carpedm20 / Ntm Tensorflow
Licence: mit
"Neural Turing Machine" in Tensorflow
Stars: ✭ 1,013
Projects that are alternatives of or similar to Ntm Tensorflow
Tigertoolbox
Toolbox repository for Tiger team
Stars: ✭ 1,003 (-0.99%)
Mutual labels: jupyter-notebook
Slow disentanglement
Towards Nonlinear Disentanglement in Natural Data with Temporal Sparse Coding
Stars: ✭ 41 (-95.95%)
Mutual labels: jupyter-notebook
Deeptrading
Deep Neural Network Trading collection of Tensorflow Jupyter notebooks
Stars: ✭ 41 (-95.95%)
Mutual labels: jupyter-notebook
Quilt
Quilt is a self-organizing data hub for S3
Stars: ✭ 1,007 (-0.59%)
Mutual labels: jupyter-notebook
Yann
This toolbox is support material for the book on CNN (http://www.convolution.network).
Stars: ✭ 41 (-95.95%)
Mutual labels: jupyter-notebook
Icml2020
Notebook for comprehensive analysis of authors, organizations, and countries of ICML 2020 papers.
Stars: ✭ 42 (-95.85%)
Mutual labels: jupyter-notebook
Tensorflow Seq2seq Tutorials
Dynamic seq2seq in TensorFlow, step by step
Stars: ✭ 1,011 (-0.2%)
Mutual labels: jupyter-notebook
Mitx Foundations Of Computer Science
The Foundations of Computer Science XSeries, offered by the M.I.T. Department of Electrical Engineering and Computer Science, is a sequence of courses that introduce key concepts of computer science and computational thinking. Students apply these concepts and build their engineering skills by completing software and hardware design problems. Additionally, students test their understanding by taking a series of exams.
Stars: ✭ 41 (-95.95%)
Mutual labels: jupyter-notebook
Machine Learning From Scratch
Succinct Machine Learning algorithm implementations from scratch in Python, solving real-world problems (Notebooks and Book). Examples of Logistic Regression, Linear Regression, Decision Trees, K-means clustering, Sentiment Analysis, Recommender Systems, Neural Networks and Reinforcement Learning.
Stars: ✭ 42 (-95.85%)
Mutual labels: jupyter-notebook
Bdaca
Course Materials Big Data and Automated Content Analysis
Stars: ✭ 41 (-95.95%)
Mutual labels: jupyter-notebook
Algorithms
🍣 Implementations of fundamental algorithms and data structures. Happy Hacktoberfest!
Stars: ✭ 41 (-95.95%)
Mutual labels: jupyter-notebook
Practical dl
DL course co-developed by YSDA, HSE and Skoltech
Stars: ✭ 1,006 (-0.69%)
Mutual labels: jupyter-notebook
Keras Pytorch Avp Transfer Learning
We pit Keras and PyTorch against each other, showing their strengths and weaknesses in action. We present a real problem, a matter of life-and-death: distinguishing Aliens from Predators!
Stars: ✭ 42 (-95.85%)
Mutual labels: jupyter-notebook
Ds Take Home
My solution to the book A Collection of Data Science Take-Home Challenges
Stars: ✭ 1,004 (-0.89%)
Mutual labels: jupyter-notebook
Siren Fastai2
Unofficial implementation of 'Implicit Neural Representations with Periodic Activation Functions'
Stars: ✭ 42 (-95.85%)
Mutual labels: jupyter-notebook
Tensorflow Programs And Tutorials
Implementations of CNNs, RNNs, GANs, etc
Stars: ✭ 1,012 (-0.1%)
Mutual labels: jupyter-notebook
Bayesgan
Tensorflow code for the Bayesian GAN (https://arxiv.org/abs/1705.09558) (NIPS 2017)
Stars: ✭ 1,011 (-0.2%)
Mutual labels: jupyter-notebook
Neural Turing Machine in Tensorflow
Tensorflow implementation of Neural Turing Machine. This implementation uses an LSTM controller. NTM models with multiple read/write heads are supported.
The referenced torch code can be found here.
** 1. Loss sometimes goes to nan even with the gradient clipping (#2).** ** 2. The code is very poorly design to support NTM inputs with variable lengths. Just use this code as a reference.**
Prerequisites
- Python 2.7 or Python 3.3+
- Tensorflow 1.1.0
- NumPy
Usage
To train a copy task:
$ python main.py --task copy --is_train True
To test a quick copy task:
$ python main.py --task copy --test_max_length 10
Results
More detailed results can be found [here](ipynb/NTM\ Test.ipynb).
Copy task:
Recall task:
(in progress)
Author
Taehoon Kim / @carpedm20
Note that the project description data, including the texts, logos, images, and/or trademarks,
for each open source project belongs to its rightful owner.
If you wish to add or remove any projects, please contact us at [email protected].