Rnn projects github

GitHub is home to over 40 million developers working together to host and review code, manage projects, and build software together. If nothing happens, download GitHub Desktop and try again. If nothing happens, download Xcode and try again. If nothing happens, download the GitHub extension for Visual Studio and try again. Please refer to the Udacity instructions in your classroom for setting up a GPU instance for this project.

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Before submitting your solution to a reviewer, you are required to submit your project to Udacity's Project Assistant, which will provide some initial feedback. The setup is simple. If you have not installed the client tool already, then you may do so with the command pip install udacity-pa. To submit your code to the project assistant, run udacity submit from within the top-level directory of this project.

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rnn projects github

This process will create a zipfile in your top-level directory named rnn. This is the file that you should submit to the Udacity reviews system. Skip to content. Dismiss Join GitHub today GitHub is home to over 40 million developers working together to host and review code, manage projects, and build software together.

Sign up. Jupyter Notebook Python. Jupyter Notebook Branch: master. Find file. Sign in Sign up. Go back. Launching Xcode If nothing happens, download Xcode and try again. Latest commit Fetching latest commit…. Step 1: Implement a function to window time series Criteria Meets Specifications Window time series data.

The submission returns the proper windowed version of input time series of proper dimension listed in the notebook. Step 3: Clean up a large text corpus Criteria Meets Specifications Find and remove all non-english or punctuation characters from input text data. Step 4: Implement a function to window a large text corpus Criteria Meets Specifications Implement a function to window input text data The submission returns the proper windowed version of input text of proper dimension listed in the notebook.

The submission presents examples of generated text from a trained RNN module. The majority of this generated text should consist of real english words. Submission Before submitting your solution to a reviewer, you are required to submit your project to Udacity's Project Assistant, which will provide some initial feedback.

You signed in with another tab or window. Reload to refresh your session.Describe the solution you'd like Reference. Use Convolutional Recurrent Neural Network to recognize the Handwritten line text image without pre segmentation into words or characters.

Use CTC loss Function to train. TensorFlow implementations of several deep learning models e. Consulting Project with Manifold. Recurrent neural network in TensorFlow for generating novel monophonic melodies. Estimate intrinsic Permanent Magnet Synchronous Motor temperatures with deep recurrent and convolutional neural networks.

An RNN made in Keras that generates names resembling those you give it, be it people's names, city names, etc. Add a description, image, and links to the recurrent-neural-network topic page so that developers can more easily learn about it. Curate this topic. To associate your repository with the recurrent-neural-network topic, visit your repo's landing page and select "manage topics.

Learn more. Skip to content. Here are 90 public repositories matching this topic Language: All Filter by language. Sort options. Star Code Issues Pull requests. Is your feature request related to a problem?

Please describe. Updated Feb 8, Python. Updated Nov 22, Python. Updated May 19, Jupyter Notebook. Time series forecasting. Updated Jan 2, Python. Updated Apr 13, Python. Updated May 17, Python. Updated Feb 20, Jupyter Notebook. Updated Jun 21, Jupyter Notebook. Updated Mar 11, Python.

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Updated Jun 26, Jupyter Notebook. Updated Apr 1, Python. Recurrent autoencoder for time-series analysis [Tensorflow]. Updated Apr 9, Python.

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Star 9. Updated Feb 10, Jupyter Notebook.GitHub is home to over 40 million developers working together to host and review code, manage projects, and build software together. If nothing happens, download GitHub Desktop and try again. If nothing happens, download Xcode and try again. If nothing happens, download the GitHub extension for Visual Studio and try again.

There are many LSTM tutorials, courses, papers in the internet. This one summarizes all of them. It will continue to be updated over time.

NOTE: This tutorial is only for education purpose. All related references are listed at the end of the file. In this tutorial, we are focusing on recurrent networks, especially LSTM. A common LSTM unit is composed of a cell, an input gate, an output gate and a forget gate. The cell remembers values over arbitrary time intervals and the three gates regulate the flow of information into and out of the cell".

