yaoconglei Goto Github PK

followers: 2.0 following: 0.0 repos: 36.0 gists: 0.0

Name: YAO CONGLEI

Type: User

Company: Kika Tech

YAO CONGLEI's Projects

administrative-divisions-of-china

中华人民共和国行政区划：省级（省份直辖市自治区）、地级（城市）、县级（区县）、乡级（乡镇街道）、村级（村委会居委会），**省市区镇村二级三级四级五级联动地址数据 Node.js 爬虫。

cadl

Course materials/Homework materials for the FREE MOOC course on "Creative Applications of Deep Learning w/ Tensorflow" #CADL

cartographer

Cartographer is a system that provides real-time simultaneous localization and mapping (SLAM) in 2D and 3D across multiple platforms and sensor configurations.

char-rnn

Multi-layer Recurrent Neural Networks (LSTM, GRU, RNN) for character-level language models in Torch

char-rnn-tensorflow

Multi-layer Recurrent Neural Networks (LSTM, RNN) for character-level language models in Python using Tensorflow

cnn-text-classification-tf

Convolutional Neural Network for Text Classification in Tensorflow

coursera-ml-andrewng

use numpy, scipy, and tensorflow to implement these basic ML model and learning algorithm

dcgan-completion.tensorflow

Image Completion with Deep Learning in TensorFlow

dcgan-tensorflow

A tensorflow implementation of "Deep Convolutional Generative Adversarial Networks"

deep-learning-book

Repository for "Introduction to Artificial Neural Networks and Deep Learning: A Practical Guide with Applications in Python"

deep-learning-tensorflow

Ready to use implementations of various Deep Learning algorithms using TensorFlow.

deep_recommend_system

TensorFlow template application for deep learning

deeplearningtutorials

Deep Learning Tutorial notes and code. See the wiki for more info.

deepqa

My tensorflow implementation of "A neural conversational model", a Deep learning based chatbot

extra-comment-for-word2vec_basic.py-in-tensorflow-tutorial

Add some extra comment for word2vec beginners

fasttext

Library for fast text representation and classification.

lstm-human-activity-recognition

Human activity recognition using TensorFlow on smartphone sensors dataset and an LSTM RNN. Classifying the type of movement amongst six categories (WALKING, WALKING_UPSTAIRS, WALKING_DOWNSTAIRS, SITTING, STANDING, LAYING).