Edge AI: ML inference in the browser

Tue, 18 Jun 2024 00:00:00 +0000

Recently, I stumbled across a guy who ported OpenAI’s Whisper model into c++, in various sizes, allowing the model to be run on-device, at impressive speed.

I went down a rabbit-hole, and found a whole family of popular models that had been ported to work on-device, from the browser:

yolo in the browser

Word2Vec Overview

Wed, 12 Jun 2024 00:00:00 +0000

In this article we will introduce the context surrounding word2vec, including the motivation for distributed word embeddings, how the Continious Bag-of-Words and Skip-gram algorithms work, and the advancements since the original paper was released. We will also go into the training of the neural network, so it is assumed you have some knowledge on this.

These 2 papers introduced word2vec to the world back in 2013:

paper1	paper2
[Word2Vec Paper 1](https://arxiv.org/pdf/1301.3781)- introducing CBOW and Skip-Gram	[Word2Vec Paper 2](https://arxiv.org/pdf/1310.4546)- Performance Improvements

Motivation

For many NLP tasks, we need to learn on data which can’t be easily represented numerically. For example, let’s look at the popular IMDB dataset, which gives reviews in one column, and a binary sentiment label in the next:

Machine Learning on jwhogg

Edge AI: ML inference in the browser

Word2Vec Overview

Motivation