Marktechpost

NOVEMBER 10, 2023

AugGPT’s framework consists of fine-tuning BERT on the base dataset, generating augmented data (Daugn) using ChatGPT, and fine-tuning BERT with the augmented data. The few-shot text classification model is based on BERT, using cross-entropy and contrastive loss functions to classify samples effectively.

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Unite.AI

MAY 29, 2024

This advancement has spurred the commercial use of generative AI in natural language processing (NLP) and computer vision, enabling automated and intelligent data extraction. Source: A pipeline on Generative AI This figure of a generative AI pipeline illustrates the applicability of models such as BERT, GPT, and OPT in data extraction.

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Data Extraction Neural Network Large Language Models NLP 271

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 Architecture III.2

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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:

Computer Vision 52

Computer Vision Neural Network Machine Learning Convolutional Neural Networks 52

Unite.AI

APRIL 2, 2024

These problems, commonly referred to as degradations in low-level computer vision, can arise from difficult environmental conditions like heat or rain or from limitations of the camera itself. An image can convey a great deal, yet it may also be marred by various issues such as motion blur, haze, noise, and low dynamic range.

Computer Vision 130

Computer Vision Neural Network Convolutional Neural Networks Deep Learning 130

Towards AI

JANUARY 7, 2025

Introduction The idea behind using fine-tuning in Natural Language Processing (NLP) was borrowed from Computer Vision (CV). In the case of BERT (Bidirectional Encoder Representations from Transformers), learning involves predicting randomly masked words (bidirectional) and sentence-order prediction.

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BERT NLP Natural Language Processing Computer Vision 98

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.

Natural Language Processing 52

Natural Language Processing Computer Vision NLP Algorithm 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.

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Unite.AI

NOVEMBER 6, 2023

For instance, NN used for computer vision tasks (object detection and image segmentation) are called convolutional neural networks (CNNs) , such as AlexNet , ResNet , and YOLO. Prominent transformer models include BERT , GPT-4 , and T5. Do We Still Need Traditional Machine Learning Algorithms?

Machine Learning 203

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AWS Machine Learning Blog

NOVEMBER 22, 2023

With eight Qualcomm AI 100 Standard accelerators and 128 GiB of total accelerator memory, customers can also use DL2q instances to run popular generative AI applications, such as content generation, text summarization, and virtual assistants, as well as classic AI applications for natural language processing and computer vision.

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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 133

NVIDIA

SEPTEMBER 11, 2023

Grace Hopper Superchips and H100 GPUs led across all MLPerf’s data center tests, including inference for computer vision, speech recognition and medical imaging, in addition to the more demanding use cases of recommendation systems and the large language models ( LLMs ) used in generative AI.

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Computer Vision LLM BERT Deep Learning 121

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.

Machine Learning 123

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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 146

Marktechpost

JUNE 7, 2023

The transformer models like BERT and T5 have recently got popular due to their excellent properties and have utilized the idea of self-supervision in Natural Language Processing tasks. Self-supervised learning is being prominently used in Artificial Intelligence to develop intelligent systems.

Natural Language Processing 98

Natural Language Processing BERT Computer Vision Artificial Intelligence 98

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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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 116

Unite.AI

MAY 24, 2024

In modern machine learning and artificial intelligence frameworks, transformers are one of the most widely used components across various domains including GPT series, and BERT in Natural Language Processing, and Vision Transformers in computer vision tasks.

Convolutional Neural Networks 130

Convolutional Neural Networks Neural Network BERT Machine Learning 130

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. Foundation models are recent developments in artificial intelligence (AI).

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AI Developer AI Development Computer Vision BERT 52

Pickl AI

JANUARY 26, 2025

Applications in Computer Vision CNNs dominate computer vision tasks such as object detection, image classification, and facial recognition. Transformers are the foundation of many state-of-the-art architectures, such as BERT and GPT.

Deep Learning 52

Deep Learning Neural Network Convolutional Neural Networks Natural Language Processing 52

AWS Machine Learning Blog

MARCH 20, 2023

Quantization is a technique to reduce the computational and memory costs of running inference by representing the weights and activations with low-precision data types like 8-bit integer (INT8) instead of the usual 32-bit floating point (FP32). In the following example figure, we show INT8 inference performance in C6i for a BERT-base model.

