Python

Twitter Sentiment Analysis with Gensim Word2Vec and Keras Convolutional Networks

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Fork Word2Vec (https://code.google.com/archive/p/word2vec/) offers a very interesting alternative to classical NLP based on term-frequency matrices. In particular, as each word is embedded into a high-dimensional vector, it’s possible to consider a sentence like a sequence of points that determine an implicit geometry. For this reason, the idea of considering 1D convolutional classifiers (usually very efficient … [Read more…]

Assessing clustering optimality with instability index

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Many clustering algorithms need to define the number of desired clusters before fitting the model. This requirement can appear as a contradiction in an unsupervised scenario, however, in many real-word scenarios, the data scientist has often already an idea about a reasonable range of clusters. Unfortunately this doesn’t imply being able to define the optimal … [Read more…]

SVD Recommendations using Tensorflow

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Recommendation system based on the user-item matrix factorization have become more and more important thanks to powerful and distributable algorithms like ALS, but sometimes the number of users and/or items is not so huge and the computation can be done using directly a SVD (Singular Value Decomposition) algorithm. In this post, we’re going to discuss … [Read more…]

Lossy image autoencoders with convolution and deconvolution networks in Tensorflow

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Fork Autoencoders are a very interesting deep learning application because they allow a consistent dimensionality reduction of an entire dataset with a controllable loss level. The Jupyter notebook for this small project is available on the Github repository: https://github.com/giuseppebonaccorso/lossy_image_autoencoder. The structure of a generic autoencoder is represented in the following figure: The encoder is a function … [Read more…]

Machine Learning Algorithms

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My latest machine learning book has been published and will be available during the last week of July. From the back cover: In this book you will learn all the important Machine Learning algorithms that are commonly used in the field of data science. These algorithms can be used for supervised as well as unsupervised … [Read more…]

Keras-based Deepdream experiment based on VGG19

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Fork I’ve just published a repository (https://github.com/giuseppebonaccorso/keras_deepdream) with a Jupyter notebook containing a Deepdream (https://github.com/google/deepdream) experiment created with Keras and a pre-trained VGG19 convolutional network. The experiment (which is a work in progress) is based on some suggestions provided by the Deepdream team in this blog post but works in a slightly different way. I use a Gaussian … [Read more…]

Seq2Seq experiment with mathematical expressions

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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 the GIST: https://goo.gl/ZmH6Tf, where there are … [Read more…]

Keras-based Neural Artistic Style Transfer

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Fork 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/  

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

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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.

Reuters-21578 text classification with Gensim and Keras

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Fork 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: Topics Places People Organizations Exchanges However, most of them are unused and, looking at the distribution, it’s possible to notice a complete … [Read more…]