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benanne
morb
Python2013–2015
benanne / morb

morb

Modular Restricted Boltzmann Machine (RBM) implementation using Theano

Explore on GitHubRead Hacker News discussions
3Distinct people
3Mentions
0Explicit recommenders
171GitHub stars
REPOSITORY AT A GLANCE

What to know before exploring

What it is

Modular Restricted Boltzmann Machine (RBM) implementation using Theano

GitHub

Where it is used

No documented product use has been verified yet.

What people are saying

No recommendation phrase was found in the indexed comments.

No classified warning is attached to this page. Read the discussions before treating silence as approval.

Also in these discussions

Mentioned in the same Hacker News threads. That is not proof they are substitutes.

This week on Hacker News

Nov 7–Nov 14 UTC · Direct repository links and separately labeled related project stories. Indexed through 11/14/2026, 3:56:07 PM UTC.

No direct repository links were found in the indexed HN discussions over the past seven days. Older discussions are available below.

Mentions over time

HN mentions by month3 dated mentions

Observed mentions: 2013-05-03 09:29:34 UTC to 2015-03-29 11:02:29 UTC.

012013-05-01 00:00:00 UTC to 2013-06-01 00:00:00 UTC (exclusive): 1 mentions; 0 submissions, 1 commentsMay 20132013-06-01 00:00:00 UTC to 2013-07-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2013-07-01 00:00:00 UTC to 2013-08-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2013-08-01 00:00:00 UTC to 2013-09-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2013-09-01 00:00:00 UTC to 2013-10-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2013-10-01 00:00:00 UTC to 2013-11-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2013-11-01 00:00:00 UTC to 2013-12-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2013-12-01 00:00:00 UTC to 2014-01-01 00:00:00 UTC (exclusive): 1 mentions; 0 submissions, 1 comments2014-01-01 00:00:00 UTC to 2014-02-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2014-02-01 00:00:00 UTC to 2014-03-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2014-03-01 00:00:00 UTC to 2014-04-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2014-04-01 00:00:00 UTC to 2014-05-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 commentsApr 20142014-05-01 00:00:00 UTC to 2014-06-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2014-06-01 00:00:00 UTC to 2014-07-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2014-07-01 00:00:00 UTC to 2014-08-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2014-08-01 00:00:00 UTC to 2014-09-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2014-09-01 00:00:00 UTC to 2014-10-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2014-10-01 00:00:00 UTC to 2014-11-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2014-11-01 00:00:00 UTC to 2014-12-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2014-12-01 00:00:00 UTC to 2015-01-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2015-01-01 00:00:00 UTC to 2015-02-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2015-02-01 00:00:00 UTC to 2015-03-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2015-03-01 00:00:00 UTC to 2015-04-01 00:00:00 UTC (exclusive): 1 mentions; 0 submissions, 1 commentsMar 2015

Counts come from the repository’s dated source evidence. Index cutoff: 2026-11-02 08:16:59 UTC.

View mention counts and date ranges
UTC ranges include their start and exclude their end. Select a period to filter the source evidence.
PeriodStart (UTC)End (exclusive, UTC)MentionsSubmissionsComments
May 20132013-05-01 00:00:00 UTC2013-06-01 00:00:00 UTC101
Jun 20132013-06-01 00:00:00 UTC2013-07-01 00:00:00 UTC000
Jul 20132013-07-01 00:00:00 UTC2013-08-01 00:00:00 UTC000
Aug 20132013-08-01 00:00:00 UTC2013-09-01 00:00:00 UTC000
Sep 20132013-09-01 00:00:00 UTC2013-10-01 00:00:00 UTC000
Oct 20132013-10-01 00:00:00 UTC2013-11-01 00:00:00 UTC000
Nov 20132013-11-01 00:00:00 UTC2013-12-01 00:00:00 UTC000
Dec 20132013-12-01 00:00:00 UTC2014-01-01 00:00:00 UTC101
Jan 20142014-01-01 00:00:00 UTC2014-02-01 00:00:00 UTC000
Feb 20142014-02-01 00:00:00 UTC2014-03-01 00:00:00 UTC000
Mar 20142014-03-01 00:00:00 UTC2014-04-01 00:00:00 UTC000
Apr 20142014-04-01 00:00:00 UTC2014-05-01 00:00:00 UTC000
May 20142014-05-01 00:00:00 UTC2014-06-01 00:00:00 UTC000
Jun 20142014-06-01 00:00:00 UTC2014-07-01 00:00:00 UTC000
Jul 20142014-07-01 00:00:00 UTC2014-08-01 00:00:00 UTC000
Aug 20142014-08-01 00:00:00 UTC2014-09-01 00:00:00 UTC000
Sep 20142014-09-01 00:00:00 UTC2014-10-01 00:00:00 UTC000
Oct 20142014-10-01 00:00:00 UTC2014-11-01 00:00:00 UTC000
Nov 20142014-11-01 00:00:00 UTC2014-12-01 00:00:00 UTC000
Dec 20142014-12-01 00:00:00 UTC2015-01-01 00:00:00 UTC000
Jan 20152015-01-01 00:00:00 UTC2015-02-01 00:00:00 UTC000
Feb 20152015-02-01 00:00:00 UTC2015-03-01 00:00:00 UTC000
Mar 20152015-03-01 00:00:00 UTC2015-04-01 00:00:00 UTC101

