Unite.AI

JANUARY 19, 2024

Traditionally, models for single-view object reconstruction built on convolutional neural networks have shown remarkable performance in reconstruction tasks. It combines knowledge of the structural arrangement of parts, low-level image cues, and high-level semantic information.

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Unite.AI

JULY 23, 2024

By leveraging advances in artificial intelligence (AI) and neuroscience, researchers are developing systems that can translate the complex signals produced by our brains into understandable information, such as text or images. Once the brain signals are collected, AI algorithms process the data to identify patterns.

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ODSC - Open Data Science

NOVEMBER 22, 2024

The PreMevE-MEO model utilizes a sophisticated machine learning algorithm that combines convolutional neural networks with transformers, enabling high-fidelity predictions based on decades of satellite observations. It also highlights the importance of long-term space observations in the age of AI. ”

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AI Weekly

NOVEMBER 23, 2023

forbes.com Applied use cases From Data To Diagnosis: A Deep Learning Approach To Glaucoma Detection When the algorithm is implemented in clinical practice, clinicians collect data such as optic disc photographs, visual fields, and intraocular pressure readings from patients and preprocess the data before applying the algorithm to diagnose glaucoma.

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Marktechpost

SEPTEMBER 1, 2023

Smartphones now hold immense amounts of sensitive information, making security a pressing concern. Incorporating machine learning and deep learning algorithms has shown promise in bolstering security. The study also noted the growing adoption of hybrid authentication systems, where algorithms like CNN were used for feature extraction.

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Pickl AI

AUGUST 3, 2023

Introduction to Deep Learning Algorithms: Deep learning algorithms are a subset of machine learning techniques that are designed to automatically learn and represent data in multiple layers of abstraction. How Deep Learning Algorithms Work? Backpropagation: Backpropagation is a crucial step in training deep learning algorithms.

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Viso.ai

DECEMBER 19, 2023

Today, the use of convolutional neural networks (CNN) is the state-of-the-art method for image classification. This means machine learning algorithms are used to analyze and cluster unlabeled datasets by discovering hidden patterns or data groups without the need for human intervention. How Does Image Classification Work?

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Unite.AI

NOVEMBER 6, 2023

Traditional machine learning is a broad term that covers a wide variety of algorithms primarily driven by statistics. The two main types of traditional ML algorithms are supervised and unsupervised. These algorithms are designed to develop models from structured datasets. Do We Still Need Traditional Machine Learning Algorithms?

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Marktechpost

NOVEMBER 20, 2024

Others have proposed a hybrid network intrusion detection system integrating convolutional neural networks (CNN), fuzzy C-means clustering, genetic algorithm, and a bagging classifier. This research demonstrates the powerful potential of deep learning in enhancing intrusion detection systems against DDoS attacks.

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Marktechpost

FEBRUARY 3, 2024

Traditional convolutional neural networks (CNNs) often struggle to capture global information from high-resolution 3D medical images. One proposed solution is the utilization of depth-wise convolution with larger kernel sizes to capture a wider range of features.

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Towards AI

JULY 9, 2024

Advances in artificial intelligence and machine learning have led to the development of increasingly complex object detection algorithms, which allow us to efficiently and precisely interpret large volumes of geographical data. This is somewhat of a popular algorithm for geospatial analysis. What is Object Detection?

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Unite.AI

MAY 31, 2023

A neural network (NN) is a machine learning algorithm that imitates the human brain's structure and operational capabilities to recognize patterns from training data. Since LLM neurons offer rich connections that can express more information, they are smaller in size compared to regular NNs.

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Unite.AI

OCTOBER 22, 2024

This term refers to how much time, memory, or processing power an algorithm requires as the size of the input grows. Initially, many AI algorithms operated within manageable complexity limits. They started looking for new algorithms, hardware solutions, and software optimizations to overcome the limitations of quadratic scaling.

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Marktechpost

JANUARY 14, 2024

CNN-based Blind Motion Deblurring CNN is extensively utilized in image processing to capture spatial information and local features. Deblurring algorithms based on convolutional neural networks (CNNs) have great efficiency and generalizability when trained with large-scale datasets.

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Viso.ai

DECEMBER 3, 2023

This article will provide an introduction to object detection and provide an overview of the state-of-the-art computer vision object detection algorithms. The goal of object detection is to develop computational models that provide the most fundamental information needed by computer vision applications : “ What objects are where ?”

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PyImageSearch

NOVEMBER 13, 2023

One of the most popular deep learning-based object detection algorithms is the family of R-CNN algorithms, originally introduced by Girshick et al. Since then, the R-CNN algorithm has gone through numerous iterations, improving the algorithm with each new publication and outperforming traditional object detection algorithms (e.g.,

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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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Unite.AI

MAY 24, 2024

Experimental results conducted to analyze the Recurrent Neural Network like mechanism of state space model conclude that the Mamba framework is suited for tasks with autoregressive or long-sequence characteristics, and is unnecessary for image classification tasks. million training images, and over 50,000 validation images.

