Mlearning.ai

APRIL 8, 2023

Charting the evolution of SOTA (State-of-the-art) techniques in NLP (Natural Language Processing) over the years, highlighting the key algorithms, influential figures, and groundbreaking papers that have shaped the field. NLP algorithms help computers understand, interpret, and generate natural language.

NLP 98

NLP Neural Network Natural Language Processing Convolutional Neural Networks 98

ML @ CMU

FEBRUARY 24, 2023

Prompting has emerged as a promising approach to solving a wide range of NLP problems using large pre-trained language models (LMs), including left-to-right models such as GPT s and masked LMs such as BERT , RoBERTa , etc. BERT and GPTs) for both classification and generation tasks. Webson and Pavlick (2021) , Zhao et al.,

BERT 99

BERT NLP Large Language Models Natural Language Processing 99

Towards AI

JULY 20, 2023

As the capabilities of high-powered computers and ML algorithms have grown, so have opportunities to improve the SLR process. BioBERT and similar BERT-based NER models are trained and fine-tuned using a biomedical corpus (or dataset) such as NCBI Disease, BC5CDR, or Species-800. a text file with one word per line).

Data Extraction 98

Data Extraction NLP Natural Language Processing Automation 98

Snorkel AI

MARCH 1, 2023

BERT BERT, an acronym that stands for “Bidirectional Encoder Representations from Transformers,” was one of the first foundation models and pre-dated the term by several years. BERT proved useful in several ways, including quantifying sentiment and predicting the words likely to follow in unfinished sentences.

BERT 83

BERT Natural Language Processing Large Language Models Neural Network 83

Mlearning.ai

JUNE 14, 2023

LLMs (Foundational Models) 101: Introduction to Transformer Models Transformers, explained: Understand the model behind GPT, BERT, and T5 — YouTube Illustrated Guide to Transformers Neural Network: A step by step explanation — YouTube Attention Mechanism Deep dive. YouTube BERT Research — Ep.

Neural Network 52

Neural Network BERT Large Language Models Natural Language Processing 52

Sebastian Ruder

NOVEMBER 14, 2022

Research models such as BERT and T5 have become much more accessible while the latest generation of language and multi-modal models are demonstrating increasingly powerful capabilities. For a recent study [3] , we similarly reviewed papers from ACL 2021 and found that almost 70% of papers only evaluate on English. 2020) (Ahia et al.,

Natural Language Processing 52

Natural Language Processing NLP Computational Linguistics BERT 52

NVIDIA

FEBRUARY 11, 2025

In a 2021 paper, researchers reported that foundation models are finding a wide array of uses. He calls the blending of AI algorithms and model architectures homogenization , a trend that helped form foundation models. See chart below.) The field continues to move fast.

Neural Network 57

Neural Network Large Language Models Robotics BERT 57

The Stanford AI Lab Blog

MAY 31, 2022

Language Model Pretraining Language models (LMs), like BERT 1 and the GPT series 2 , achieve remarkable performance on many natural language processing (NLP) tasks. To achieve this, we first chunk each document into segments of roughly 256 tokens, which is half of the maximum BERT LM input length.

BERT 40

BERT Natural Language Processing NLP Neural Network 40

The Stanford AI Lab Blog

JANUARY 21, 2022

Indeed, this recipe of massive, diverse datasets combined with scalable offline learning algorithms (e.g. Replicating these impressive generalization and adaptation capabilities in robot learning algorithms would certainly be a step toward robots that can be used in unstructured, real world environments. Chen, Suraj Nair, Chelsea Finn.

Robotics 40

Robotics Computational Linguistics BERT Computer Vision 40

NVIDIA Developer

MAY 20, 2021

Chung , posted May 21 2021 at 12:03AM Businesses worldwide are using artificial intelligence to solve their greatest challenges. Languages: English Overview Title: Fundamentals of Deep Learning Date: Thursday, June 10, 2021, 09:00 AM +05 Status: Open Businesses worldwide are using artificial intelligence to solve their greatest challenges.

Natural Language Processing 40

Natural Language Processing Deep Learning Neural Network NLP 40

DataRobot Blog

FEBRUARY 2, 2023

These models are trained on massive amounts of text data using deep learning algorithms. As an example, getting started with a BERT model for question answering (bert-large-uncased-whole-word-masking-finetuned-squad) is as easy as executing these lines: !pip writefile $BASE_PATH/custom.py """ Copyright 2021 DataRobot, Inc.