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Marktechpost

NOVEMBER 20, 2023

Recently, convolutions have emerged as a critical primitive for sequence modeling, supporting state-of-the-art performance in language modeling, time-series analysis, computer vision, DNA modeling, and more. points better perplexity and allows M2-BERT-base to achieve up to 3.3 and by up to 5.60

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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.

NLP 98

NLP BERT Large Language Models Natural Language Processing 98

Towards AI

AUGUST 24, 2023

While domains such as language and computer vision still dominate the headlines, speech is becoming an increasingly important domain. Meta AI employs a self-supervised speech encoder known as w2v-BERT 2.0 — an enhanced iteration of w2v-BERT distinguished by improved training stability and representation quality.

BERT 98

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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 101

Towards AI

DECEMBER 28, 2023

The advent of more powerful personal computers paved the way for the gradual acceptance of deep learning-based methods. 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).

Large Language Models 122

Large Language Models Natural Language Processing Deep Learning Prompt Engineering 122

Marktechpost

JUNE 4, 2024

Table 1 compares the average time per training or inference step for models like SAM, Gemma, BERT, and Mistral across different versions and frameworks of Keras. KerasCV and KerasNLP publish all pretrained models on Kaggle Models, which are accessible in Kaggle competition notebooks even in Internet-off mode.

Machine Learning 88

Machine Learning NLP Natural Language Processing BERT 88

Unite.AI

SEPTEMBER 9, 2024

Similarly, computer vision methods are progressively embracing extensive data scales for pretraining. AIM explores the scalability of autoregressive visual pretraining similar to BERT for vision transformers. The pursuit of large-scale 3D human digitization remains a pivotal goal in computer vision.

Computer Vision 146

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AWS Machine Learning Blog

MAY 8, 2024

The following example shows how to fine-tune a BERT base model identified by model_id=huggingface-tc-bert-base-cased on a custom training dataset. He has experience in working on a diverse range of machine learning problems within the domain of natural language processing, computer vision, and time series analysis.

Algorithm 123

Algorithm BERT Machine Learning Natural Language Processing 123

Viso.ai

APRIL 26, 2024

About us: Viso Suite is the end-to-end computer vision infrastructure for enterprises. Typically, transformer-based architectures like BERT (Bidirectional Encoder Representations from Transformers) and GPT (Generative Pre-trained Transformer) serve as the foundations for these systems.

Convolutional Neural Networks 52

Convolutional Neural Networks Computer Vision Neural Network Natural Language Processing 52

Marktechpost

JANUARY 20, 2024

Pseudo-labels from advanced cross-encoder models like BERT are one of the methods for generating synthetic data for domain adaptation of dense passage retrievers.

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BERT Large Language Models LLM AI 131

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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AWS Machine Learning Blog

JANUARY 17, 2023

Computer vision. BERT + Random Forest. After the tuning jobs are complete, we deploy the model that gives the best evaluation metric score on the validation dataset, perform inference on the same hold-out test dataset we did in the previous section, and compute evaluation metrics. BERT + Random Forest. 0.81115.

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Categorization BERT Machine Learning Neural Network 96

Unite.AI

APRIL 26, 2024

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. The advancements in large language models have significantly accelerated the development of natural language processing , or NLP.

Large Language Models 159

Large Language Models Natural Language Processing Convolutional Neural Networks Neural Network 159

Marktechpost

NOVEMBER 22, 2024

Language and vision models have experienced remarkable breakthroughs with the advent of Transformer architecture. Models like BERT and GPT have revolutionized natural language processing, while Vision Transformers have achieved significant success in computer vision tasks.

Natural Language Processing 82

Natural Language Processing BERT Computer Vision Deep Learning 82

AWS Machine Learning Blog

JUNE 12, 2023

Deep learning (DL) models with more layers and parameters perform better in complex tasks like computer vision and NLP. You can also learn and run sample codes for BERT, GPT-2, and GPT-J on the Amazon SageMaker Examples public repository. He focuses on deep learning, including NLP and computer vision domains.

Deep Learning 94

Deep Learning Auto-classification Computer Vision Machine Learning 94

PyImageSearch

AUGUST 14, 2023

As shown in Figure 10 , the module uses a BERT (bidirectional encoder representations from transformers) model, which performs classification on top of classification token ([CLS]) output embedding. Do you think learning computer vision and deep learning has to be time-consuming, overwhelming, and complicated?

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Computer Vision Deep Learning Algorithm Neural Network 82