What Hacker News says

3 sources
albertzeyer·Mar 29, 2015
I know some more. Some of them are made as libraries, some are just code examples, where you however could extract out some relevant code. * PyLearn, LISA labs, http://deeplearning.net/software/pylearn2/ * LSTM, http://deeplearning.net/tutorial/lstm.html#lstm * LSTM, https://github.com/skaae/nntools * LSTM, https://github.com/JonathanRaiman/theano_lstm * LSTM, https://github.com/mohammadpz/Recurrent-Neural-Networks * LSTM, http://christianherta.de/lehre/dataScience/machineLearning/n... * LSTM, https://gist.github.com/jpuigcerver/9358036 * LSTM + CTC, https://github.com/kastnerkyle/net * Speech modeling, LSTM, https://github.com/kastnerkyle/speech_density * FF + RNN, https://github.com/lmjohns3/theano-nets * FF + RNN, https://github.com/lmjohns3/theanets Speech: https://github.com/lmjohns3/arrnn-experiment/blob/master/tas... * FF, https://
Read the rest
github.com/benanne/Lasagne * RNN, https://github.com/pascanur/trainingRNNs * RNN, https://github.com/pascanur/GroundHog (Razvan Pascanu, KyungHyun Cho, Caglar Gulcehre) * RNN + CTC, https://github.com/shawntan/rnn-experiment (Shawn Tan) * RNN + CTC, https://github.com/shawntan/theano-ctc (Shawn Tan) * RNN + CTC, https://github.com/rakeshvar/rnn_ctc * RNN + CTC, OCR, https://github.com/rakeshvar/chamanti_ocr, https://github.com/rakeshvar/chamanti3_ocr * RNN, https://github.com/gwtaylor/theano-rnn * LSTM, RBM, DBN, https://github.com/kratarth1203/NeuralNet * RBM, https://github.com/benanne/morb * Q-learning, https://github.com/spragunr/deep_q_rl * Deep Generative Models, https://github.com/dpkingma/nips14-ssl * RNN, agents, “bricks”: https://github.com/bartvm/blocks * NTM, https://github.com/shawntan/neural-turing-machines/ * RL + CNN, https://github.com/brian473/neural_rl * DRAW RNN, https://github.com/jbornschein/draw And this is far from complete, there are countless more examples. Just search on GitHub. I just filtered out the ones which interest me (which at least have RNNs/LSTMs or some other interesting things).
In Keras: Theano-Based Deep Learning Library
kyzyl·Dec 5, 2013
Hm. And here I was thinking I'd seen most of the accelerated deep learning implementations. Although this one seems a touch incomplete as far as features go. There are some other for those not aware: [1] [Python] pylearn2 by the Lisa Lab @ uMontreal. Probably one of the beefiest of the bunch. Has a pretty extensive feature set, but IMO isn't the simplest to use. [2] [Python] MORB by Sander Dielman. Just restricted boltzman machines, but very nice and intuitive to use. Also built on Theano. [3] [C++] CUV by the AIS lab at Bonn University in Germany. Not strictly deep learning, but it's a fast CUDA-backed library written in templated C++ with bindings to other languages, including python. It's been used to implement deep learning models. [4] [Python] DeepNet by Nitish Srivastava and co. at U of Toronto. I don't have as much experience with
Read the rest
this one but it's built on cudamat and implements most of the main model types. Interestingly, they've taken the approach of using Google protocol buffers as a sustainable means of defining model configurations. [5] [MATLAB] DeepLearnToolbox by Rasmus Palm. If matlab is your thing, here you are. Implements most models you are likely to want. Not all of these developed equally as actively developed, but there is some good stuff above and I haven't found many instances where what I wanted wasn't (somewhat) readily available in at least one. I'm sure I'm forgetting one or two. [1] https://github.com/lisa-lab/pylearn2 [2] https://github.com/benanne/morb [3] https://github.com/deeplearningais/CUV [4] https://github.com/nitishsrivastava/deepnet/tree/master/deep... [5] https://github.com/rasmusbergpalm/DeepLearnToolbox
In GPU-Accelerated Deep Learning Library in Python

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