Marktechpost

MARCH 26, 2024

The transformer architecture has improved natural language processing, with recent advancements achieved through scaling efforts from millions to billion-parameter models. However, larger models’ increased computational cost and memory footprint limit their practicality, benefiting only a few major corporations.

Convolutional Neural Networks 121

Convolutional Neural Networks Natural Language Processing Computer Vision Neural Network 121

Marktechpost

APRIL 12, 2024

Deep learning architectures have revolutionized the field of artificial intelligence, offering innovative solutions for complex problems across various domains, including computer vision, natural language processing, speech recognition, and generative models.

Deep Learning 137

Deep Learning Convolutional Neural Networks Neural Network Natural Language Processing 137

Marktechpost

NOVEMBER 10, 2023

In image recognition, researchers and developers constantly seek innovative approaches to enhance the accuracy and efficiency of computer vision systems. However, recent advancements have paved the way for exploring alternative architectures, prompting the integration of Transformer-based models into visual data analysis.

Convolutional Neural Networks 107

Convolutional Neural Networks Computer Vision Natural Language Processing Neural Network 107

Viso.ai

MARCH 7, 2023

These neural networks have made significant contributions to computer vision, natural language processing , and anomaly detection, among other fields. How autoencoders are used with real-world examples We will explore the different applications of autoencoders in computer vision.

Computer Vision 52

Computer Vision Neural Network Convolutional Neural Networks Machine Learning 52

AI Weekly

OCTOBER 26, 2023

cryptopolitan.com Applied use cases Alluxio rolls out new filesystem built for deep learning Alluxio Enterprise AI is aimed at data-intensive deep learning applications such as generative AI, computer vision, natural language processing, large language models and high-performance data analytics.

Neural Network 197

Neural Network Convolutional Neural Networks Robotics Deep Learning 197

Chatbots Life

APRIL 16, 2024

This is useful in natural language processing tasks. By applying generative models in these areas, researchers and practitioners can unlock new possibilities in various domains, including computer vision, natural language processing, and data analysis.

Neural Network 59

Neural Network Convolutional Neural Networks Natural Language Processing Machine Learning 59

Unite.AI

NOVEMBER 6, 2023

Advances in neural network techniques have formed the basis for transitioning from machine learning to deep learning. 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.

Machine Learning 204

Machine Learning Neural Network Deep Learning Convolutional Neural Networks 204

Unite.AI

APRIL 26, 2024

The advancements in large language models have significantly accelerated the development of natural language processing , or NLP. These extend far beyond the traditional text-based processing of LLMs to include multimodal interactions.

Large Language Models 162

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

Viso.ai

MARCH 20, 2024

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. Viso Suite is the only end-to-end computer vision platform What are Foundation Models?

AI Developer 52

AI Developer AI Development Computer Vision BERT 52

Viso.ai

JANUARY 2, 2024

In the following, we will explore Convolutional Neural Networks (CNNs), a key element in computer vision and image processing. Whether you’re a beginner or an experienced practitioner, this guide will provide insights into the mechanics of artificial neural networks and their applications.

Convolutional Neural Networks 52

Convolutional Neural Networks Neural Network Computer Vision Data Scarcity 52

Unite.AI

JULY 31, 2023

Self-supervised learning has already shown its results in Natural Language Processing as it has allowed developers to train large models that can work with an enormous amount of data, and has led to several breakthroughs in fields of natural language inference, machine translation, and question answering.

Computer Vision 246

Computer Vision Metadata Natural Language Processing ML 246

Heartbeat

MARCH 8, 2023

Happy Reading, Emilie, Abby & the Heartbeat Team Natural Language Processing With SpaCy (A Python Library) — by Khushboo Kumari This post goes over how the most cutting-edge NLP software, SpaCy , operates. Convolutional, pooling, and fully linked layers are some of the layers that make up a CNN.

Convolutional Neural Networks 40

Convolutional Neural Networks Neural Network Natural Language Processing Deep Learning 40

How to Learn Machine Learning

MAY 14, 2023

When selecting projects, consider tackling problems in different domains, such as natural language processing, computer vision, or recommendation systems. Some popular areas of specialization include natural language processing, computer vision, and reinforcement learning.

Machine Learning 52

Machine Learning Natural Language Processing Convolutional Neural Networks Deep Learning 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

Mlearning.ai

JULY 11, 2023

You can use libraries like TensorFlow or PyTorch to practice building simple neural networks. Focus on convolutional neural networks (CNNs) for working with images, recurrent neural networks (RNNs) for analyzing sequential data, and generative adversarial networks (GANs) for creating realistic content.

Deep Learning 40

Deep Learning Neural Network Convolutional Neural Networks Computer Vision 40

Pickl AI

AUGUST 3, 2023

These algorithms have shown remarkable success in solving a wide range of complex tasks, such as image recognition, natural language processing, speech recognition, and more. At the core of Deep Learning is the artificial neural network (ANN), which is inspired by the structure and function of the human brain.

Deep Learning 52

Deep Learning Machine Learning Algorithm Neural Network 52

Viso.ai

DECEMBER 27, 2023

This article will provide insights into how CLIP bridges the gap between natural language and image processing. 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?

Convolutional Neural Networks 52

Convolutional Neural Networks Computer Vision Neural Network NLP 52

Viso.ai

FEBRUARY 24, 2023

In this article, we’ll dive into the techniques, latest research, and advantages of self-supervised learning, and explore how it is being used in computer vision. Challenges and advantages of self supervised learning The learning process and popular methods Recent research and applications of self supervised learning.

