camphora

141-Dataprofolio

Applied-Machine-Learning-in-Python--University-of-Michigan---Coursera

Course materials for the Coursera MOOC: Applied Machine Learning in Python from University of Michigan

BERT-Classification-Tutorial

chatbot-retrieval

Dual LSTM Encoder for Dialog Response Generation

CLUEDatasetSearch

搜索所有中文NLP数据集，附常用英文NLP数据集

ConversationalRobotDesign

对话机器人（聊天机器人）设计思考

CS224n

CS224n: Natural Language Processing with Deep Learning Assignments Winter, 2017

deep-learning-time-series

List of papers, code and experiments using deep learning for time series forecasting

deep-siamese-text-similarity

Tensorflow based implementation of deep siamese LSTM network to capture phrase/sentence similarity using character/word embeddings

deeplearning.ai

Some work of Andrew Ng's course on Coursera

camphora/ranking_clarification_questions

Code and Data for the paper: "Learning to Ask Good Questions: Ranking Clarification Questions using Neural Expected Value of Perfect Information"

camphora/tensorflow-LTR

Tensorflow implementations of various Learning to Rank (LTR) algorithms.

camphora/Applied-Machine-Learning-in-Python--University-of-Michigan---Coursera

Course materials for the Coursera MOOC: Applied Machine Learning in Python from University of Michigan

camphora/deepLearning.ai.solution

This repository contains the implementation of deep learning courses by Andrew ng on Coursera

camphora/Python_DL_Working_Notebooks

Alex Fleming's solution work in Python on Deep Learning

camphora/learning2rank

Learning to rank with neuralnet - RankNet and ListNet

camphora/ParallelR

Material for workshop on parallel R

camphora/learning-rank-public

Learning Rank

camphora/141-Dataprofolio

camphora/test

test repository for 141b

camphora/PairCNN-Ranking

A tensorflow implementation of Learning to Rank Short Text Pairs with Convolutional Deep Neural Networks

camphora/iclr2016

Python code for training all models in the ICLR paper, "Towards Universal Paraphrastic Sentence Embeddings". These models achieve strong performance on semantic similarity tasks without any training or tuning on the training data for those tasks. They also can produce features that are at least as discriminative as skip-thought vectors for semantic similarity tasks at a minimum. Moreover, this code can achieve state-of-the-art results on entailment and sentiment tasks.

camphora/DL-to-Rank

Learning to Rank with Deep Neural Networks

camphora/RankingFeatureSelection

Feature selection algorithms for learning to rank