Related Projects

web-traffic-forecasting - Kaggle | Web Traffic Forecasting 📈

  •    Python

My solution for the Web Traffic Forecasting competition hosted on Kaggle. The training dataset consists of approximately 145k time series. Each of these time series represents a number of daily views of a different Wikipedia article, starting from July 1st, 2015 up until September 10th, 2017. The goal is to forecast the daily views between September 13th, 2017 and November 13th, 2017 for each article in the dataset. The name of the article as well as the type of traffic (all, mobile, desktop, spider) is given for each article.

neuralmonkey - An open-source tool for sequence learning in NLP built on TensorFlow.

  •    Python

The Neural Monkey package provides a higher level abstraction for sequential neural network models, most prominently in Natural Language Processing (NLP). It is built on TensorFlow. It can be used for fast prototyping of sequential models in NLP which can be used e.g. for neural machine translation or sentence classification. The higher-level API brings together a collection of standard building blocks (RNN encoder and decoder, multi-layer perceptron) and a simple way of adding new building blocks implemented directly in TensorFlow.

LSTM-Human-Activity-Recognition - Human Activity Recognition example using TensorFlow on smartphone sensors dataset and an LSTM RNN (Deep Learning algo)

  •    Jupyter

Compared to a classical approach, using a Recurrent Neural Networks (RNN) with Long Short-Term Memory cells (LSTMs) require no or almost no feature engineering. Data can be fed directly into the neural network who acts like a black box, modeling the problem correctly. Other research on the activity recognition dataset can use a big amount of feature engineering, which is rather a signal processing approach combined with classical data science techniques. The approach here is rather very simple in terms of how much was the data preprocessed. Let's use Google's neat Deep Learning library, TensorFlow, demonstrating the usage of an LSTM, a type of Artificial Neural Network that can process sequential data / time series.

Seq2Seq-PyTorch - Sequence to Sequence Models with PyTorch

  •    Python

A vanilla sequence to sequence model presented in https://arxiv.org/abs/1409.3215, https://arxiv.org/abs/1406.1078 consits of using a recurrent neural network such as an LSTM (http://dl.acm.org/citation.cfm?id=1246450) or GRU (https://arxiv.org/abs/1412.3555) to encode a sequence of words or characters in a source language into a fixed length vector representation and then deocoding from that representation using another RNN in the target language. An extension of sequence to sequence models that incorporate an attention mechanism was presented in https://arxiv.org/abs/1409.0473 that uses information from the RNN hidden states in the source language at each time step in the deocder RNN. This attention mechanism significantly improves performance on tasks like machine translation. A few variants of the attention model for the task of machine translation have been presented in https://arxiv.org/abs/1508.04025.

seq2seq-signal-prediction - Signal prediction with a Sequence-to-Sequence (seq2seq) Recurrent Neural Network (RNN) model in TensorFlow - Guillaume Chevalier

  •    Jupyter

The goal of this project of mine is to bring users to try and experiment with the seq2seq neural network architecture. This is done by solving different simple toy problems about signal prediction. Normally, seq2seq architectures may be used for other more sophisticated purposes than for signal prediction, let's say, language modeling, but this project is an interesting tutorial in order to then get to more complicated stuff. Except the fact I made available an ".py" Python version of this tutorial within the repository, it is more convenient to run the code inside the notebook. The ".py" code exported feels a bit raw as an exportation.


word-rnn-tensorflow - Multi-layer Recurrent Neural Networks (LSTM, RNN) for word-level language models in Python using TensorFlow

  •    Python

Multi-layer Recurrent Neural Networks (LSTM, RNN) for word-level language models in Python using TensorFlow. Mostly reused code from https://github.com/sherjilozair/char-rnn-tensorflow which was inspired from Andrej Karpathy's char-rnn.

RLSeq2Seq - Deep Reinforcement Learning For Sequence to Sequence Models

  •    Python

NOTE: THE CODE IS UNDER DEVELOPMENT, PLEASE ALWAYS PULL THE LATEST VERSION FROM HERE. In recent years, sequence-to-sequence (seq2seq) models are used in a variety of tasks from machine translation, headline generation, text summarization, speech to text, to image caption generation. The underlying framework of all these models are usually a deep neural network which contains an encoder and decoder. The encoder processes the input data and a decoder receives the output of the encoder and generates the final output. Although simply using an encoder/decoder model would, most of the time, produce better result than traditional methods on the above-mentioned tasks, researchers proposed additional improvements over these sequence to sequence models, like using an attention-based model over the input, pointer-generation models, and self-attention models. However, all these seq2seq models suffer from two common problems: 1) exposure bias and 2) inconsistency between train/test measurement. Recently a completely fresh point of view emerged in solving these two problems in seq2seq models by using methods in Reinforcement Learning (RL). In these new researches, we try to look at the seq2seq problems from the RL point of view and we try to come up with a formulation that could combine the power of RL methods in decision-making and sequence to sequence models in remembering long memories. In this paper, we will summarize some of the most recent frameworks that combines concepts from RL world to the deep neural network area and explain how these two areas could benefit from each other in solving complex seq2seq tasks. In the end, we will provide insights on some of the problems of the current existing models and how we can improve them with better RL models. We also provide the source code for implementing most of the models that will be discussed in this paper on the complex task of abstractive text summarization.

