Unite.AI

SEPTEMBER 21, 2023

Organizations and practitioners build AI models that are specialized algorithms to perform real-world tasks such as image classification, object detection, and natural language processing. Some prominent AI techniques include neural networks, convolutional neural networks, transformers, and diffusion models.

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Convolutional Neural Networks Neural Network Artificial Intelligence Artificial Intelligence 286

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.

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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

MARCH 30, 2024

Vision Language Models (VLMs) emerge as a result of a unique integration of Computer Vision (CV) and Natural Language Processing (NLP). These innovations enable Mini-Gemini to process high-resolution images effectively and generate context-rich visual and textual content, setting it apart from existing models.

Artificial Intelligence 138

Artificial Intelligence Artificial Intelligence Convolutional Neural Networks Computer Vision 138

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. Observations indicate diminishing returns with increased model depth, mirroring challenges in deep convolutional neural networks for computer vision.

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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. Today, generative AI technology is taking neural network techniques one step further, allowing it to excel in various AI domains.

Machine Learning 203

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Unite.AI

AUGUST 2, 2023

Self Supervised Learning models build representations of the training data using human annotated labels, and it’s one of the major reasons behind the advancement of the NLP or Natural Language Processing , and the Computer Vision technology.

Computer Vision 246

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Marktechpost

JUNE 3, 2024

By the end, students will understand network construction, kernels, and expanding networks using transfer learning. Natural Language Processing This course covers natural language processing (NLP), which includes text manipulation, generation, and topic modeling.

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Convolutional Neural Networks Neural Network Deep Learning Computer Vision 122

Marktechpost

MAY 5, 2024

It covers topics like image processing, cluster analysis, gradient boosting, and popular libraries like scikit-learn, Spark, and Keras. and demonstrates their application in various real-world applications. The course also teaches how to implement these models using Python libraries like PyTorch.

Machine Learning 120

Machine Learning Python Neural Network Convolutional Neural Networks 120

Marktechpost

MAY 24, 2024

Transformers have greatly transformed natural language processing, delivering remarkable progress across various applications. Previous studies have explored methods like backpropagation and fine-tuning to understand sparsity in convolutional neural networks.

Convolutional Neural Networks 120

Convolutional Neural Networks Neural Network Natural Language Processing Machine Learning 120

Marktechpost

JULY 31, 2024

Learning TensorFlow enables you to create sophisticated neural networks for tasks like image recognition, natural language processing, and predictive analytics. Natural Language Processing in TensorFlow This course focuses on building natural language processing systems using TensorFlow.

Neural Network 108

Neural Network Auto-classification Natural Language Processing Deep Learning 108

Heartbeat

SEPTEMBER 20, 2023

Pixabay: by Activedia Image captioning combines natural language processing and computer vision to generate image textual descriptions automatically. These algorithms can learn and extract intricate features from input images by using convolutional layers.

Natural Language Processing 52

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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. These extend far beyond the traditional text-based processing of LLMs to include multimodal interactions.

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

Marktechpost

NOVEMBER 10, 2023

Traditionally, Convolutional Neural Networks (CNNs) have been the go-to models for processing image data, leveraging their ability to extract meaningful features and classify visual information.

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Convolutional Neural Networks Computer Vision Natural Language Processing Neural Network 109

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 158

Neural Network Convolutional Neural Networks BERT Deep Learning 158

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.

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Convolutional Neural Networks Neural Network BERT Machine Learning 130

Marktechpost

JANUARY 15, 2025

These limitations are particularly significant in fields like medical imaging, autonomous driving, and natural language processing, where understanding complex patterns is essential. Recurrent Neural Networks (RNNs): Well-suited for sequential data like time series and text, RNNs retain context through loops.

Deep Learning 87

Deep Learning Neural Network Convolutional Neural Networks Natural Language Processing 87

Marktechpost

JANUARY 20, 2025

The automation of radiology report generation has become one of the significant areas of focus in biomedical natural language processing. The traditional approach to the automation of radiology reporting is based on convolutional neural networks (CNNs) or visual transformers to extract features from images.

Automation 82

Automation Convolutional Neural Networks Natural Language Processing Neural Network 82

Unite.AI

MARCH 26, 2024

The development of Large Language Models (LLMs) built from decoder-only transformer models has played a crucial role in transforming the Natural Language Processing (NLP) domain, as well as advancing diverse deep learning applications including reinforcement learning , time-series analysis, image processing, and much more.

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Natural Language Processing Neural Network Convolutional Neural Networks Large Language Models 146

Unite.AI

OCTOBER 22, 2024

AI models like neural networks , used in applications like Natural Language Processing (NLP) and computer vision , are notorious for their high computational demands. Computer vision tasks rely heavily on matrix operations and have also used sub-quadratic techniques to streamline convolutional processes.

Neural Network 146

Neural Network Convolutional Neural Networks Deep Learning Natural Language Processing 146

Marktechpost

DECEMBER 10, 2024

1943: McCulloch and Pitts created a mathematical model for neural networks, marking the theoretical inception of ANNs. 1958: Frank Rosenblatt introduced the Perceptron , the first machine capable of learning, laying the groundwork for neural network applications. How Do Artificial Neural Networks Work?

