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Deep Learning (the favourite buzzword of late 2010s along with blockchain/bitcoin and Data Science/Machine Learning) has enabled us to do some really cool stuff the last few years. Other than the advances in algorithms (which admittedly are based on ideas already known since 1990s aka “Data Mining era”), the main reasons of its success can […]
UPDATE: Unfortunately my Pull-Request to Keras that changed the behaviour of the Batch Normalization layer was not accepted. You can read the details here. For those of you who are brave enough to mess with custom implementations, you can find the code in my branch. I might maintain it and merge it with the latest
The Batch Normalization layer of Keras is broken Read More »
I am really excited to announce that, after several months of development, the new version of Datumbox is out! The 0.7.0 version brings multi-threading support, fast disk-based training for datasets that don’t fit in memory, several algorithmic enhancements and better architecture. Download it now from Github or Maven Central Repository. What is new? The focus
Datumbox Machine Learning Framework 0.7.0 Released Read More »
Datumbox Framework v0.8.0 is out and packs several powerful features! This version brings new Preprocessing, Feature Selection and Model Selection algorithms, new powerful Storage Engines that give better control on how the Models and the Dataframes are saved/loaded, several pre-trained Machine Learning models and lots of memory & speed improvements. Download it now from Github
Datumbox Machine Learning Framework version 0.8.0 released Read More »
The dstat is an awesome little tool which allows you to get resource statistics for your Linux box. It has a modular architecture which allows you to develop additional plugins and it’s easy to use. Recently I was profiling a Deep Learning pipeline developed with Keras and Tensorflow and I needed detailed statistics about the
Getting the GPU usage of NVIDIA cards with the Linux dstat tool Read More »
The ALS algorithm introduced by Hu et al., is a very popular technique used in Recommender System problems, especially when we have implicit datasets (for example clicks, likes etc). It can handle large volumes of data reasonably well and we can find many good implementations in various Machine Learning frameworks. Spark includes the algorithm in
Drilling into Spark’s ALS Recommendation algorithm Read More »
In the previous articles we discussed in detail the Dirichlet Process Mixture Models and how they can be used in cluster analysis. In this article we will present a Java implementation of two different DPMM models: the Dirichlet Multivariate Normal Mixture Model which can be used to cluster Gaussian data and the Dirichlet-Multinomial Mixture Model
Clustering with Dirichlet Process Mixture Model in Java Read More »
I am happy to announce that the Datumbox Machine Learning Framework is now open sourced under GPL 3.0 and you can download its code from Github! What is this Framework? The Datumbox Machine Learning Framework is an open-source framework written in Java which enables the rapid development of Machine Learning models and Statistical applications. It
New open-source Machine Learning Framework written in Java Read More »
In this guide we are going to discuss how to install and use the Datumbox Machine Learning framework in your Java projects. Since almost all of the code is written in Java, using it is as simple as including it as dependency in your Java project. Nevertheless a couple of classes (DataEnvelopmentAnalysis and LPSolver) use
How to install and use the Datumbox Machine Learning Framework Read More »
The new version of Datumbox Machine Learning Framework has been released! Download it now from Github or Maven Central Repository. What is new? The main focus of version 0.6.0 is to extend the Framework to handle Large Data, improve the code architecture and the public APIs, simplify data parsing, enhance the documentation and move to
Datumbox Machine Learning Framework 0.6.0 Released Read More »