Long Short Term Memory LSTM is a type of deep learning model that is mostly used for analysis of sequential data time series data prediction. There are different application areas that are used: Language model, Neural machine translation, Music generation, Time series prediction, Financial prediction, Robot control, Time series prediction, Speech recognition, Rhythm learning, Music composition, Grammar learning, Handwriting recognition, Human action recognition, Sign Language Translation,Time series anomaly detection, Several prediction tasks in the area of business process management, Prediction in medical care pathways, Semantic parsing, Object Co-segmentation.

In this project, it will be implemented a model which inputs a sentence and finds the most appropriate emoji to be used with this sentence. Glove 50 dimension, words of dictionary file is used for word embeddings.

Code is adapted from Andrew Ng's Course 'Sequential models'. X: This is an m, Tx78 dimensional array. At each time step, the input is one of 78 different possible values, represented as a one-hot vector. Thus for example, X[i,t,:] is a one-hot vector representating the value of the i-th example at time t. Skip to content. Dismiss Join GitHub today GitHub is home to over 40 million developers working together to host and review code, manage projects, and build software together.

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Creating A Text Generator Using Recurrent Neural Network

Sign in Sign up. Go back. Launching Xcode If nothing happens, download Xcode and try again. Latest commit. Latest commit d3bb6b7 Jan 3, Table of Contents What is Deep Learning? What is RNN? Recurrent neural network RNN is a type of deep learning model that is mostly used for analysis of sequential data time series data prediction.

There are different application areas that are used: Language model, neural machine translation, music generation, time series prediction, financial prediction, etc. The problem was explored in depth by Hochreiter [German] and Bengio, et al.

rnn projects github

It is a special type of RNN, capable of learning long-term dependencies. The cell remembers values over arbitrary time intervals and the three gates regulate the flow of information into and out of the cell" Long Short Term Memory LSTM is a type of deep learning model that is mostly used for analysis of sequential data time series data prediction.GitHub is home to over 40 million developers working together to host and review code, manage projects, and build software together.

If nothing happens, download GitHub Desktop and try again. If nothing happens, download Xcode and try again.

If nothing happens, download the GitHub extension for Visual Studio and try again. There are four different versions. All scripts will divide the data into training set, validation set, and test set. They will run for a fixed number of epochs. The model with the best "Test AUC" will be saved at the end of the training.

Skip to content. Dismiss Join GitHub today GitHub is home to over 40 million developers working together to host and review code, manage projects, and build software together. Sign up. Python Branch: master. Find file. Sign in Sign up. Go back. Launching Xcode If nothing happens, download Xcode and try again. Latest commit Fetching latest commit….

This suses time information in addition to the code sequences. The data are synthetic and make no sense at all. It is intended only for testing the codes. Each integer is assumed to be some medical code. Each integer is assumed to the time at which the medical code occurred. You signed in with another tab or window. Reload to refresh your session.

rnn projects github

You signed out in another tab or window.GitHub is home to over 40 million developers working together to host and review code, manage projects, and build software together. If nothing happens, download GitHub Desktop and try again. If nothing happens, download Xcode and try again. If nothing happens, download the GitHub extension for Visual Studio and try again. In other words the model takes one text file as input and trains a Recurrent Neural Network that learns to predict the next character in a sequence.

The RNN can then be used to generate text character by character that will look like the original training data. The context of this code base is described in detail in my blog post.

rnn projects github

The code in this repo additionally: allows for multiple layers, uses an LSTM instead of a vanilla RNN, has more supporting code for model checkpointing, and is of course much more efficient since it uses mini-batches and can run on a GPU. It's under the name torch-rnn.

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This also avoids headaches with cloning models in this repo. In other words, torch-rnn should be the default char-rnn implemention to use now instead of the one in this code base.

