Marktechpost

NOVEMBER 10, 2023

NLP, or Natural Language Processing, is a field of AI focusing on human-computer interaction using language. NLP aims to make computers understand, interpret, and generate human language. Recent NLP research has focused on improving few-shot learning (FSL) methods in response to data insufficiency challenges.

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Topbots

APRIL 11, 2023

We’ll start with a seminal BERT model from 2018 and finish with this year’s latest breakthroughs like LLaMA by Meta AI and GPT-4 by OpenAI. BERT by Google Summary In 2018, the Google AI team introduced a new cutting-edge model for Natural Language Processing (NLP) – BERT , or B idirectional E ncoder R epresentations from T ransformers.

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NLP BERT Large Language Models Natural Language Processing 98

Flipboard

MARCH 27, 2023

Project Structure Accelerating Convolutional Neural Networks Parsing Command Line Arguments and Running a Model Evaluating Convolutional Neural Networks Accelerating Vision Transformers Evaluating Vision Transformers Accelerating BERT Evaluating BERT Miscellaneous Summary Citation Information What’s New in PyTorch 2.0?

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Neural Network Convolutional Neural Networks BERT Deep Learning 158

ODSC - Open Data Science

APRIL 11, 2023

A lot goes into NLP. Going beyond NLP platforms and skills alone, having expertise in novel processes, and staying afoot in the latest research are becoming pivotal for effective NLP implementation. We have seen these techniques advancing multiple fields in AI such as NLP, Computer Vision, and Robotics.

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NLP Data Science Large Language Models Data Scientist 96

Pickl AI

APRIL 6, 2025

AI Capabilities : Enables image recognition, NLP, and predictive analytics. Neural Networks: The Foundation A neural network is a computing system inspired by the biological neural networks that constitute animal brains. BERT) and decoder-only (e.g., Layered Architectures : Deep Learning uses CNNs, RNNs, and more.

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Neural Network Deep Learning Natural Language Processing Convolutional Neural Networks 52

IBM Journey to AI blog

AUGUST 1, 2023

Foundation models can be trained to perform tasks such as data classification, the identification of objects within images (computer vision) and natural language processing (NLP) (understanding and generating text) with a high degree of accuracy. An open-source model, Google created BERT in 2018. All watsonx.ai

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Mlearning.ai

MARCH 2, 2023

Pre-training of Deep Bidirectional Transformers for Language Understanding BERT is a language model that can be fine-tuned for various NLP tasks and at the time of publication achieved several state-of-the-art results. Finally, the impact of the paper and applications of BERT are evaluated from today’s perspective. 1 Impact V.2

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Towards AI

JANUARY 7, 2025

Introduction The idea behind using fine-tuning in Natural Language Processing (NLP) was borrowed from Computer Vision (CV). Despite the popularity and success of transfer learning in CV, for many years it wasnt clear what the analogous pretraining process was for NLP. How is Fine-tuning Different from Pretraining?

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BERT NLP Natural Language Processing Computer Vision 98

Flipboard

DECEMBER 6, 2023

Extractive summarization Extractive summarization is a technique used in NLP and text analysis to create a summary by extracting key sentences. In this post, we focus on the BERT extractive summarizer. BERT is a pre-trained language model that can be fine-tuned for a variety of tasks, including text summarization.

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BERT Large Language Models Artificial Intelligence Artificial Intelligence 167

Unite.AI

SEPTEMBER 18, 2023

Artificial Intelligence is a very vast branch in itself with numerous subfields including deep learning, computer vision , natural language processing , and more. NLP in particular has been a subfield that has been focussed heavily in the past few years that has resulted in the development of some top-notch LLMs like GPT and BERT.

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Marktechpost

SEPTEMBER 17, 2023

The creation of transformer-based NLP models has sparked advancements in designing and using transformer-based models in computer vision and other modalities. Large language models (LLMs) built on transformers, including ChatGPT and GPT-4, have demonstrated amazing natural language processing abilities.

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Large Language Models Natural Language Processing BERT Computer Vision 134

AWS Machine Learning Blog

FEBRUARY 24, 2023

This satisfies the strong MME demand for deep neural network (DNN) models that benefit from accelerated compute with GPUs. These include computer vision (CV), natural language processing (NLP), and generative AI models. There are two notebooks provided in the repo: one for load testing CV models and another for NLP.

