Unite.AI

JANUARY 19, 2024

Traditionally, models for single-view object reconstruction built on convolutional neural networks have shown remarkable performance in reconstruction tasks. More recent depth estimation frameworks deploy convolutional neural network structures to extract depth in a monocular image.

Convolutional Neural Networks 130

Convolutional Neural Networks Neural Network Explainability Categorization 130

Viso.ai

DECEMBER 19, 2023

Today, the use of convolutional neural networks (CNN) is the state-of-the-art method for image classification. With the Internet of Things (IoT) and Artificial Intelligence (AI) becoming ubiquitous technologies, we now have huge volumes of data being generated. How Does Image Classification Work?

Convolutional Neural Networks 98

Convolutional Neural Networks Neural Network Computer Vision Deep Learning 98

Unite.AI

SEPTEMBER 13, 2023

Industry Anomaly Detection and Large Vision Language Models Existing IAD frameworks can be categorized into two categories. The prompt learner consists of learnable base prompt embeddings, and a convolutional neural network. Reconstruction-based IAD. Feature Embedding-based IAD.

Convolutional Neural Networks 288

Convolutional Neural Networks LLM Neural Network Large Language Models 288

Marktechpost

NOVEMBER 16, 2023

Utilizing a two-stage convolutional neural network, the model classifies macula-centered 3D volumes from Topcon OCT images into Normal, early/intermediate AMD (iAMD), atrophic (GA), and neovascular (nAMD) stages. The study emphasizes the significance of accurate AMD staging for timely treatment initiation.

Deep Learning 109

Deep Learning Convolutional Neural Networks Neural Network Categorization 109

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

IBM Journey to AI blog

AUGUST 28, 2023

Text mining —also called text data mining—is an advanced discipline within data science that uses natural language processing (NLP) , artificial intelligence (AI) and machine learning models, and data mining techniques to derive pertinent qualitative information from unstructured text data. What is text mining?

Data Mining 170

Data Mining Convolutional Neural Networks Categorization Machine Learning 170

PyImageSearch

NOVEMBER 13, 2023

You’ll typically find IoU and mAP used to evaluate the performance of HOG + Linear SVM detectors ( Dalal and Triggs, 2005 ), Convolutional Neural Network methods, such as Faster R-CNN ( Girshick et al., Today, we would typically swap in a deeper, more accurate base network, such as ResNet ( He et al., 2015 ; He et al.,

Convolutional Neural Networks 119

Convolutional Neural Networks Neural Network Computer Vision Deep Learning 119

Marktechpost

SEPTEMBER 12, 2023

Fine-grained image categorization delves into distinguishing closely related subclasses within a broader category. Modern algorithms for fine-grained image classification frequently rely on convolutional neural networks (CNN) and vision transformers (ViT) as their structural basis.

Convolutional Neural Networks 109

Convolutional Neural Networks Categorization Neural Network Deep Learning 109

Marktechpost

JANUARY 14, 2024

The researchers present a categorization system that uses backbone networks to organize these methods. Most picture deblurring methods use paired images to train their neural networks. It employs neural networks to understand intricate feature mapping interactions to improve picture restoration quality.

Deep Learning 113

Deep Learning Convolutional Neural Networks Neural Network Computer Vision 113

Marktechpost

DECEMBER 24, 2023

These methods address the challenges of traditional approaches, offering more automated, accurate, and robust solutions for identifying and categorizing plant leaf diseases. As the demand for sustainable agriculture grows, machine learning emerges as a vital force, reshaping the future of food security and cultivation.

Machine Learning 119

Machine Learning Convolutional Neural Networks Neural Network Deep Learning 119

Viso.ai

DECEMBER 3, 2023

Object detection is a key field in artificial intelligence, allowing computer systems to “see” their environments by detecting objects in visual images or videos. Various two-stage detectors include region convolutional neural network (RCNN), with evolutions Faster R-CNN or Mask R-CNN.

Convolutional Neural Networks 98

Convolutional Neural Networks Computer Vision Neural Network Deep Learning 98

Marktechpost

MAY 31, 2024

These signals are essential in categorizing sleep stages and identifying sleep disorders. SleepFM employs three 1D convolutional neural networks (CNNs) to generate embeddings from each modality (BAS, ECG, and respiratory signals).

Convolutional Neural Networks 107

Convolutional Neural Networks Neural Network Automation Categorization 107

Marktechpost

NOVEMBER 27, 2024

Machine Learning, a subset of Artificial Intelligence, has emerged as a transformative force, empowering machines to learn from data and make intelligent decisions without explicit programming. Classification: Categorizing data into discrete classes (e.g., Document categorization. housing prices, stock prices).

