Marktechpost

JANUARY 13, 2024

Join our 36k+ ML SubReddit , 41k+ Facebook Community, Discord Channel , and LinkedIn Gr oup. They recommend the use of CLIP models in the event of a significant domain transition. Check out the Paper, Project, and Github. All credit for this research goes to the researchers of this project. Also, don’t forget to follow us on Twitter.

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Heartbeat

JUNE 27, 2023

Photo by Resource Database on Unsplash Introduction Neural networks have been operating on graph data for over a decade now. Neural networks leverage the structure and properties of graph and work in a similar fashion. Graph Neural Networks are a class of artificial neural networks that can be represented as graphs.

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Unite.AI

NOVEMBER 6, 2023

With these advancements, it’s natural to wonder: Are we approaching the end of traditional machine learning (ML)? In this article, we’ll look at the state of the traditional machine learning landscape concerning modern generative AI innovations. What is Traditional Machine Learning?

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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. It has an excellent reputation as a tool for predicting many kinds of problems in data science and machine learning. It was envisioned by Thongsuwan et al.,

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Marktechpost

MAY 26, 2024

The rapid growth of AI and complex neural networks drives the need for efficient hardware that suits power and resource constraints. HW-NAS optimizes neural network models by considering IMC hardware’s specific features and constraints, aiming for efficient deployment.

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Marktechpost

MAY 7, 2024

Current computerized systems employ machine learning techniques, from shallow learning that relies on hand-crafted features to more advanced deep learning models that extract features directly from raw EEG data. These technologies aim to mimic the precision of human analysts while surpassing their speed and endurance.

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Marktechpost

APRIL 24, 2024

While traditional PAM analysis is time-consuming, recent advancements in deep learning technology offer promising solutions for automating bird species identification from audio recordings. Preliminary research in interpretable deep learning for audio includes deep prototype learning, initially proposed for image classification.

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The MLOps Blog

NOVEMBER 14, 2023

Deep learning models are typically highly complex. While many traditional machine learning models make do with just a couple of hundreds of parameters, deep learning models have millions or billions of parameters. This is where visualizations in ML come in.

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Bugra Akyildiz

FEBRUARY 3, 2024

Google wrote an excellent ML efficiency post on how they optimized mixed-input matrix multiplication for LLMs. SMAD traditionally relies on hand-crafted features and statistical models, but Netflix in this post how they leverage deep learning , particularly convolutional neural networks (CNNs) , for superior performance and generalizability.

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Marktechpost

JANUARY 14, 2024

There has been a meteoric rise in the use of deep learning in image processing in the past several years. The robust feature learning and mapping capabilities of deep learning-based approaches enable them to acquire intricate blur removal patterns from large datasets.

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Marktechpost

NOVEMBER 16, 2023

A new research paper presents a deep learning-based classifier for age-related macular degeneration (AMD) stages using retinal optical coherence tomography (OCT) scans. The research details creating a deep learning-based system for automated AMD detection and staging using retinal OCT scans. in a real-world test set.

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Viso.ai

APRIL 29, 2024

AlexNet is an Image Classification model that transformed deep learning. It was introduced by Geoffrey Hinton and his team in 2012, and marked a key event in the history of deep learning, showcasing the strengths of CNN architectures and its vast applications. In that competition, AlexNet performed exceptionally well.

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Heartbeat

MAY 2, 2023

Artificial intelligence has undergone a revolution thanks to deep learning. Deep learning allows machines to learn from vast amounts of data and carry out complex tasks that were previously only considered possible by humans (like translation between languages, recognizing objects etc.).

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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. Participants also gain hands-on experience with open-source frameworks and libraries like TensorFlow and Scikit-learn.

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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?

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Marktechpost

OCTOBER 10, 2023

Deep learning, a machine learning subset, automatically learns complex representations from the input. Convolutional neural networks (CNNs) and vision transformers (ViT), two examples of deep learning models for computer vision, analyze signals by assuming planar (flat) regions.