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BERT NLP Computer Vision Neural Network 105

Pickl AI

JANUARY 26, 2025

Summary: Deep Learning models revolutionise data processing, solving complex image recognition, NLP, and analytics tasks. Applications in Computer Vision CNNs dominate computer vision tasks such as object detection, image classification, and facial recognition. Why are Transformer Models Important in NLP?

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Deep Learning Neural Network Convolutional Neural Networks Natural Language Processing 52

Heartbeat

SEPTEMBER 20, 2023

Pixabay: by Activedia Image captioning combines natural language processing and computer vision to generate image textual descriptions automatically. Image captioning integrates computer vision, which interprets visual information, and NLP, which produces human language.

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Natural Language Processing Computer Vision NLP Algorithm 52

Viso.ai

MARCH 26, 2024

This drastically enhanced the capabilities of computer vision systems to recognize patterns far beyond the capability of humans. In this article, we present 7 key applications of computer vision in finance: No.1: Applications of Computer Vision in Finance No. 1: Fraud Detection and Prevention No.2:

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Computer Vision Neural Network Machine Learning Convolutional Neural Networks 52

DataRobot Blog

APRIL 21, 2022

The natural follow-up question is if this increase in computing requirements has led to an increase in accuracy. The below graph illustrates accuracy versus model size for some of the more well-known computer vision models. Some of the models offer a slight improvement in accuracy but at an immense cost of computer resources.

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Deep Learning Machine Learning Computer Vision Natural Language Processing 145

Towards AI

JANUARY 14, 2025

Understanding Vision Transformers (ViTs) And what I learned while implementing them! Transformers have revolutionized natural language processing (NLP), powering models like GPT and BERT. But recently, theyve also been making waves in computer vision.

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Convolutional Neural Networks Neural Network Computer Vision Natural Language Processing 117

Unite.AI

OCTOBER 22, 2024

Put simply, if we double the input size, the computational needs can increase fourfold. AI models like neural networks , used in applications like Natural Language Processing (NLP) and computer vision , are notorious for their high computational demands.

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Neural Network Convolutional Neural Networks Deep Learning Natural Language Processing 147

Towards AI

JULY 21, 2023

The Vision of St. John on Patmos | Correggio NATURAL LANGUAGE PROCESSING (NLP) WEEKLY NEWSLETTER The NLP Cypher | 02.14.21 A key… github.com RpBERT BERT Model for multimodal name-entity recognition (NER) Connected Papers ? Heartbreaker Hey Welcome back! Natural reading order for the text line output. torch==1.2.0…

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NLP Neural Network Natural Language Processing BERT 52

Marktechpost

JUNE 4, 2024

Recent advances in CV and NLP have introduced challenges, such as the prohibitive cost of training large, state-of-the-art models. Additionally, preprocessing and metrics computation complexity has increased due to varied techniques and frameworks like JAX, TensorFlow, and PyTorch. Access to open-source pretrained models is crucial.

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Machine Learning NLP Natural Language Processing BERT 89

Towards AI

DECEMBER 28, 2023

The introduction of attention mechanisms has notably altered our approach to working with deep learning algorithms, leading to a revolution in the realms of computer vision and natural language processing (NLP). In 2023, we witnessed the substantial transformation of AI, marking it as the ‘year of AI.’

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Large Language Models Natural Language Processing Deep Learning Prompt Engineering 122

Flipboard

JUNE 20, 2023

Training experiment: Training BERT Large from scratch Training, as opposed to inference, is a finite process that is repeated much less frequently. Training a well-performing BERT Large model from scratch typically requires 450 million sequences to be processed. The first uses traditional accelerated EC2 instances.

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Machine Learning BERT Deep Learning ML Engineer 123

Heartbeat

JUNE 6, 2023

GCNs have been successfully applied to many domains, including computer vision and social network analysis. In recent years, researchers have also explored using GCNs for natural language processing (NLP) tasks, such as text classification , sentiment analysis , and entity recognition.

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NLP Convolutional Neural Networks Neural Network Natural Language Processing 59

Sebastian Ruder

JANUARY 6, 2020

This post gathers ten ML and NLP research directions that I found exciting and impactful in 2019.  Unsupervised pretraining was prevalent in NLP this year, mainly driven by BERT ( Devlin et al., 2019 ) and other variants.  Unsupervised pretraining was prevalent in NLP this year, mainly driven by BERT ( Devlin et al.,

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NLP ML Neural Network BERT 52

Sebastian Ruder

JANUARY 19, 2021

The selection of areas and methods is heavily influenced by my own interests; the selected topics are biased towards representation and transfer learning and towards natural language processing (NLP). This is less of a problem in NLP where unsupervised pre-training involves classification over thousands of word types.

