Viso.ai

APRIL 26, 2024

Object detection systems typically use frameworks like Convolutional Neural Networks (CNNs) and Region-based CNNs (R-CNNs). Concept of Convolutional Neural Networks (CNN) However, in prompt object detection systems, users dynamically direct the model with many tasks it may not have encountered before.

Convolutional Neural Networks 52

Convolutional Neural Networks Computer Vision Neural Network Natural Language Processing 52

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).

AI Developer 52

AI Developer AI Development Computer Vision BERT 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. These deep learning image restoration models propose to use neural networks based on Transformers and Convolutional Neural Networks.

Computer Vision 130

Computer Vision Neural Network Convolutional Neural Networks Deep Learning 130

Viso.ai

FEBRUARY 25, 2023

Vision Transformer (ViT) have recently emerged as a competitive alternative to Convolutional Neural Networks (CNNs) that are currently state-of-the-art in different image recognition computer vision tasks. This article will cover the following topics: What is a Vision Transformer (ViT)?

Convolutional Neural Networks 52

Convolutional Neural Networks Computer Vision Neural Network Natural Language Processing 52

Viso.ai

DECEMBER 22, 2023

The Segment Anything Model (SAM), a recent innovation by Meta’s FAIR (Fundamental AI Research) lab, represents a pivotal shift in computer vision. SAM performs segmentation, a computer vision task , to meticulously dissect visual data into meaningful segments, enabling precise analysis and innovations across industries.

Convolutional Neural Networks 52

Convolutional Neural Networks Computer Vision Neural Network Auto-classification 52

Viso.ai

DECEMBER 27, 2023

Architecture and training process How CLIP resolves key challenges in computer vision Practical applications Challenges and limitations while implementing CLIP Future advancements How Does CLIP Work? It typically uses a convolutional neural network (CNN) architecture, like ResNet , for extracting image features.

Convolutional Neural Networks 52

Convolutional Neural Networks Computer Vision Neural Network NLP 52

Viso.ai

MARCH 26, 2024

This enhances the interpretability of AI systems for applications in computer vision and natural language processing (NLP). Viso Suite: The only truly end-to-end computer vision solution, Viso Suite eliminates the need for point solutions. Viso Suite is the end-to-End, No-Code Computer Vision Solution.

Deep Learning 52

Deep Learning Computer Vision Neural Network Natural Language Processing 52

Unite.AI

JUNE 21, 2024

The field of artificial intelligence (AI) has witnessed remarkable advancements in recent years, and at the heart of it lies the powerful combination of graphics processing units (GPUs) and parallel computing platform. This engine can then be used to perform efficient inference on the GPU, leveraging CUDA for accelerated computation.

Deep Learning 130

Deep Learning Neural Network Convolutional Neural Networks Large Language Models 130

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 162

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

Viso.ai

JULY 9, 2024

About us : Viso Suite is our end-to-end computer vision infrastructure for enterprises. The powerful solution enables teams to develop, deploy, manage, and secure computer vision applications in one place. To understand how transfer learning works, it is essential to understand the architecture of Deep Neural Networks.

Neural Network 52

Neural Network BERT Deep Learning Computer Vision 52

Heartbeat

JUNE 6, 2023

GCNs have been successfully applied to many domains, including computer vision and social network analysis. GCNs use a combination of graph-based representations and convolutional neural networks to analyze large amounts of textual data. References Paperwithcode | Graph Convolutional Network Kai, S.,

NLP 59

NLP Convolutional Neural Networks Neural Network Natural Language Processing 59

Pickl AI

NOVEMBER 13, 2024

Key Use Case : Layer Normalization can be used in RNNs or transformer-based architectures like GPT and BERT, where sequences are processed one sample at a time. Key Use Case : Use group Normalization in computer vision tasks with small mini-batches, such as object detection or medical imaging.

Deep Learning 52

Deep Learning Neural Network Convolutional Neural Networks 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.

BERT 83

BERT Natural Language Processing Large Language Models Neural Network 83

Viso.ai

DECEMBER 18, 2023

ONNX is an open standard for representing computer vision and machine learning models. ONNX (Open Neural Network Exchange) is an open-source format that facilitates interoperability between different deep learning algorithms for simple model sharing and deployment. Microsoft Cognitive Toolkit (CNTK). Apache MXNet.

Explainability 52

Explainability Neural Network Deep Learning Machine Learning 52

TensorFlow

MARCH 29, 2023

ZenDNN is purpose-built to help deep learning application and framework developers improve inference performance on AMD EPYC CPUs across an array of workloads, including computer vision, natural language processing, and recommender systems. TF-ZenDNN We have integrated ZenDNN into high-level AI frameworks for ease of use.