crfasrnn_keras - CRF-RNN Keras/Tensorflow version

  •    Python

This repository contains Keras/Tensorflow code for the "CRF-RNN" semantic image segmentation method, published in the ICCV 2015 paper Conditional Random Fields as Recurrent Neural Networks. This paper was initially described in an arXiv tech report. The online demo of this project won the Best Demo Prize at ICCV 2015. Original Caffe-based code of this project can be found here. Results produced with this Keras/Tensorflow code are almost identical to that with the Caffe-based version. The root directory of the clone will be referred to as crfasrnn_keras hereafter.

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

  •    Python

This work tries to reproduce the results of A Neural Conversational Model (aka the Google chatbot). It uses a RNN (seq2seq model) for sentence predictions. It is done using python and TensorFlow. The loading corpus part of the program is inspired by the Torch neuralconvo from macournoyer.

GroundHog - Library for implementing RNNs with Theano

  •    Python

This library is built on top of Theano (http://deeplearning.net/software/theano/) and can be used with Jobman (http://deeplearning.net/software/jobman/). It is meant to provide a flexible yet efficient way of implementing complex recurrent models. Currently it supports variations of recurrent neural networks (such as DT-RNN, DOT-RNN, RNN Encoder-Decoder) and stacked version of them. Most of the library documentation is still work in progress, but check the files containing Tut (in scripts) for a quick tutorial on how to use the library.

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

  •    Python

Multi-layer Recurrent Neural Networks (LSTM, RNN) for character-level language models in Python using Tensorflow. Inspired from Andrej Karpathy's char-rnn.

seq2seq - A general-purpose encoder-decoder framework for Tensorflow

  •    Python

A general-purpose encoder-decoder framework for Tensorflow that can be used for Machine Translation, Text Summarization, Conversational Modeling, Image Captioning, and more.The official code used for the Massive Exploration of Neural Machine Translation Architectures paper.

Tensorflow-Programs-and-Tutorials - Implementations of CNNs, RNNs, GANs, etc

  •    Jupyter

CNN's with Noisy Labels - This notebook looks at a recent paper that discusses how convolutional neural networks that are trained on random labels (with some probability) are still able to acheive good accuracy on MNIST. I thought that the paper showed some eye-brow raising results, so I went ahead and tried it out for myself. It was pretty amazing to see that even when training a CNN with random labels 50% of the time, and the correct labels the other 50% of the time, the network was still able to get a 90+% accuracy. Character Level RNN (Work in Progress) - This notebook shows you how to train a character level RNN in Tensorflow. The idea was inspired by Andrej Karpathy's famous blog post and was based on this Keras implementation. In this notebook, you'll learn more about what the model is doing, and how you can input your own dataset, and train a model to generate similar looking text.

kaggle - A collection of Kaggle solutions. Not very polished.

  •    Python

I've been using Kaggle as an excuse to learn techniques in machine learning/artificial intelligence. Here are some primary resources I've been learning from (in rough chronological order). For reference, I started from an extensive programming background, a decent but rusty math background, and a rudimentary background in machine learning.

kaggle-galaxies - Winning solution for the Galaxy Challenge on Kaggle (http://www

  •    Python

Winning solution for the Galaxy Challenge on Kaggle (http://www.kaggle.com/c/galaxy-zoo-the-galaxy-challenge). Documentation about the method and the code is available in doc/documentation.pdf. Information on how to generate the solution file can also be found below.

kaggle-cifar10-torch7 - Code for Kaggle-CIFAR10 competition. 5th place.

  •    Lua

Please check your Torch7/CUDA environment when this code fails. Place the data files into a subfolder ./data.

kaggle - Kaggle 项目实战(教程) = 文档 + 代码 + 视频

  •    Jupyter

Kaggle 项目实战(教程) = 文档 + 代码 + 视频

docker-python - Kaggle Python docker image

  •    Dockerfile

This is the Dockerfile (etc.) used for building the image that runs python scripts on Kaggle. Here's the Docker image on Dockerhub. To get started with this image, read our guide to using it yourself, or browse Kaggle Kernels for ideas.

kaggle-api - Official Kaggle API

  •    Python

Official API for https://www.kaggle.com, accessible using a command line tool implemented in Python. Beta release - Kaggle reserves the right to modify the API functionality currently offered.