6022 Open source projects that are alternatives of or similar to Deepembeding

Wrapper of DELF Tensorflow Model

PyTorch Implementation of "Large-Scale Image Retrieval with Attentive Deep Local Features"

Using siamese network to do dimensionality reduction and similar image retrieval

R package to interface with OpenML

CS231n 2019年春季学期课程作业

Keras implementation of NASNet-A

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

Assets for data8.org

Implementing YOLO using ResNet as the feature extraction network

vehicle image retrieval using k CNNs ensemble method

Material for the Montréal Deep Learning Summer School 2017

推荐系统从入门到实战

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.

Python package for publishing Jupyter Notebooks as Medium blogposts

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

Public repo for Materials API documentation

nbconflux converts Jupyter Notebooks to Atlassian Confluence pages

Computational reproducibility using Continuous Integration to produce verifiable end-to-end runs of scientific analysis.

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

Code for "Online and Linear Time Attention by Enforcing Monotonic Alignments"

introduction to text analytics in python training for odsc west 2018

Local AI demo and distributed AI demo using TensorFlow

Codes for Learning Affinity via Spatial Propagation Networks

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

code repository for Udacity nanodegree Artificial Intelligence for Trading

Augmented environments with RL

Data Reading Blocks for Python

API for accessing the GraphLog dataset

Machine learning portfolio

A Python library for integrating model-based and judgmental forecasting

Powerful, automated analysis and design of quantum microwave chips & devices [Energy-Participation Ratio and more]

google it automation with python professional certificate

生命情報の機械学習入門（新学術領域「先進ゲノム支援」中級講習会資料）

A collection of IJulia notebooks related to optimization

Papers I read

Data visualization course material

Colorise Anime/Manga Sketches with DeOldify

Exposure Matrix Factorization: modeling user exposure in recommendation

A collection of simple tutorials for using Fonduer

Video2GIF neural network model from our paper at CVPR2016

TensorFlow Tutorials with YouTube Videos

Accompanying notebook for the Entailment with Tensorflow article.

Stanford cs231n'18 assignment

This is the code for "How to Generate Video - Intro to Deep Learning #15' by Siraj Raval on YouTube

Solution of RecSys Challenge 2015

2017-CCF-BDCI-企业经营退出风险预测：9th/569 (Top 1.58%)

Sample code for running deterministic variational inference to train Bayesian neural networks

A chatbot built in python using Selenium module.

Learning to Exploit Long-term Relational Dependencies in Knowledge Graphs, ICML 2019

基于ltp的简单评论观点抽取模块

Malware Data Science Reading Diary / Notes

brief introduction to Python for neural networks

A collection of examples for the ML.NET machine learning package from Microsoft

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

Applying UNET Model on TGS Salt Identification Challenge hosted on Kaggle

The Biggest Scala Cheat-Sheet.

Jupyter notebooks for h3-py, a hierarchical hexagonal geospatial indexing system

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.