Neural Network 52

Neural Network Convolutional Neural Networks Natural Language Processing BERT 52

Pragnakalp

JANUARY 1, 2024

Compared with traditional recurrent neural networks (RNNs) and convolutional neural networks (CNNs), transformers differ in their ability to capture long-range dependencies and contextual information. They’re used to perform or improve AI and NLP business tasks, as well as streamline enterprise workflows.

Neural Network 52

Neural Network Natural Language Processing Convolutional Neural Networks NLP 52

DataRobot Blog

MARCH 9, 2022

These embeddings are sometimes trained jointly with the model, but usually additional accuracy can be attained by using pre-trained embeddings such as Word2Vec, GloVe, BERT, or FastText.

Machine Learning 52

Machine Learning Neural Network Convolutional Neural Networks Deep Learning 52

Heartbeat

FEBRUARY 8, 2024

Cross-modal retrieval is a branch of computer vision and natural language processing that links visual and verbal descriptions. Convolutional neural networks (CNNs) and recurrent neural networks (RNNs) are often employed to extract meaningful representations from images and text, respectively.

Neural Network 52

Neural Network Deep Learning Convolutional Neural Networks Computer Vision 52

Lexalytics

APRIL 5, 2021

While working as an RA in the computer vision group, I had the opportunity to sit in a robotic Humvee as it used Pomerleau’s code to drive around the University of Massachusetts’ stadium.) The CNN was a 6-layer neural net with 132 convolution kernels and (don’t laugh!) Hinton (again!)

Neural Network 52

Neural Network Convolutional Neural Networks Natural Language Processing BERT 52

Topbots

OCTOBER 4, 2023

Image processing : Predictive image processing models, such as convolutional neural networks (CNNs), can classify images into predefined labels (e.g., Masking in BERT architecture ( illustration by Misha Laskin ) Another common type of generative AI model are diffusion models for image and video generation and editing.

Generative AI 113

Generative AI Natural Language Processing Convolutional Neural Networks Machine Learning 113

Marktechpost

JUNE 27, 2023

Since this is a crucial component of any deep learning or convolutional neural network system. Gaussian Error Linear Unit Function Many of the most popular NLP models, including BERT, ROBERTa, and ALBERT, are compatible with the GELU activation function.

Neural Network 52

Neural Network Convolutional Neural Networks Deep Learning NLP 52

Sebastian Ruder

JANUARY 19, 2021

More recently, contrastive learning gained popularity in self-supervised representation learning in computer vision and speech ( van den Oord, 2018 ; Hénaff et al., A plethora of language-specific BERT models have been trained for languages beyond English such as AraBERT ( Antoun et al., 2020 ), RemBERT ( Chung et al.,

NLP 52

NLP ML Computer Vision Neural Network 52

ML Review

DECEMBER 16, 2019

NeurIPS’18 presented several papers with deep theoretical studies of building hyperbolic neural nets. Source: Chami et al Chami et al present Hyperbolic Graph Convolutional Neural Networks (HGCN) and Liu et al propose Hyperbolic Graph Neural Networks (HGNN). Top-5 accuracy grows from previous SOTA of 84.3%

Machine Learning 52

Machine Learning Neural Network Explainability Convolutional Neural Networks 52

ML Review

FEBRUARY 11, 2019

If you don’t know it already, NLP had a huge hype of transfer learning in this past 1 year, starting with ULMFit , ELMo , GLoMo , OpenAI transformer , BERT and recently Transformer-XL for further improving language modeling capabilities of the current state of the art.

Neural Network 52

Neural Network NLP Explainability Convolutional Neural Networks 52

Marktechpost

APRIL 20, 2024

Deep neural networks like convolutional neural networks (CNNs) have revolutionized various computer vision tasks, from image classification to object detection and segmentation. As models grew larger and more complex, their accuracy soared. Check out the Paper.

Machine Learning 101

Machine Learning Convolutional Neural Networks Neural Network Computer Vision 101

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. The impact is more for models using a convolutional neural network (CNN).

BERT 104

BERT NLP Computer Vision Neural Network 104

John Snow Labs

MAY 8, 2024

Nevertheless, the trajectory shifted remarkably with the introduction of advanced architectures like BERT (Bidirectional Encoder Representations from Transformers) and GPT (Generative Pre-trained Transformer), including subsequent versions such as OpenAI’s GPT-3.

Large Language Models 40

Large Language Models Automation NLP Machine Learning 40