Marktechpost

SEPTEMBER 10, 2023

Graph-based ML models also lose important details about where the things are placed when molecules stick to each other. Also, don’t forget to join our 30k+ ML SubReddit , 40k+ Facebook Community, Discord Channel , and Email Newsletter , where we share the latest AI research news, cool AI projects, and more.

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Mlearning.ai

MAY 30, 2023

Recurrent Neural Networks (RNNs) have become a potent tool for analysing sequential data in the large subject of artificial intelligence and machine learning. As we know that Convolutional Neural Network (CNN) is used for structured arrays of data such as image data. RNN is used for sequential data.

Neural Network 52

Neural Network Convolutional Neural Networks Natural Language Processing Data Analysis 52

Unite.AI

NOVEMBER 6, 2023

With these advancements, it’s natural to wonder: Are we approaching the end of traditional machine learning (ML)? The two main types of traditional ML algorithms are supervised and unsupervised. Data Preprocessing and Feature Engineering: Traditional ML requires extensive preprocessing to transform datasets as per model requirements.

Machine Learning 204

Machine Learning Neural Network Deep Learning Convolutional Neural Networks 204

Mlearning.ai

MAY 21, 2023

CNNs with random weights are very useful even without training! Continue reading on MLearning.ai »

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Convolutional Neural Networks Neural Network ML Data Science 52

Mlearning.ai

FEBRUARY 14, 2023

Image classification is a field of study within the broader area of computer vision and deep learning, with the goal of automating the… Continue reading on MLearning.ai »

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Convolutional Neural Networks Neural Network Computer Vision Deep Learning 52

AI News

JULY 27, 2023

Gcore trained a Convolutional Neural Network (CNN) – a model designed for image analysis – using the CIFAR-10 dataset containing 60,000 labelled images, on these devices. The results were striking, with IPUs and GPUs significantly outperforming CPUs in training speed.

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Towards AI

MAY 9, 2024

I tried to make that part easier with this GitHub Repository I created for those without any background in the field: a complete guide to start and improve in ML and AI. 7 Best Machine Learning Workflow and Pipeline Orchestration Tools by Eryk Lewinson This article briefly describes ML workflows and pipelines. Meme of the week!

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Marktechpost

MAY 5, 2024

In recent years, the demand for AI and Machine Learning has surged, making ML expertise increasingly vital for job seekers. Additionally, Python has emerged as the primary language for various ML tasks. Students learn to implement and analyze models like linear models, kernel machines, neural networks, and graphical models.

Machine Learning 118

Machine Learning Python Neural Network Convolutional Neural Networks 118

Mlearning.ai

JULY 17, 2023

How taking inspiration from the brain can help us create Neural Networks. One of the most promising avenues for this kind of research is to learn from evolution and biological systems to improve the design of AI Models (after all Neural Networks were based on our brains).

Neural Network 52

Neural Network Convolutional Neural Networks Machine Learning Data Scientist 52

Marktechpost

SEPTEMBER 4, 2023

Recent advancements in deep neural networks have enabled new approaches to address anatomical segmentation. For instance, state-of-the-art performance in the anatomical segmentation of biomedical images has been attained by deep convolutional neural networks (CNNs). Check out the Paper and Github.

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Marktechpost

APRIL 27, 2024

Historically, recurrent neural networks (RNNs) and convolutional neural networks (CNNs) have been employed to manage these predictions. While RNNs are adept at processing data sequentially, they often fall short in speed and struggle with long-term dependencies. Also, don’t forget to follow us on Twitter.

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Convolutional Neural Networks Neural Network ML Artificial Intelligence 131

Marktechpost

APRIL 11, 2024

The proposed approach involves utilizing neural networks to capitalize on the structured relationship between band dispersion and strain. Don’t Forget to join our 40k+ ML SubReddit The post This AI Paper from MIT Offers a Guide for Fine-Tuning Specific Material Properties Using Machine Learning appeared first on MarkTechPost.