Machine Learning 113

Machine Learning Algorithm Neural Network Convolutional Neural Networks 113

Becoming Human

MARCH 16, 2023

This post includes the fundamentals of graphs, combining graphs and deep learning, and an overview of Graph Neural Networks and their applications. Through the next series of this post here , I will try to make an implementation of Graph Convolutional Neural Network. So, let’s get started! What are Graphs?

Neural Network 87

Neural Network Auto-classification Convolutional Neural Networks Auto-complete 87

ODSC - Open Data Science

APRIL 28, 2023

This diagram I think gives you a good overview: spaCy 101: Everything you need to know Above you can see that text is processed by a “Language” object, which has a number of components such as part-of-speech tagging, vector representations, and models for categorization. These can be customized and trained. We’ll be mainly using the “.cats”

Deep Learning 98

Deep Learning Convolutional Neural Networks NLP Neural Network 98

Marktechpost

OCTOBER 4, 2024

Evaluated Models Ready Tensor’s benchmarking study categorized the 25 evaluated models into three main types: Machine Learning (ML) models, Neural Network models, and a special category called the Distance Profile model. Prominent models include Long-Short-Term Memory (LSTM) and Convolutional Neural Networks (CNN).

Neural Network 104

Neural Network Machine Learning Convolutional Neural Networks Categorization 104

Viso.ai

OCTOBER 7, 2024

Artificial Intelligence in Waste Management The field of Artificial Intelligence (AI) and machine learning has evolved significantly in recent years. Object Detection : Computer vision algorithms, such as convolutional neural networks (CNNs), analyze the images to identify and classify waste types (i.e.,

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Convolutional Neural Networks Computer Vision Neural Network Robotics 52

Viso.ai

DECEMBER 1, 2024

The field of computer vision is a sector of Artificial Intelligence (AI) that uses Machine Learning and Deep Learning to enable computers to see , perform AI pattern recognition , and analyze objects in photos and videos like people do. There exist multiple datasets for parking lot detection, such as PKLot and CNRPark-EXT.

Computer Vision 98

Computer Vision Convolutional Neural Networks Deep Learning Neural Network 98

Pickl AI

JULY 28, 2023

Machine Learning algorithms can extract pertinent information from photos and generate precise predictions about the content or objects present using methods like Convolutional Neural Networks (CNNs). Training the Model: The labeled dataset is used to train a Machine Learning model, typically a deep neural network like a CNN.

Machine Learning 52

Machine Learning Convolutional Neural Networks Neural Network Deep Learning 52

Topbots

OCTOBER 4, 2023

Basic Definitions Generative AI and predictive AI are two powerful types of artificial intelligence with a wide range of applications in business and beyond. Here are a few examples across various domains: Natural Language Processing (NLP) : Predictive NLP models can categorize text into predefined classes (e.g.,

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Generative AI Natural Language Processing Machine Learning Convolutional Neural Networks 119

Viso.ai

FEBRUARY 11, 2024

It is a technique used in computer vision to identify and categorize the main content (objects) in a photo or video. 2012 – A deep convolutional neural net called AlexNet achieves a 16% error rate. This marks the start of an industry-wide artificial intelligence boom. parameters and achieved 84.5%

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Convolutional Neural Networks Neural Network Computer Vision Deep Learning 52

Heartbeat

MARCH 8, 2023

In this article, I will go through some basic concepts of creating a neural network using TensorFlow and then explore how we might improve upon our model’s architecture using Keras Tuner. Deep Reinforcement Learning (DRL) and Generative Adversarial Networks (GANs) are two promising deep learning trends.

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Convolutional Neural Networks Neural Network Natural Language Processing Deep Learning 40

Heartbeat

JANUARY 12, 2023

The same is true for machines running AI hardware and software in the deep learning field of artificial intelligence (AI). Convolutional neural networks ( CNNs ) are a subtype of artificial neural networks that have been popular in several applications linked to computer vision and are attracting interest in other domains.

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Deep Learning Convolutional Neural Networks Neural Network Machine Learning 52

Viso.ai

DECEMBER 6, 2024

Autonomous Driving applying Semantic Segmentation in autonomous vehicles Semantic segmentation is now more accurate and efficient thanks to deep learning techniques that utilize neural network models. Autonomous vehicles will become significantly more intelligent as artificial intelligence (AI) advances.

