Probably Approximately a Scientific Blog

JANUARY 3, 2016

Figure 1: an illustration of one-hot vectors of cat , cats and dog. Figure 2: an illustration of distributional vectors of food , eat and laptop. 2] , but gained extreme popularity with word2vec [3] in 2013. Figure 4: two-dimensional projection of word2vec vectors of countries and their capitals, from the paper.

Natural Language Processing 52

Natural Language Processing Algorithm Deep Learning NLP 52

Jay Alammar

OCTOBER 3, 2022

Just like in word2vec , the training process also needs to include negative examples of images and captions that don’t match, and the model needs to assign them low similarity scores.

Neural Network 98

Neural Network Explainability Computer Vision BERT 98

Mlearning.ai

JANUARY 17, 2024

1x: Evolution of Generative AI 1x: Timeline of Generative AI 1x: Exiting, amazing, and sometimes a little bit spooky: Early precursors of LLMs 2x: AI timeline , and some striking data visualizations 90+: Current and past notable AI projects Evolutionary Tree of Large Language Models: From Word2Vec to GPT-4. Image credit: Yang, Jingfeng et.

Generative AI 98

Generative AI Prompt Engineer Prompt Engineering Large Language Models 98

Explosion

MAY 8, 2016

It also comes with word vectors representations, produced from word2vec. To illustrate the difference, consider the example below. The vectors were produced using the skip-gram with negative sampling word2vec algorithm using Gensim , with a context window of 2. doc = de(u'Ich möchte gern zum Essen eine Tasse Kaffee bestellen.')

Convolutional Neural Networks 40

Convolutional Neural Networks NLP Algorithm Neural Network 40

Sebastian Ruder

AUGUST 18, 2019

Our go-to definition throughout this post will be the following, which is illustrated in the diagram below: Transfer learning is a means to extract knowledge from a source setting and apply it to a different target setting. An illustration of the process of transfer learning. Early approaches such as word2vec ( Mikolov et al.,

NLP 75

NLP BERT Natural Language Processing Computational Linguistics 75

ML Review

DECEMBER 16, 2019

where a root is in the middle of the ball and branches & leaves are placed closer to the “circumference” of the ball (check out the illustration). at the illustrative example nearby from the paper. as a continuous version of trees ?where On the other hand, training and optimization of hyperbolic nets is arguably hard.

Machine Learning 52

Machine Learning Neural Network Explainability NLP 52

The MLOps Blog

NOVEMBER 14, 2023

The TensorFlow embedding projector gives everyone access to interactive visualizations of well-known embeddings like standard Word2vec corpora. One common approach for visualizing a deep learning model’s architecture is graphs illustrating the connections and data flow between its components.

Deep Learning 111

Deep Learning Neural Network Convolutional Neural Networks Data Scientist 111

Probably Approximately a Scientific Blog

AUGUST 14, 2018

Then, go over the text and replace each noun with its most similar word in word2vec. (It It doesn’t have to be word2vec and doesn’t have to be only for nouns - this is what I did. Apart from being funny, this is a good illustration of this phenomenon: words have been replaced with other words in different relationships with them.

Deep Learning 52

Deep Learning NLP Neural Network Machine Learning 52