Machine Learning 108

Machine Learning Convolutional Neural Networks Neural Network AI 108

Viso.ai

DECEMBER 19, 2023

Today, the use of convolutional neural networks (CNN) is the state-of-the-art method for image classification. Therefore, there is a big emerging trend called Edge AI that aims to move machine learning (ML) tasks from the cloud to the edge. Concept of a neural network with the input values (green) and weights (blue).

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Marktechpost

APRIL 8, 2024

Amidst this pursuit, the emergence of State-Space Models (SSMs) marks a significant stride toward efficacious audio processing, marrying the prowess of neural networks with the finesse required for discerning individual voices from a composite auditory tapestry. Join our Telegram Channel , Discord Channel , and LinkedIn Gr oup.

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Convolutional Neural Networks Neural Network AI AI 131

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. One robust use case for XGBoost is integrating it with neural networks to perform a given task. It was envisioned by Thongsuwan et al.,

Deep Learning 52

Deep Learning Convolutional Neural Networks Neural Network Machine Learning 52

Unite.AI

SEPTEMBER 21, 2023

Some prominent AI techniques include neural networks, convolutional neural networks, transformers, and diffusion models. AI and machine learning (ML) algorithms are capable of the following: Analyzing transaction patterns to detect fraudulent activities made by bots. What is Blockchain?

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Marktechpost

AUGUST 25, 2023

A team of researchers from Apple and the University of California, Santa Barbara created a direct inference of scene-level 3D geometry using deep neural networks, which didn’t involve the traditional method of test-time optimization. Check out the Paper and Github link.

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Convolutional Neural Networks Neural Network AI Researcher AI Research 98

Marktechpost

MAY 6, 2024

Existing research includes convolutional neural networks (CNNs) and transformer-based object detection and segmentation architectures. This need for speed and accuracy pushes researchers to explore new techniques that can provide reliable results in dynamic environments. Also, don’t forget to follow us on Twitter.

Convolutional Neural Networks 119

Convolutional Neural Networks Neural Network Computer Vision Robotics 119

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. Check out the Paper and Github.

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Convolutional Neural Networks Computer Vision Neural Network Artificial Intelligence 144

Marktechpost

MARCH 30, 2024

The methodology behind Mini-Gemini involves a dual-encoder system that includes a convolutional neural network for refined image processing, enhancing visual tokens without increasing their number. It utilizes patch info mining for detailed visual cue extraction. Also, don’t forget to follow us on Twitter.

Artificial Intelligence 138

Artificial Intelligence Artificial Intelligence Convolutional Neural Networks Computer Vision 138

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 successfully developed an automated deep learning-based AMD detection and staging system using OCT scans.

Deep Learning 108

Deep Learning Convolutional Neural Networks Neural Network Categorization 108

AI Weekly

MARCH 21, 2024

nature.com The lucent yet opaque challenge of regulating AI in radiology Here we discuss the current and future regulatory landscapes of AI/ML in radiology, and we highlight pressing challenges that are critical for regulatory bodies to traverse.

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Marktechpost

JULY 14, 2023

This image representation comes under a broad category of Computer Vision and Convolutional Neural Networks. Research scientists find it very similar to Convolutional Neural Networks. But formatting an image through text is challenging as there was a severe loss and less accuracy.

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Convolutional Neural Networks AI Researcher AI Research Neural Network 97

Marktechpost

JUNE 15, 2023

Convolutional neural networks can use Images with translational symmetry, and permutation symmetry in graphs can be used by graph neural networks. Theoretical research and practical methods for constructing general group equivariant neural networks have seen a recent uptick in interest.

Convolutional Neural Networks 98

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Marktechpost

NOVEMBER 21, 2023

Their pioneering breakthrough, named Senseiver, showcases a neural network that achieves a remarkable feat: representing extensive data with minimal computational resources. The team developed a neural network that allows them to represent a large system in a very compact way. Check out the Paper and Reference Article.

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Convolutional Neural Networks Neural Network Artificial Intelligence Artificial Intelligence 103

Heartbeat

DECEMBER 20, 2023

This blog aims to equip you with a thorough understanding of these powerful neural network architectures. In a typical neural network, you flatten your input one vector, take those input values in at once, multiply them by the weights in the first layer, add the bias, and pass the result into a neuron.