Neural Network 109

Neural Network Convolutional Neural Networks Natural Language Processing Algorithm 109

Artificial Corner

JANUARY 23, 2025

Subscribe now #3 Natural Language Processing Course in Python This is a short yet useful 2-hour NLP course for anyone interested in the field of Natural Language Processing. NLP is a branch of artificial intelligence that allows machines to understand human language.

Data Science 95

Data Science Python Convolutional Neural Networks Natural Language Processing 95

Viso.ai

OCTOBER 9, 2024

With the rapid development of Convolutional Neural Networks (CNNs) , deep learning became the new method of choice for emotion analysis tasks. Generally, the classifiers used for AI emotion recognition are based on Support Vector Machines (SVM) or Convolutional Neural Networks (CNN).

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Convolutional Neural Networks Neural Network Computer Vision Emotion AI 98

IBM Journey to AI blog

SEPTEMBER 24, 2024

” When Guerena’s team first started working with smartphone images, they used convolutional neural networks (CNNs). ” Guerena’s team is now working on integrating speech-to-text and natural language processing alongside computer vision in the systems they’re building.

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Explainability Computer Vision Convolutional Neural Networks AI 184

Marktechpost

SEPTEMBER 17, 2024

One of the central challenges in this field is the extended time needed to train complex neural networks. In one experiment on a language task, the baseline Adam optimizer required 23,500 steps to reach the target perplexity, while NINO achieved the same performance in just 11,500 steps. reduction in training time.

Neural Network 113

Neural Network Machine Learning Convolutional Neural Networks Natural Language Processing 113

Marktechpost

JULY 16, 2023

This idea is based on “example packing,” a technique used in natural language processing to efficiently train models with inputs of varying lengths by combining several instances into a single sequence.

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Convolutional Neural Networks Neural Network Computer Vision Natural Language Processing 98

Towards AI

JANUARY 14, 2025

Transformers have revolutionized natural language processing (NLP), powering models like GPT and BERT. The goal was to see if I could accurately identify these digits using a Transformer-based approach, which feels quite different from the traditional Convolutional Neural Network (CNN) methods I was more familiar with.

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Convolutional Neural Networks Neural Network Computer Vision Natural Language Processing 116

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. A VQA system takes free-form, text-based questions about an input image and presents answers in a natural language format.

Natural Language Processing 40

Natural Language Processing Deep Learning NLP Convolutional Neural Networks 40

Mlearning.ai

APRIL 8, 2023

Charting the evolution of SOTA (State-of-the-art) techniques in NLP (Natural Language Processing) over the years, highlighting the key algorithms, influential figures, and groundbreaking papers that have shaped the field. Evolution of NLP Models To understand the full impact of the above evolutionary process.

NLP 98

NLP Neural Network Natural Language Processing Convolutional Neural Networks 98

ODSC - Open Data Science

APRIL 28, 2023

If a Natural Language Processing (NLP) system does not have that context, we’d expect it not to get the joke. We’ll be specifying a “textcat” component, the “model” that will process text into spaCy Doc objects. His major focus has been on Natural Language Processing (NLP) technology and applications.

Deep Learning 98

Deep Learning Convolutional Neural Networks NLP Neural Network 98

Marktechpost

JUNE 3, 2024

Numerous groundbreaking models—including ChatGPT, Bard, LLaMa, AlphaFold2, and Dall-E 2—have surfaced in different domains since the Transformer’s inception in Natural Language Processing (NLP).

Convolutional Neural Networks 102

Convolutional Neural Networks Neural Network Large Language Models Algorithm 102

NVIDIA

OCTOBER 18, 2023

Transforming the AI Landscape Generative AI dramatically improves ease of use by understanding human language prompts to make model changes. Those AI models are more flexible in detecting, segmenting, tracking, searching and even reprogramming — and help outperform traditional convolutional neural network -based models.

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Robotics Generative AI Convolutional Neural Networks Neural Network 126

Marktechpost

FEBRUARY 4, 2025

Graph Neural Networks (GNNs) have found applications in various domains, such as natural language processing, social network analysis, recommendation systems, etc. Due to its widespread usage, improving the defences of GNNs has emerged as a critical challenge.

Neural Network 73

Neural Network Convolutional Neural Networks Natural Language Processing Deep Learning 73

IBM Journey to AI blog

AUGUST 28, 2023

One of the best ways to take advantage of social media data is to implement text-mining programs that streamline the process. Machine learning algorithms like Naïve Bayes and support vector machines (SVM), and deep learning models like convolutional neural networks (CNN) are frequently used for text classification.

Data Mining 170

Data Mining Convolutional Neural Networks Categorization Machine Learning 170

Viso.ai

DECEMBER 10, 2023

Training of Neural Networks for Image Recognition The images from the created dataset are fed into a neural network algorithm. The training of an image recognition algorithm makes it possible for convolutional neural network image recognition to identify specific classes.

Convolutional Neural Networks 98

Convolutional Neural Networks Computer Vision Neural Network Deep Learning 98

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

Explosion

JUNE 12, 2023

After training on the annotation process, the annotation tool, and the supporting neuro-ontology, three raters annotated 15 clinical notes in three rounds. A machine annotator based on a convolutional neural network had a high level of agreement with the human annotators but one that was lower than human inter-rater agreement.

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Convolutional Neural Networks Neural Network Natural Language Processing Automation 52