DeepMindv2

docs

TensorFlow documentation

Deep-Learning

covid-chestxray-dataset

We are building an open database of COVID-19 cases with chest X-ray or CT images.

FinRL-Tutorials

Tutorials. Please star.

bdl-benchmarks

Bayesian Deep Learning Benchmarks

BristolStockExchange

BSE is a simple minimal simulation of a limit order book financial exchange

CEAL-Medical-Image-Segmentation

Active Deep Learning for Medical Imaging Segmentation

keras_lr_finder

Plots the change of the loss function of a Keras model when the learning rate is exponentially increasing.

mimic-cxr

Code, documentation, and discussion around the MIMIC-CXR database

Chexpert

Top 1 solution of Chexpert

mobilenetv3-tensorflow

Unofficial implementation of MobileNetV3 architecture described in paper Searching for MobileNetV3.

Deep-Reinforcement-Learning-in-Trading

This repository provides the code for a Reinforcement Learning trading agent with its trading environment that works with both simulated and historical market data. This was inspired by OpenAI Gym framework.

ctc-executioner

Master Thesis: Limit order placement with Reinforcement Learning

Going-Deeper-with-Convolutional-Neural-Network-for-Stock-Market-Prediction

Repository for Going Deeper with Convolutional Neural Network for Stock Market Prediction

Retinal_blindness_detection_Pytorch

Detecting Diabetic Retinopathy using Deep learning algorithm - Convolution neural network (Resnet-152) using PyTorch + GUI + SMS notification

bcnn

3D Bayesian Convolutional Neural Network (BCNN) for Credible Geometric Uncertainty. Code for the paper: https://arxiv.org/abs/1910.10793

NIH-Chest-X-Rays-Multi-Label-Image-Classification-In-Pytorch

Multi-Label Image Classification of Chest X-Rays In Pytorch

HTFE-tensortrade

Tensortrade project for reinforcement learning in futures market

tbcnn

A convolutional neural network for tuberculosis diagnosis from frontal chest X-Rays

DS-5500-Project-Portfolio-Optimization-Using-Deep-Reinforcement-Learning

The repository contains the code for project for DS 5500 course at Northeastern.

nih-chest-xrays

A collection of projects which explore image classification on chest x-ray images (via the NIH dataset)

ISBI2018-Diagnostic-Classification-Of-Lung-Nodules-Using-3D-Neural-Networks

Network Architecture for the ISBI_2018 paper : DIAGNOSTIC CLASSIFICATION OF LUNG NODULES USING 3D NEURAL NETWORKS

D-N-N_HEALTH-CARE

3D-U-net-Keras

3D-Unet: patched based Keras implementation for medical images segmentation

monte_carlo_dropout

Uncertainty estimation in deep learning using monte carlo dropout with keras

Rap_Generation

A repository dedicated to the workshops I give on natural language generation

Acute-Lymphoblastic-Leukemia-cell-classification-using-Bayesian-Convolutional-Neural-Networks

In this project, we deploy the Bayesian Convolution Neural Networks (BCNN), proposed by Gal and Ghahramani [2015] to classify microscopic images of blood samples (lymphocyte cells). The data contains 260 microscopic images of cancerous and non-cancerous lymphocyte cells. We experiment with different network structures to obtain the model that return lowest error rate in classifying the images. We estimate the uncertainty for the predictions made by the models which in turn can assist a doctor in better decision making. The Stochastic Regularization Technique (SRT), popularly known as Dropout is utilized in the BCNN structure to obtain the Bayesian interpretation.

BCNN_cancer_detection

Using Bayesian deep neural networks for classification of histopathological images.

Image-Classification

KerasDropoutUncertainty

A very short example of using dropout uncertainty from (https://github.com/yaringal/DropoutUncertaintyCaffeModels) with Keras and Tensorflow. Including how to do test time dropout.