Computer Vision 52

Computer Vision Neural Network Natural Language Processing Machine Learning 52

Heartbeat

JULY 6, 2023

The process of locating and identifying objects of interest in an image or video is part of the object detection method, a popular computer vision technique. Object detection is typically achieved through the use of deep learning models, particularly Convolutional Neural Networks (CNNs).

Computer Vision 59

Computer Vision Convolutional Neural Networks Deep Learning Neural Network 59

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.

NLP 59

NLP Convolutional Neural Networks Neural Network Natural Language Processing 59

Heartbeat

NOVEMBER 21, 2023

Computer vision, the field dedicated to enabling machines to perceive and understand visual data, has witnessed a monumental shift in recent years with the advent of deep learning. Photo by charlesdeluvio on Unsplash Welcome to a journey through the advancements and applications of deep learning in computer vision.

Computer Vision 52

Computer Vision Deep Learning NLP Neural Network 52

Mlearning.ai

MARCH 6, 2023

Some of the most common types include: Deep Convolutional GANs (DCGANs): These GANs use convolutional neural networks (CNNs) for image generation. Structure of GAN GANs have many applications in computer vision, natural language processing, and audio processing.

Neural Network 52

Neural Network Convolutional Neural Networks Machine Learning Natural Language Processing 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., Here are a few examples across various domains: Natural Language Processing (NLP) : Predictive NLP models can categorize text into predefined classes (e.g.,

Generative AI 118

Generative AI Natural Language Processing Machine Learning Convolutional Neural Networks 118

Viso.ai

MARCH 26, 2024

Arguably, one of the most pivotal breakthroughs is the application of Convolutional Neural Networks (CNNs) to financial processes. This drastically enhanced the capabilities of computer vision systems to recognize patterns far beyond the capability of humans. 2: Automated Document Analysis and Processing No.3:

Computer Vision 52

Computer Vision Neural Network Machine Learning Convolutional Neural Networks 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.

Neural Network 52

Neural Network Convolutional Neural Networks Natural Language Processing Computer Vision 52

Pickl AI

AUGUST 1, 2023

As technology continues to impact how machines operate, Machine Learning has emerged as a powerful tool enabling computers to learn and improve from experience without explicit programming. Deep Learning Models: Deep learning models are a subset of neural networks with multiple layers (deep architectures).

Machine Learning 52

Machine Learning Neural Network Algorithm Convolutional Neural Networks 52

Viso.ai

DECEMBER 10, 2023

This article will cover image recognition, an application of Artificial Intelligence (AI), and computer vision. Image recognition with deep learning is a key application of AI vision and is used to power a wide range of real-world use cases today. Get a personalized demo. link] What is Image Recognition?

Convolutional Neural Networks 98

Convolutional Neural Networks Computer Vision Neural Network Deep Learning 98

John Snow Labs

MAY 23, 2023

Visual question answering (VQA), an area that intersects the fields of Deep Learning, Natural Language Processing (NLP) and Computer Vision (CV) is garnering a lot of interest in research circles. This is why VQA problems are widely accepted as “AI-complete” or “AI-hard”.

Natural Language Processing 40

Natural Language Processing Deep Learning NLP Convolutional Neural Networks 40

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?

Neural Network 117

Neural Network Convolutional Neural Networks BERT Computer Vision 117

Pickl AI

SEPTEMBER 18, 2023

These AI systems can generate new data or content rather than simply analyzing or processing existing data. Natural language processing, computer vision, music composition, art generation, and other applications frequently employ generative AI models.

Generative AI 52

Generative AI Neural Network Artificial Intelligence Artificial Intelligence 52

Viso.ai

APRIL 26, 2024

Thus, these systems are grounded in traditional object detection and natural language processing frameworks. Object detection systems typically use frameworks like Convolutional Neural Networks (CNNs) and Region-based CNNs (R-CNNs). TensorFlow, for its ease of deployment and development.

Convolutional Neural Networks 52

Convolutional Neural Networks Computer Vision Neural Network Natural Language Processing 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. Vaswani et al. Learn more by booking a demo.

Deep Learning 52

Deep Learning Computer Vision Neural Network Natural Language Processing 52

Viso.ai

DECEMBER 1, 2023

Value of AI models for businesses The most popular AI models AI models in computer vision applications – Viso Suite About us: We provide the platform Viso Suite to collect data and train, deploy, and scale AI models on powerful infrastructure. In computer vision, this process is called image annotation.

AI Modeling 98

AI Modeling Neural Network Computer Vision Deep Learning 98

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. Transformer models have become the de-facto status quo in Natural Language Processing (NLP).

Convolutional Neural Networks 52

Convolutional Neural Networks Computer Vision Neural Network Natural Language Processing 52

Unite.AI

AUGUST 2, 2023

To overcome the challenge presented by single modality models & algorithms, Meta AI released the data2vec, an algorithm that uses the same learning methodology for either computer vision , NLP or speech. For example, there are vocabulary of speech units in speech processing that can define a self-supervised learning task in NLP.

Computer Vision 246

Computer Vision Natural Language Processing Algorithm Convolutional Neural Networks 246

Viso.ai

MAY 6, 2024

By eliminating the need to purchase and manage point solutions, Viso Suite presents teams with a truly end-to-end computer vision infrastructure. Automatic annotation using AI and computer vision technologies is more efficient but requires computational resources, training datasets, and initial supervision.

Computer Vision 52

Computer Vision Deep Learning Neural Network Convolutional Neural Networks 52

The MLOps Blog

FEBRUARY 22, 2024

For example, convolutional neural networks achieve significantly better accuracy in continual learning when they use batch normalization and skip connections. It is designed for PyTorch and can be used in various domains like Computer Vision and Natural Language Processing.

Continuous Learning 59

Continuous Learning Machine Learning ML Neural Network 59