Explosion

JANUARY 12, 2018

spaCy In 2017 spaCy grew into one of the most popular open-source libraries for Artificial Intelligence. Highlights included: Developed new deep learning models for text classification, parsing, tagging, and NER with near state-of-the-art accuracy. spaCy’s Machine Learning library for NLP in Python. Released Prodigy v1.0,

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Explosion

APRIL 1, 2017

Prodigy features many of the ideas and solutions for data collection and supervised learning outlined in this blog post. It’s a cloud-free, downloadable tool and comes with powerful active learning models. Transfer learning and better annotation tooling are both key to our current plans for spaCy and related projects.

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Explosion

AUGUST 3, 2017

In order to take full advantage of this strategy, Prodigy is provided as a Python library and command line utility, with a flexible web application. The components are wired togther into a recipe , by adding the @recipe decorator to any Python function. Recipes can start the web service by return a dictionary of components.

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Mlearning.ai

JULY 31, 2023

2017) paper, vector embeddings have become a standard for training text-based DL models. Data2Vec: A General Framework For Self-Supervised Learning in Speech, Vision and Language. It is none other than the legendary Vector Embeddings! Without further ado, let’s dive right in! A vector embedding is an object (e.g., and Auli, M.,

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AWS Machine Learning Blog

AUGUST 16, 2023

Training machine learning (ML) models to interpret this data, however, is bottlenecked by costly and time-consuming human annotation efforts. One way to overcome this challenge is through self-supervised learning (SSL). The types of land cover in each image, such as pastures or forests, are annotated according to 19 labels.

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Mlearning.ai

AUGUST 6, 2023

Towards the end of my studies, I incorporated basic supervised learning into my thesis and picked up Python programming at the same time. I also started on my data science journey by attending the Coursera specialization by Andrew Ng —  Deep Learning. That was in 2017.

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AWS Machine Learning Blog

AUGUST 7, 2023

The Transformer architecture 3 (Vaswani, 2017) was a breakthrough improvement on the encoder-decoder; it introduced the concept of self-attention , which allowed the model to focus its attention on different words on the input and output phrases. AWS ProServe MLDT used this blueprint as its basis for fine-tuning.

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