Analytics Vidhya

OCTOBER 19, 2022

Source: Canva Introduction In 2018, Google AI researchers came up with BERT, which revolutionized the NLP domain. Later in 2019, the researchers proposed the ALBERT (“A Lite BERT”) model for self-supervised learning of language representations, which shares the same architectural backbone as BERT. The key […].

Supervised Learning 338

Supervised Learning Data Science Analytics Analytics 338

Explosion

MAY 17, 2023

Once you’re past prototyping and want to deliver the best system you can, supervised learning will often give you better efficiency, accuracy and reliability than in-context learning for non-generative tasks — tasks where there is a specific right answer that you want the model to find. That’s not a path to improvement.

Supervised Learning 135

Supervised Learning Natural Language Processing Clustering Machine Learning 135

Explosion

JANUARY 12, 2018

spaCy’s Machine Learning library for NLP in Python. Bringing pjreddie’s DarkNet out of the shadows #yolo Originally developed for testing active learning-powered image annotation with Prodigy. The DarkNet code base is a great way to learn about implementing neural networks from scratch. cython-blis ?

Machine Learning 52

Machine Learning Machine Learning Supervised Learning Python 52

Pickl AI

APRIL 10, 2023

As per the recent report by Nasscom and Zynga, the number of data science jobs in India is set to grow from 2,720 in 2018 to 16,500 by 2025. Top 5 Colleges to Learn Data Science (Online Platforms) 1. The amount increases with experience and varies from industry to industry. offers a host of courses.

Data Science 52

Data Science Machine Learning Machine Learning Python 52

AWS Machine Learning Blog

AUGUST 7, 2023

The transformer architecture was the foundation for two of the most well-known and popular LLMs in use today, the Bidirectional Encoder Representations from Transformers (BERT) 4 (Radford, 2018) and the Generative Pretrained Transformer (GPT) 5 (Devlin 2018). AWS ProServe MLDT used this blueprint as its basis for fine-tuning.

AWS 98

AWS ML ML Data Science 98

Mlearning.ai

JULY 31, 2023

One example is the Pairwise Inner Product (PIP) loss, a metric designed to measure the dissimilarity between embeddings using their unitary invariance (Yin and Shen, 2018). Yin and Shen (2018) accompany their research with a code implementation on GitHub here. Fortunately, there is; use an embedding loss. Equation 2.3.1. and Auli, M.,

Deep Learning 52

Deep Learning Deep Learning Supervised Learning Algorithm 52

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.

ML 98

ML ML Data Scientist AWS 98