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I’m starting a new blog post series about the development of PyTorch’s computer vision library. I plan to discuss interesting upcoming features primarily from TorchVision and secondary from the PyTorch ecosystem. My target is to highlight new and in-development features and provide clarity of what’s happening in between the releases. Though the format is likely
New Blog series – Memoirs of a TorchVision developer Read More »
The last couple of weeks were super busy in “PyTorch Land” as we are frantically preparing the release of PyTorch v1.10 and TorchVision v0.11. In this 2nd instalment of the series, I’ll cover some of the upcoming features that are currently included in the release branch of TorchVision. Disclaimer: Though the upcoming release is packed
A sneak peek at TorchVision v0.11 – Memoirs of a TorchVision developer – 2 Read More »
Cryptography often feels like an ancient dark art, full of math-heavy concepts, rigid key sizes, and strict protocols. But what if you could rethink the idea of a “key” entirely? What if the key wasn’t a fixed blob of bits, but a living, breathing function? VernamVeil is an experimental cipher that explores exactly this idea.
VernamVeil: A Fresh Take on Function-Based Encryption Read More »
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 »