Computer Vision 52

Computer Vision Convolutional Neural Networks Neural Network Deep Learning 52

PyImageSearch

FEBRUARY 6, 2023

The Adam optimizer is used with the initial learning rate specified in the config file, and the loss function used is sparse categorical cross-entropy. format(initial_accuracy)) # train the image classification network print("[INFO] training network.") format(initial_loss)) print("initial accuracy: {: 2f}".format(initial_accuracy))

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Auto-classification Computer Vision Neural Network Deep Learning 140

Viso.ai

DECEMBER 6, 2023

Computer vision (CV) is a rapidly evolving area in artificial intelligence (AI), allowing machines to process complex real-world visual data in different domains like healthcare, transportation, agriculture, and manufacturing. Based on the presence of a tiger, the entire image is categorized as such. Get a demo here.

Computer Vision 52

Computer Vision Convolutional Neural Networks Neural Network Categorization 52

Viso.ai

JANUARY 4, 2024

Together, these techniques contribute to the advancement of artificial intelligence. Unlike simple segmentation that might just separate foreground from background, semantic segmentation categorizes all pixels in an image into predefined categories.

Computer Vision 52

Computer Vision Convolutional Neural Networks Deep Learning Neural Network 52

Snorkel AI

MARCH 1, 2023

Foundation Models (FMs), such as GPT-3 and Stable Diffusion, mark the beginning of a new era in machine learning and artificial intelligence. Dog and Boy In January 2023, Netflix announced the release of an animated short film called “Dog and Boy” that used artificial intelligence to assist with the creation of background images.

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BERT Natural Language Processing Large Language Models NLP 83

Viso.ai

FEBRUARY 19, 2024

The evolution of computer vision technology has paved the way for innovative artificial intelligence (AI) solutions in the legal industry. CV algorithms can accurately categorize documents by analyzing document characteristics including structures, layout, and formatting.

Computer Vision 52

Computer Vision Deep Learning Convolutional Neural Networks Neural Network 52

Becoming Human

MAY 15, 2023

Machine Learning Machine learning is a type of artificial intelligence that allows software applications to learn from the data and become more accurate over time. These libraries provide pre-built functionality to train, test and deploy deep neural networks.

Data Science 75

Data Science Neural Network Machine Learning Python 75

Mlearning.ai

JUNE 13, 2023

Traditional object identification approaches involved segmenting an image into many areas and categorizing and refining each region independently. It improves both the accuracy and efficiency of object detection by introducing a region proposal network (RPN) and sharing convolutional features.

Computer Vision 52

Computer Vision Convolutional Neural Networks Categorization Algorithm 52

Viso.ai

MARCH 26, 2024

Attention mechanisms allow artificial intelligence (AI) models to dynamically focus on individual elements within visual data. Attention Mechanisms in Deep Learning Attention mechanisms are helping reimagine both convolutional neural networks ( CNNs ) and sequence models.

Deep Learning 52

Deep Learning Computer Vision Neural Network Natural Language Processing 52

PyImageSearch

JULY 24, 2023

In short, Keras Core is the first effort to end the 100-year war of Deep Learning Frameworks and unite forces for a better and “open” artificial intelligence (AI) future. Then, it creates three blocks of layers, each consisting of two convolutional layers followed by batch normalization and dropout. What Is Keras Core?

Deep Learning 96

Deep Learning Computer Vision Machine Learning Convolutional Neural Networks 96

Viso.ai

JANUARY 16, 2024

This prevents you from applying artificial intelligence (AI) in several real-world industrial use cases, such as healthcare, retail, and manufacturing, where data is scarce. The AI community categorizes N-shot approaches into few, one, and zero-shot learning. The embedding functions can be convolutional neural networks (CNNs).

Neural Network 52

Neural Network Computer Vision Convolutional Neural Networks Algorithm 52

Viso.ai

JULY 9, 2024

In today’s digital world, Artificial Intelligence (AI) and Machine learning (ML) models are used everywhere, from face detection in electronic devices to real-time language translation. VGG16 has a CNN ( Convolutional Neural Network ) based architecture that has 16 layers.

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Neural Network BERT Deep Learning Computer Vision 52

PyImageSearch

SEPTEMBER 16, 2024

Types of Anomaly Detection Problems Anomaly detection problems can be broadly categorized into three main types: point anomalies contextual anomalies collective anomalies Each type ( Figure 4 ) has distinct characteristics and applications, making it essential to understand their differences and how they can be effectively identified.

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Computer Vision Algorithm Machine Learning Deep Learning 56

Viso.ai

JUNE 24, 2024

Artificial Intelligence (AI) for the blind and visually impaired, specifically using Computer Vision (CV), has the potential to improve the lives of visually impaired individuals significantly. We can categorize the types of AI for the blind and their functions. Every day we do tasks that we take for granted.

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