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Marktechpost

APRIL 12, 2024

This article explores some of the most influential deep learning architectures: Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Generative Adversarial Networks (GANs), Transformers, and Encoder-Decoder architectures, highlighting their unique features, applications, and how they compare against each other.

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Unite.AI

AUGUST 1, 2023

OpenAI has been instrumental in developing revolutionary tools like the OpenAI Gym, designed for training reinforcement algorithms, and GPT-n models. Prompt 1 : “Tell me about Convolutional Neural Networks.” The spotlight is also on DALL-E, an AI model that crafts images from textual inputs.

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Viso.ai

OCTOBER 15, 2024

The video shows the output of a pose estimation application built using Viso Suite : [link] More and more computer vision and machine learning (ML) applications need 2D human pose estimation as information input. However, in recent years, human pose estimation accuracy achieved great breakthroughs with Convolutional Neural Networks (CNNs).

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Heartbeat

NOVEMBER 14, 2023

Integrating XGboost with Convolutional Neural Networks Photo by Alexander Grey on Unsplash XGBoost is a powerful library that performs gradient boosting. XGBoost is not limited to machine learning tasks, as its incredible power can be harnessed when harmonized with deep learning algorithms.

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Marktechpost

MAY 6, 2024

Developing new methods and algorithms allows for innovations that benefit various industries, including transportation, manufacturing, and healthcare. These tasks require models that can process visual information quickly and correctly to recognize, classify, and outline different objects.

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Marktechpost

JULY 18, 2023

First, let’s have a peek at the best object detection algorithms currently available. The HOG algorithm employs a gradient orientation process to pinpoint an image’s most crucial features. The simplicity of HOG-like characteristics makes the information they contain more readily digestible.

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Unite.AI

AUGUST 2, 2023

To tackle the issue of single modality, Meta AI released the data2vec, the first of a kind, self supervised high-performance algorithm to learn patterns information from three different modalities: image, text, and speech. Why Does the AI Industry Need the Data2Vec Algorithm?

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AWS Machine Learning Blog

DECEMBER 7, 2023

Amazon Forecast is a fully managed service that uses machine learning (ML) algorithms to deliver highly accurate time series forecasts. Calculating courier requirements The first step is to estimate hourly demand for each warehouse, as explained in the Algorithm selection section.

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IBM Journey to AI blog

MAY 6, 2024

Enter the age of data-driven protocol assessment: using benchmarking tools and predictive modeling to gauge protocol intricacies and forecast eligible patient numbers, which then inform protocol adjustments. These algorithms can help curate highly personalized advertisements and content tailored to the desired audience.

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Viso.ai

DECEMBER 10, 2023

An introduction The basic concepts and how it works Traditional and modern deep learning image recognition The best popular image recognition algorithms How to use Python for image recognition Examples and deep learning applications Popular image recognition software About: We provide the leading end-to-end computer vision platform Viso Suite.

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Viso.ai

OCTOBER 10, 2024

Satellite imagery has applications in the creation of maps, geographic information systems (GIS), land cover classification, navigation, agriculture, crisis management, and more. We will elaborate on computer vision techniques like Convolutional Neural Networks (CNNs).

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Viso.ai

FEBRUARY 11, 2024

The Need for Image Training Datasets To train the image classification algorithms we need image datasets. These datasets contain multiple images similar to those the algorithm will run in real life. The labels provide the Knowledge the algorithm can learn from. Algorithms that won the ImageNet challenge by year – source.

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Marktechpost

JUNE 12, 2024

This multi-omics data offers a wealth of information that can be utilized to gain profound insights into the mechanisms of health and disease. Techniques like deep convolutional neural networks excel in classifying and diagnosing digitized whole-slide images.

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Viso.ai

AUGUST 5, 2024

Faster R-CNN is a two-stage object detection algorithm. It uses a Region Proposal Network (RPN) and Convolutional Neural Networks (CNNs) to identify and locate objects in complex real-world images. Backbone Network The backbone network acts as the feature extractor for Faster R-CNN. Get a demo.

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Heartbeat

APRIL 4, 2023

Using Deep Learning Algorithms to Perform Image Style Transfer source: Chelsea Troy Artificial Intelligence (AI) has revolutionized the way computer vision and deep learning algorithms create stunning visuals. One of the fascinating applications of AI is Neural Style Transfer (NST).

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Mlearning.ai

MAY 10, 2023

In this article, I explore the technical reasons behind this challenge but also delves (briefly) into the way our brain process information about human characteristic to further understand the complexities of hand drawings and our perception. AI Algorithms and Hand Drawing What does it mean to “draw” for an AI ?

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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.

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Marktechpost

NOVEMBER 21, 2024

Throughout the manuscript, the researchers analyze AI’s impact on algorithmic development and provide forward-looking insights into potential future applications and developmental challenges.

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Pickl AI

NOVEMBER 20, 2024

Introduction Linear Algebra is a fundamental mathematical discipline that underpins many algorithms and techniques in Machine Learning. By understanding Linear Algebra operations, practitioners can better grasp how Machine Learning models work, optimize their performance, and implement various algorithms effectively.

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