6116 Open source projects that are alternatives of or similar to Simple Qa Emnlp 2018

Generating Captions for images using Deep Learning

Gamified tutorial for the QISKit quantum SDK

Koç University deep learning framework.

An LSTM Autoencoder for rare event classification

《Python工具代码速查手册》是我们的python培训教材，主要面向数据分析方向。其中包含了python的常用总结性操作，使用jupyter notebook，利用markdown和script结果对常用操作进行总结，包括了使用方式和脚本。之所以使用notebook形式是可以方便大家编辑，方便大家形成自己的总结笔记。当然各位有更好的操作建议也欢迎向我们团队分享~

适合初级到中级晋升者，有了体系之后就看熟练度了。

Deliverable: This Jupyter notebook will help aspiring data scientists learn and practice the necessary python code needed for many data science projects.

Numerical simulations using flexible Lattice Boltzmann solvers

Gemfield homework or libgemfield.so

statistics and classes for exploratory spatial data analysis

Experiments with Deep Learning

A short tutorial for Torchtext, the NLP-specific add-on for Pytorch.

Taking a pretrained GloVe model, and using it as a TensorFlow embedding weight layer **inside the GPU**. Therefore, you only need to send the index of the words through the GPU data transfer bus, reducing data transfer overhead.

My repository for the Carvana Image Masking Challenge

There are plenty of courses and tutorials that can help you learn machine learning from scratch but here in GitHub, I want to solve some Kaggle competitions as a comprehensive workflow with python packages. After reading, you can use this workflow to solve other real problems and use it as a template.

It is the DKT+ model implemented in python3 and tensorflow1.2

Caffe implementation of Mobilenet-SSD face detector (NCS compatible)

Project to train/test convolutional neural networks to extract buildings from SpaceNet satellite imageries.

Repository for reading Pascal VOC data in Python, rather than requiring MATLAB to read the XML files.

VGG-19 deep learning model trained using ISCX 2012 IDS Dataset

ALICE (Automated Learning and Intelligence for Causation and Economics) is a Microsoft Research project aimed at applying Artificial Intelligence concepts to economic decision making. One of its goals is to build a toolkit that combines state-of-the-art machine learning techniques with econometrics in order to bring automation to complex causal inference problems. To date, the ALICE Python SDK (econml) implements orthogonal machine learning algorithms such as the double machine learning work of Chernozhukov et al. This toolkit is designed to measure the causal effect of some treatment variable(s) t on an outcome variable y, controlling for a set of features x.

Automagically remove trends from time-series data

Amazon SageMaker examples for prebuilt framework mode containers, a.k.a. Script Mode, and more (BYO containers and models etc.)

The art of using t-SNE for single-cell transcriptomics

Resources for Machine Learning Pocket Reference

Code for reproducing the results from the paper Few Shot Text Classification with a Human in the Loop

Série histórica dos dados sobre COVID-19, a partir de informações do Ministério da Saúde

BERT + Image Captioning

Deep Learning: Image classification, feature visualization and transfer learning with Keras

Designing Deep neural network architectures using topologies from the world of Complex Networks/network Science

Local AI demo and distributed AI demo using TensorFlow

Templates for jupyter notebooks

Neural machine translator for English2German translation.

Distributed deep learning on Hadoop and Spark clusters.

A Notebook where I implement differents anomaly detection algorithms on a simple exemple. The goal was just to understand how the different algorithms works and their differents caracteristics.

Keras implementation of NASNet-A

nbconflux converts Jupyter Notebooks to Atlassian Confluence pages

Python-based spatial data analysis and visualization of the GPS location data from my 2014 summer travels.

Implementing YOLO using ResNet as the feature extraction network

[BA project] Dynamic Pricing Optimization for Airbnb listing to optimize yearly profit for host. Use Clustering for competitive analysis, kNN regression for demand forecasting, and find dynamic optimal price with Optimization model.

introduction to text analytics in python training for odsc west 2018

Voice Conversion using Cycle GAN's For Non-Parallel Data

Course materials for: Introduction to Data Science and Programming

This repository includes the code used in my corresponding Medium post.

Examples for Deep Learning/Feature Store/Spark/Flink/Hive/Kafka jobs and Jupyter notebooks on Hops

Interactive guide to Auclert, Bardóczy, Rognlie, and Straub (2019): "Using the Sequence-Space Jacobian to Solve and Estimate Heterogeneous-Agent Models".

Atom notebook

推荐系统从入门到实战

A collection of code examples as well as presentations for training purposes

Computer Science SCHOOL resources

CAUTION: This is not maintained anymore. Visit https://github.com/chainer-community/chainer-colab-notebook/

Building classifiers using cancer transcriptomes across 33 different cancer-types

Código para el curso "Aprende Data Science y Machine Learning con Python"

Implementation of the paper - Generative Recurrent Networks for De Novo Drug Design.