BERT 52

BERT Large Language Models Natural Language Processing Machine Learning 52

Bugra Akyildiz

DECEMBER 18, 2022

Dragon is a new foundation model (improvement of BERT) that is pre-trained jointly from text and knowledge graphs for improved language, knowledge and reasoning capabilities. Dragon can be used as a drop-in replacement for BERT. The following is my hypothesis- 🧵⬇ 7:42 PM ∙ Sep 8, 2021 154 Likes 13 Retweets

Neural Network 52

Neural Network ChatGPT Machine Learning Deep Learning 52

Viso.ai

JUNE 12, 2024

GPT-2 is not just a language model like BERT, it can also generate text. The algorithm learns contextual relationships between words in the texts provided as training examples and then generates a new text. The transformer provides a mechanism based on encoder-decoders to detect input-output dependencies. trillion words.

Large Language Models 52

Large Language Models ChatGPT Neural Network Computer Vision 52

John Snow Labs

JUNE 5, 2023

Let's just peek into the pre-BERT world… For creating models, we need words to be represented in a form n understood by the training network, ie, numbers. Thus many algorithms were used to convert words into vectors or more precisely, word embeddings. One of the earliest algorithms used for this purpose is word2vec.

NLP 52

NLP Natural Language Processing Deep Learning Algorithm 52

Applied Data Science

DECEMBER 23, 2022

In our review of 2019 we talked a lot about reinforcement learning and Generative Adversarial Networks (GANs), in 2020 we focused on Natural Language Processing (NLP) and algorithmic bias, in 202 1 Transformers stole the spotlight. This trend started in 2021, with OpenAI Codex , a GPT-3 based tool. What happened? Who should I follow?

Neural Network 52

Neural Network Data Science AI AI 52

Viso.ai

FEBRUARY 25, 2023

Popular image recognition algorithms include ResNet , VGG , YOLOv3 , and YOLOv7. No 2018 Oct BERT Pre-trained transformer models started dominating the NLP field. Yes 2021 – today ViT Variants There are several ViT variants, including DeiT, PVT, TNT, Swin, and CSWin (2022). Top-1 accuracy on ImageNet-1K, 53.9

Convolutional Neural Networks 52

Convolutional Neural Networks Computer Vision Neural Network Natural Language Processing 52

Chatbots Life

MAY 12, 2023

There are many approaches to language modelling, we can for example ask the model to fill in the words in the middle of a sentence (as in the BERT model) or predict which words have been swapped for fake ones (as in the ELECTRA model). The most recent training data is of ChatGPT from 2021 September.

ChatGPT 90

ChatGPT Software Engineer NLP ML Engineer 90

Sebastian Ruder

JANUARY 19, 2021

A plethora of language-specific BERT models have been trained for languages beyond English such as AraBERT ( Antoun et al., This is similar to findings for distilling an inductive bias into BERT ( Kuncoro et al.,  Reinforcement learning algorithms have a multitude of practical implications ( Bellemare et al.,

NLP 52

NLP ML Computer Vision Natural Language Processing 52

Mlearning.ai

JANUARY 17, 2024

Major milestones in the last few years comprised BERT (Google, 2018), GPT-3 (OpenAI, 2020), Dall-E (OpenAI, 2021), Stable Diffusion (Stability AI, LMU Munich, 2022), ChatGPT (OpenAI, 2022). Generative AI models usually have millions of neurons and billions of synapses (aka „ parameters “).

Generative AI 98

Generative AI Prompt Engineer Prompt Engineering Large Language Models 98

Topbots

JUNE 29, 2023

After parsing a question, an algorithm encodes it into a structured logical form in the query language of choice, such as SQL. High-quality , so that the Text2SQL algorithm does not have to deal with excessive noise (inconsistencies, empty values etc.) in the data. section “Enriching the prompt with database information”).

Algorithm 52

Algorithm Auto-complete Data Scientist Auto-classification 52

Topbots

JUNE 6, 2023

Autoencoding models, which are better suited for information extraction, distillation and other analytical tasks, are resting in the background — but let’s not forget that the initial LLM breakthrough in 2018 happened with BERT, an autoencoding model.

LLM 105

LLM ChatGPT Prompt Engineer Prompt Engineering 105