Convolutional Neural Networks 52

Convolutional Neural Networks Neural Network Deep Learning Computer Vision 52

Marktechpost

MARCH 19, 2024

Traditional methods like 3D convolutional neural networks (CNNs) and video transformers have made significant strides but often struggle to effectively address both local redundancy and global dependencies. Also, don’t forget to follow us on Twitter. Join our Telegram Channel , Discord Channel , and LinkedIn Gr oup.

Convolutional Neural Networks 126

Convolutional Neural Networks AI Modeling Neural Network Large Language Models 126

Marktechpost

MARCH 22, 2024

Developed by a research team from Northeastern University, it introduces a unique approach that combines global-to-local processing and lightweight Convolutional Neural Networks (CNNs) for feature extraction at various spatial resolutions. Also, don’t forget to follow us on Twitter.

Computer Vision 132

Computer Vision Convolutional Neural Networks Neural Network Deep Learning 132

Mlearning.ai

JANUARY 28, 2023

From Sale Marketing Business 7 Powerful Python ML For Data Science And Machine Learning need to be use. This post will outline seven powerful python ml libraries that can help you in data science and different python ml environment. A python ml library is a collection of functions and data that can use to solve problems.

Data Science 52

Data Science Machine Learning ML Python 52

Towards AI

JULY 19, 2023

How to Start Your ML Journey? | | Results from LinkedIn Polls Photo by Susan Q Yin / Unsplash Someone trying to start their ML journey would be confused about where to start. A few ML Memes that I found funny. Hence, I conducted polls in different ML groups for the two questions. What is important?

ML 72

ML Convolutional Neural Networks Neural Network Deep Learning 72

Mlearning.ai

MARCH 25, 2023

Translation invariance and translation equivariance are two important concepts in convolutional neural networks (CNNs) related to the… Continue reading on MLearning.ai »

Convolutional Neural Networks 52

Convolutional Neural Networks Neural Network Computer Vision Deep Learning 52

Marktechpost

DECEMBER 15, 2023

This method involves the application of a generative neural network, specifically a Generative Adversarial Network (GAN), a form of AI. According to researchers, this is the reason for developing GAN, an AI-based generative neural network trained using high-resolution radar precipitation fields.

Deep Learning 101

Deep Learning Convolutional Neural Networks Neural Network Artificial Intelligence 101

Marktechpost

OCTOBER 10, 2023

Its applications are used in many fields, such as image and speech recognition for language processing, object detection, and medical imaging diagnostics; finance for algorithmic trading and fraud detection; autonomous vehicles using convolutional neural networks for real-time decision-making; and recommendation systems for personalized content.

Deep Learning 112

Deep Learning Convolutional Neural Networks Neural Network Computer Vision 112

Mlearning.ai

JUNE 26, 2023

Training a convolutional neural network (ConvNet or CNN) from scratch might sound challenging, especially for newcomers in the field… Continue reading on MLearning.ai »

Convolutional Neural Networks 52

Convolutional Neural Networks Neural Network Computer Vision Deep Learning 52

Heartbeat

DECEMBER 14, 2023

is well known for his work on optical character recognition and computer vision using convolutional neural networks (CNN), and is a founding father of convolutional nets. in 1998, In general, LeNet refers to LeNet-5 and is a simple convolutional neural network. > Finished chain. . >

Convolutional Neural Networks 52

Convolutional Neural Networks Neural Network Deep Learning Computer Scientist 52

Marktechpost

APRIL 24, 2024

The model comprises a Convolutional Neural Network (CNN) backbone, a prototype layer, and a fully connected final layer. Don’t Forget to join our 40k+ ML SubReddit The post Interpretable Deep Learning for Biodiversity Monitoring: Introducing AudioProtoPNet appeared first on MarkTechPost.

Deep Learning 101

Deep Learning Convolutional Neural Networks Neural Network Automation 101

Marktechpost

JUNE 30, 2023

The first step depends on using a detector based on a Convolutional Neural Network (CNN). They show that a real-time model for any arbitrary data segment is feasible using the computational efficiency of convolutional neural networks (CNNs). Segmentation masks for each instance in the image are generated.

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