talk is about the promise of Deep Learning in Healthcare and its practial limitations. What are some of the gaps between academic research and prodcution level code and how can we mitigate this production level gap in Deep Learning and Healthcare, and what are some of the tools and techniques we can deploy to make production easier for Deep Learning especially Healthcare?
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.
Project Description A Deep Learning approach to detecting deforestation risk, using satellite images and a deep learning model. We relied on Planet imagery from two Kaggle datasets (one from the Amazon rainforest and another on oil palm plantations in Borneo) and trained a ResNet model using FastAI.
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.
Multi-layer recurrent neural networks for training and sampling from texts, inspired by karpathy/char-rnn.
Requirements This code is written in Python 2, and it requires the Keras deep learning library.
Usage All input data should be placed in the data/ directory.
Deep Reinforcement Learning : Navigation This project repository contains my work for the Udacity’s Deep Reinforcement Learning Nanodegree Project 1: Navigation.
Project’s goal In this project, the goal is to train an agent to navigate a virtual world and collect as many yellow bananas as possible while avoiding blue bananas