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Articles by Ane Berasategi
Semantic search
A brief post on semantics, search, and semantic search ⚠️ READ THE ORIGINAL POST IN MY BLOG ⚠️ Semantics is a branch of linguistics studying the meanings of words, their symbolic use, also including their multiple meanings. This sentence is semantically ambiguous: it’s not clear if the author shoots an elephant while in his pajamas, or if he shot the elephant, which happened to be in his pajamas. Clearly, in this example only one of the two makes sense, but both cases are gramatically correct.
How to Build a Computer Vision Project Easily and without Code
A beginner friendly, no-code solution to your own fruit detector. Also, upload the model to the Internet for everyone to see! Photo by Kevin Crosby on Unsplash Do you want to build an app to detect something? Cats vs. dogs, detect how ripe a fruit is, find brands in images? Do you want to just label a few images, make a model and test it without much work?
Overview of Azure Resources for AI Applications
Photo by Sigmund on Unsplash It can be a daunting task to navigate cloud providers finding how to build your AI app in the cloud. This post goes over the main Azure AI resources for different AI applications. 1. Computer vision Computer Vision is the main Azure resource for image applications. In its most basic form, it supports image classification and multiclass object detection.
The Power of Synthetic Data - Part 1
In one of our previous blog posts and webinars, we’ve written about the power of synthetic data and Neurolabs’ synthetic data engine that transforms 3D assets into robust synthetic datasets for computer vision. At Neurolabs, we believe synthetic data holds the key for making computer vision and object detection more accurate, more affordable and more scalable.
Analysis of ELMo and BERT embedding similarity for similar sentences
Given similar sentences, how similar are their contextual embeddings? Let’s find out Photo by Myriam Jessier on Unsplash Embeddings are a key part of modern NLP, they encode the meaning of words or other linguistic units into vectors of numbers. The embedding of a specific word might seem random, but the idea is that similar words have similar embeddings, and opposite words have opposite embeddings. For example, imagine this is king’s embedding: [2, 3,1, 0, 5].
Back where it all started
How I’d start learning machine learning again (3-years in) By Daniel Bourke — 10 min read I’m underground, back where it all started. Sitting at the hidden cafe where I first met Mike. I’d been studying in my bedroom for the past 9-months and decided to step out of the cave. Half of me was concerned about having to pay $19 for breakfast (unless it’s Christmas, driving Uber on the weekends isn’t very lucrative), the other half about whether any of this study I’d been doing online meant anything.
Overview of tokenization algorithms in NLP
Introduction to tokenization methods, including subword, BPE, WordPiece and SentencePiece Photo by Hannah Wright on Unsplash This article is an overview of tokenization algorithms, ranging from word level, character level and subword level tokenization, with emphasis on BPE, Unigram LM, WordPiece and SentencePiece. It is meant to be readable by both experts and beginners alike. If any concept or explanation is unclear, please contact me and I will be happy to clarify whatever is needed.
What I learned during my Masters in NLP
Key lessons from a degree in a fast-paced research field Photo by Mikael Kristenson on Unsplash I had the idea to write this post by the Github graduation initiative and the post was originally published in dev.to. I started my Masters degree in NLP in LMU Munich, Germany in 2018. I had been interested in machine learning for about a year and had completed Coursera’s Deep Learning specialization. I wanted to learn NLP more deeply and become a kind of expert in the topic.
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