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 Natural Language Processing 130

Unite.AI

AUGUST 8, 2023

The Technologies Behind Generative Models Generative models owe their existence to deep neural networks, sophisticated structures designed to mimic the human brain's functionality. By capturing and processing multifaceted variations in data, these networks serve as the backbone of numerous generative models.

Generative AI 243

Generative AI ChatGPT Neural Network Convolutional Neural Networks 243

ODSC - Open Data Science

APRIL 28, 2023

Be sure to check out his talk, “ Bagging to BERT — A Tour of Applied NLP ,” there! Deep learning refers to the use of neural network architectures, characterized by their multi-layer design (i.e. cats” component of Docs, for which we’ll be training a text categorization model to classify sentiment as “positive” or “negative.”

Deep Learning 98

Deep Learning Convolutional Neural Networks Neural Network NLP 98

Viso.ai

DECEMBER 22, 2023

This leap forward is due to the influence of foundation models in NLP, such as GPT and BERT. The Segment Anything Model Technical Backbone: Convolutional, Generative Networks, and More Convolutional Neural Networks (CNNs) and Generative Adversarial Networks (GANs) play a foundational role in the capabilities of SAM.

Convolutional Neural Networks 52

Convolutional Neural Networks Computer Vision Neural Network Auto-classification 52

Viso.ai

MARCH 26, 2024

Uniquely, this model did not rely on conventional neural network architectures like convolutional or recurrent layers. without conventional neural networks. Source ) This has led to groundbreaking models like GPT for generative tasks and BERT for understanding context in Natural Language Processing ( NLP ).

Deep Learning 52

Deep Learning Computer Vision Neural Network Natural Language Processing 52

Heartbeat

FEBRUARY 8, 2024

Convolutional neural networks (CNNs) and recurrent neural networks (RNNs) are often employed to extract meaningful representations from images and text, respectively. Textual queries are transformed into embeddings using methods like word embeddings or recurrent neural networks.

Neural Network 52

Neural Network Deep Learning Convolutional Neural Networks Computer Vision 52

Topbots

OCTOBER 4, 2023

Here are a few examples across various domains: Natural Language Processing (NLP) : Predictive NLP models can categorize text into predefined classes (e.g., Image processing : Predictive image processing models, such as convolutional neural networks (CNNs), can classify images into predefined labels (e.g.,

Generative AI 118

Generative AI Natural Language Processing Machine Learning Convolutional Neural Networks 118