Seq2Seq experiment with mathematical expressions

After reading the article "How to Learn to Add Numbers with seq2seq Recurrent Neural Networks" by Jason Brownlee (that I suggest reading before going on), I've decided to try an experiment with more complex expressions like: -(10+5) or 4+ -2, etc. The code (with some extra information) is published on […]
Read More

Keras-based Neural Artistic Style Transfer

I've just moved my Keras-based Neural Artistic Style Transfer GIST to a dedicated repository: https://github.com/giuseppebonaccorso/Neural_Artistic_Style_Transfer. Please refer always to it because the GIST is not more maintained. The original post is: https://www.bonaccorso.eu/2016/11/13/neural-artistic-style-transfer-experiments-with-keras/  
Read More

BBC News classification algorithm comparison

BBC News dataset (available for download in Insight Project Resources website) is made up of 2225 newslines classified into 5 categories (Politics, Sport, Entertainment, Tech, Business) and, similarly to Reuters-21578, it can be adopted in order to test both the efficacy and the efficiency of different classification strategies. In the repository: https://github.com/giuseppebonaccorso/bbc_news_classification_comparison, I've […]
Read More

Neural artistic style transfer experiments with Keras

Artistic style transfer using neural networks is a technique proposed by Gatys, Ecker and Bethge in the paper: arXiv:1508.06576 [cs.CV] which exploits a trained convolutional network in order to reconstruct the elements of a picture adopting the artistic style of a particular painting. I've written a Python snippet (available in […]
Read More

OpenAI-Gym Cartpole-v0 LSTM experiment with Keras (Theano)

OpenAI-Gym evaluation page: https://gym.openai.com/evaluations/eval_JxPKNwd1QjaofWkaE4aLfQ. Below there's the whole Gist page containing the full Python code. It has been developed and tested with Theano/GPU support, but it can easily work with CPU-only support. Any comment or suggestion is welcome! View the code on Gist.
Read More

Deep learning, God and Zen emptiness

It's quite terrifying discovering how deep learning can work smoothly while performing tasks that even human beings consider difficult. Undoubtedly it's stunning, extraordinary, but at the same time, it's almost a terrible discovery. I must confess that I've never liked reductionism and I still continue thinking about and "ego" behind […]
Read More

CIFAR-10 image classification with Keras ConvNet

CIFAR-10 is a small image (32 x 32) dataset made up of 60000 images subdivided into 10 main categories. Check the web page in the reference list in order to have further information about it and download the whole set. Considering our current screen resolutions, it's not difficult saying that […]
Read More

Reuters-21578 text classification with Gensim and Keras

Reuters-21578 is a collection of about 20K news-lines (see reference for more information, downloads and copyright notice), structured using SGML and categorized with 672 labels. They are diveded into five main categories:   However, most of them are unused and, looking at the distribution, it's possible to notice a complete […]
Read More

Fixed-delay smoothing in HMM with Numpy

Let's consider a Hidden Markov Model describing a sequential problem: a system has three internal (hidden) states: However, we can observe only a sensor (globally connected with different sub-systems) which states are represented by three colors (green, yellow and red), representing respectively a normal, partially dangerous and absolutely risky situation. […]
Read More

Theano GPU vs pure Numpy (CPU)

In this benchmark, I've used a Windows 10 Pro 64 Bit computer with Intel Core i7 6700HQ 2.60 GHz with 32 Gb RAM and NVIDIA GeForce GTX 960M. As a programming environment, I've used Python 2.7 (Anaconda distribution) and Jupyter. The task is very simple, integrating this expression (simple but […]
Read More
Page 1 of 212