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Machine Learning :: Cosine Similarity for Vector Space Models (Part III)
* It has been a long time since I wrote the TF-IDF tutorial (Part I and Part II) and as I promissed, here is the continuation of the tutorial. Unfortunately I had no time to fix the previous tutorials...
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Luigi’s Codex Seraphinianus deep learning dreams
Some time ago I received the Luigi’s Codex Seraphinianus book, for those who still didn’t had a chance to take a look, I really recommend you to buy the book, this is the kind of book that will...
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Deep learning – Convolutional neural networks and feature extraction with Python
Convolutional neural networks (or ConvNets) are biologically-inspired variants of MLPs, they have different kinds of layers and each different layer works different than the usual MLP layers. If you...
View ArticleConvolutional Neural Networks Presentation
Just published this deck of slides of a presentation about Deep Learning and Convolutional Neural Networks. Deep Learning – Convolutional Neural Networks from Christian Perone
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Convolutional hypercolumns in Python
If you are following some Machine Learning news, you certainly saw the work done by Ryan Dahl on Automatic Colorization (Hacker News comments, Reddit comments). This amazing work uses pixel hypercolumn...
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Voynich Manuscript: word vectors and t-SNE visualization of some patterns
Update 17/01: reddit discussion thread. Update 19/01: hacker news thread. The codex The Voynich Manuscript is a hand-written codex written in an unknown system and carbon-dated to the early 15th...
View ArticleConvolutional Neural Networks – Architectural Zoo
Presentation about an “Achitectural Zoo” of different applications and architectures of CNNs. Presented at Machine Learning Meetup in Porto Alegre yesterday. Deep Learning – Convolutional Neural...
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JIT native code generation for TensorFlow computation graphs using Python and...
Update: Hacker News discussion here. The TensorFlow Computation Graph One of the most amazing components of the TensorFlow architecture is the computation graph that can be serialized using Protocol...
View ArticleIntroduction to Word Embeddings
Just publised a deck of slides for an Introduction to Word Embeddings, presented at our Machine Learning Meetup in Porto Alegre / RS / Brazil. Word Embeddings – Introduction from Christian Perone Here...
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The effective receptive field on CNNs
Given the interesting recent article on “The Emergence of a Fovea while Learning to Attend“, I decide to make a review of the paper written by Luo, Wenjie et al. called “Understanding the Effective...
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Privacy-preserving sentence semantic similarity using InferSent embeddings...
Privacy-preserving Computation Privacy-preserving computation or secure computation is a sub-field of cryptography where two (two-party, or 2PC) or multiple (multi-party, or MPC) parties can evaluate a...
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PyTorch – Internal Architecture Tour
Introduction Short intro to Python extension objects in C/C++ Zero-copy PyTorch Tensor to Numpy and vice-versa Tensor Storage Shared Memory DLPack: a hope for the Deep Learning frameworks Babel...
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NLP word representations and the Wittgenstein philosophy of language
I made an introductory talk on word embeddings in the past and this write-up is an extended version of the part about philosophical ideas behind word vectors. The aim of this article is to provide an...
View ArticleConcentration inequalities – Part I
Introduction Concentration inequalities, or probability bounds, are very important tools for the analysis of Machine Learning algorithms or randomized algorithms. In statistical learning theory, we...
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PyTorch 1.0 tracing JIT and LibTorch C++ API to integrate PyTorch into NodeJS
Today, at the PyTorch Developer Conference, the PyTorch team announced the plans and the release of the PyTorch 1.0 preview with many nice features such as a JIT for model graphs (with and without...
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Benford’s law emerges from deep language model
I was experimenting with the digits distribution from a pre-trained (weights from the OpenAI repository) Transformer language model (LM) and I found a very interesting correlation between the Benford’s...
View ArticleAnálise bayesiana dos microdados do ENEM do Rio Grande do Sul
* This post is in Portuguese. It’s a bayesian analysis of a Brazilian national exam. The main focus of the analysis is to understand the underlying factors impacting the participants performance on...
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Introducing EuclidesDB – A machine learning feature database
Past week I released the first public version of EuclidesDB. EuclidesDB is a multi-model machine learning feature database that is tightly coupled with PyTorch and provides a backend for including and...
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A sane introduction to maximum likelihood estimation (MLE) and maximum a...
It is frustrating to learn about principles such as maximum likelihood estimation (MLE), maximum a posteriori (MAP) and Bayesian inference in general. The main reason behind this difficulty, in my...
View ArticlePyData Montreal slides for the talk: PyTorch under the hood
These are the slides of the talk I presented on PyData Montreal on Feb 25th. It was a pleasure to meet you all ! Thanks a lot to Maria and Alexander for the invitation !
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