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NLP ML Computer Vision Natural Language Processing 52

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.

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BERT Natural Language Processing Large Language Models Neural Network 83

AWS Machine Learning Blog

JANUARY 31, 2024

Embeddings play a key role in natural language processing (NLP) and machine learning (ML). Another common approach is to use large language models (LLMs), like BERT or GPT, which can provide contextualized embeddings for entire sentences. Why do we need an embeddings model?

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Natural Language Processing Machine Learning Computer Vision ML 135

Viso.ai

MARCH 20, 2024

Models like GPT 4, BERT, DALL-E 3, CLIP, Sora, etc., Use Cases for Foundation Models Applications in Pre-trained Language Models like GPT, BERT, Claude, etc. Applications in Computer Vision Models like ResNET, VGG, Image Captioning, etc. Traditional NLP methods heavily rely on models that are trained on labeled datasets.

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AI Developer AI Development Computer Vision BERT 52

Marktechpost

JUNE 11, 2024

Recent advancements in LLMs like BERT, T5, and GPT have revolutionized natural language processing (NLP) using transformers and pretraining-then-finetuning strategies. After testing the LLaVA-1.5 These models excel in various tasks, from text generation to question answering.

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Unite.AI

APRIL 26, 2024

The advancements in large language models have significantly accelerated the development of natural language processing , or NLP. The introduction of the transformer framework proved to be a milestone, facilitating the development of a new wave of language models, including OPT and BERT, which exhibit profound linguistic understanding.

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Large Language Models Natural Language Processing Convolutional Neural Networks Neural Network 162

AWS Machine Learning Blog

APRIL 7, 2023

As an example, smart venue solutions can use near-real-time computer vision for crowd analytics over 5G networks, all while minimizing investment in on-premises hardware networking equipment. In our example, we use the Bidirectional Encoder Representations from Transformers (BERT) model, commonly used for natural language processing.

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BERT Metadata Natural Language Processing ML 100

AWS Machine Learning Blog

JUNE 25, 2024

Unlike traditional natural language processing (NLP) approaches, such as classification methods, LLMs offer greater flexibility in adapting to dynamically changing categories and improved accuracy by using pre-trained knowledge embedded within the model.

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Automation Prompt Engineering Prompt Engineer Categorization 128

Sebastian Ruder

JANUARY 24, 2022

2021) 2021 saw many exciting advances in machine learning (ML) and natural language processing (NLP). In computer vision, supervised pre-trained models such as Vision Transformer [2] have been scaled up [3] and self-supervised pre-trained models have started to match their performance [4]. Why is it important?  

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AWS Machine Learning Blog

JUNE 12, 2023

Some of the other useful properties of the architecture compared to previous generations of natural language processing (NLP) models include the ability distribute, scale, and pre-train. Deep learning (DL) models with more layers and parameters perform better in complex tasks like computer vision and NLP.

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Deep Learning Auto-classification Computer Vision Machine Learning 86

AWS Machine Learning Blog

AUGUST 21, 2023

Predictions of these are now highly achievable from admission notes alone, through the use of natural language processing (NLP) algorithms [1]. Yet, in order to use the new predictions effectively, how these accurate BERT models are achieving their predictions still needs to be explained.

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Explainability BERT ML NLP 95

AWS Machine Learning Blog

MAY 10, 2023

We cover computer vision (CV), natural language processing (NLP), classification, and ranking scenarios for models and ml.c6g, ml.c7g, ml.c5, and ml.c6i SageMaker instances for benchmarking. 4xlarge; for the PyTorch NLP models, the cost savings is about 30–50% compared to c5 and c6i.4xlarge 4xlarge instances.

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Machine Learning Software Development Deep Learning ML 99

Heartbeat

MARCH 22, 2023

This week, we’ve got some fantastic pieces on NLP, including a discussion of RoBERTa, and some deep dives into computer vision. A Vision for the Future: How Computer Vision is Transforming Robotics — by Randy Barak a Computer vision is crucial to robotics because it allows robots to see and interpret their environment.

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BERT Computer Vision Robotics NLP 40