This code is written in Lua and requires Torch. If you're on Ubuntu, installing Torch in your home directory may look something like:. See the Torch installation documentation for more details. After Torch is installed we need to get a few more packages using LuaRocks which already came with the Torch install. In particular:. Then get the cutorch and cunn packages:.

ATI cardsyou will instead need to install the cltorch and clnn packages, and then use the option -opencl 1 during training cltorch issues :. I'm providing a few more datasets on this page. Your own data : If you'd like to use your own data then create a single file input. Dataset sizes : Note that if your data is too small 1MB is already considered very small the RNN won't learn very effectively.

Remember that it has to learn everything completely from scratch. It will work significantly better. Start training the model using train. As a sanity check, to run on the included example dataset simply try:. Notice that here we are setting the flag gpuid to -1, which tells the code to train using CPU, otherwise it defaults to GPU 0.GitHub is home to over 40 million developers working together to host and review code, manage projects, and build software together.

If nothing happens, download GitHub Desktop and try again. If nothing happens, download Xcode and try again. If nothing happens, download the GitHub extension for Visual Studio and try again.

How to Predict Stock Prices Easily - Intro to Deep Learning #7

A toy chatbot powered by deep learning and trained on data from Reddit. Built on TensorFlow v1. Here is a sample chat transcript not cherry-picked. Install TensorFlow for Python 3. To run a pretrained model, the CPU-only installation should suffice. Download my pre-trained model 2.

The zip file extracts into a folder named "reddit". Place that folder into the "models" directory of this project. Run the chatbot. Open a terminal session and run python3 chatbot. Warning: this pre-trained model was trained on a diverse set of frequently off-color Reddit comments. It can and eventually will say things that are offensive, disturbing, bizarre or sexually explicit.

It may insult minorities, it may call you names, it may accuse you of being a pedophile, it may try to seduce you. Please don't use the chatbot if these possibilities would distress you! Set this higher for more careful, more conservative and slower responses, or set it to 1 to disable beam search.

Temperature can adjust the probability distribution. Values outside of the range of 0. Disabled by default. The mask model is scaled by the relevance value, and then the probabilities of the primary model are combined according to equation 9 in Li, Jiwei, et al.

The state of the mask model is reset upon each newline character. The net effect is that the model is encouraged to choose a line of dialogue that is most relevant to the prior line of dialogue, even if a more generic response e. Higher relevance values put more pressure on the model to produce relevant responses, at the cost of the coherence of the responses. Going much above 0. Setting it to a negative value disables relevance, and this is the default, because I'm not confident that it qualitatively improves the outputs and it halves the speed of sampling.

These values can also be manipulated during a chat, and the model state can be reset, without restarting the chatbot:. Use pre-formatted Reddit training data. This is what the pre-trained model was trained on. Download the training data 2.

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Unzip the monolithic zip file. You'll be left with a folder named "reddit" containing 34 files named "output 1. Do not extract those individual bzip2 files. Instead, place the whole "reddit" folder that contains those files inside the data folder of the repo.

The first time you run train. Generate your own Reddit training data.GitHub is home to over 40 million developers working together to host and review code, manage projects, and build software together. If nothing happens, download GitHub Desktop and try again. If nothing happens, download Xcode and try again.

If nothing happens, download the GitHub extension for Visual Studio and try again. Skip to content. Dismiss Join GitHub today GitHub is home to over 40 million developers working together to host and review code, manage projects, and build software together. Sign up. Learning about and doing projects with recurrent neural networks. Jupyter Notebook Python Other. Jupyter Notebook Branch: master. Find file. Sign in Sign up. Go back. Launching Xcode If nothing happens, download Xcode and try again.

Latest commit. Latest commit 8da0e37 Dec 11, You signed in with another tab or window. Reload to refresh your session. You signed out in another tab or window. Nov 7, Images for articles. Nov 19, Added images. Nov 5, Training complete. Nov 4, Nov 18, Added table.

recurrent-neural-network

Run with docker and docker-compose. Dec 11, Updated gitignore. Nov 17, Initial commit. Oct 27, Update requirements.



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