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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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Marktechpost

DECEMBER 29, 2023

Consequently, the researchers of Plant Phenomics have introduced BarbNet, a deep-learning model designed specifically for the automated detection and phenotyping of barbs in microscopic images of awns. Despite their importance is evident, analyzing these small structures has been challenging due to the lack of automated tools.

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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. The event is co-located with Digital Transformation Week.

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Marktechpost

DECEMBER 15, 2023

This method involves the application of a generative neural network, specifically a Generative Adversarial Network (GAN), a form of AI. This GAN is trained using high-resolution radar precipitation data, allowing it to learn and mimic realistic precipitation fields with significantly higher spatial and temporal resolutions.

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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.

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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. Image Classification Using Machine Learning CNN Image Classification (Deep Learning) Example applications of Image Classification Let’s dive deep into it! How Does Image Classification Work?

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Mlearning.ai

FEBRUARY 25, 2023

How does the Artificial Neural Network algorithm work? In the same way, artificial neural networks (ANNs) were developed inspired by neurons in the brain. Complex machine-learning problems such as image classification, recommendation systems, and language-to-language translation have been solved with this technique.

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Marktechpost

SEPTEMBER 1, 2023

Incorporating machine learning and deep learning algorithms has shown promise in bolstering security. In addition, the study highlights that hybrid schemes combining deep learning features with deep learning/machine learning classification can significantly improve authentication performance.

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Marktechpost

NOVEMBER 30, 2023

Addressing this challenge, a study by researchers from Tokyo University of Science (TUS), Japan, in collaboration with esteemed institutions, introduced a new deep learning model. The researchers constructed a binary classifier employing 80 convolutional neural networks.

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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.

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Marktechpost

JANUARY 16, 2024

Person Re-identification (Person Re-ID) in Machine Learning uses deep learning models like convolutional neural networks to recognize and track individuals across different camera views, holding promise for surveillance and public safety but raising significant privacy concerns.

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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

DECEMBER 12, 2023

The FGS has been used to predict facial landmark placements and pain scores by utilizing deep neural networks and machine learning models. The post These Fully Automated Deep Learning Models Can Be Used For Pain Prediction Using The Feline Grimace Scale (FGS) With Smartphone Integration appeared first on MarkTechPost.

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Viso.ai

DECEMBER 3, 2023

How pose estimation works: Deep learning methods Use Cases and pose estimation applications How to get started with AI motion analysis Real-time full body pose estimation in construction – built with Viso Suite About us: Viso.ai Today, the most powerful image processing models are based on convolutional neural networks (CNNs).

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Viso.ai

DECEMBER 3, 2023

The recent deep learning algorithms provide robust person detection results. However, deep learning models such as YOLO that are trained for person detection on a frontal view data set still provide good results when applied for overhead view person counting ( TPR of 95%, FPR up to 0.2% ).

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Heartbeat

AUGUST 29, 2023

Deep learning is a branch of machine learning that makes use of neural networks with numerous layers to discover intricate data patterns. Deep learning models use artificial neural networks to learn from data. Deep learning models use artificial neural networks to learn from data.

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Heartbeat

APRIL 23, 2023

Photo by RetroSupply on Unsplash Introduction Deep learning has been widely used in various fields, such as computer vision, NLP, and robotics. The success of deep learning is largely due to its ability to learn complex representations from data using deep neural networks.

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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).

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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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Mlearning.ai

DECEMBER 13, 2023

Mastering Deep Learning and AI Interview Questions: What You Need to Know Image created by the author on Canva Knowledge is power, but enthusiasm pulls the switch.” Ever wondered what it takes to excel in deep learning interviews? This makes learning very slow or stops it altogether. said Ivern Ball.

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Viso.ai

MARCH 26, 2024

The introduction of the Transformer model was a significant leap forward for the concept of attention in deep learning. Uniquely, this model did not rely on conventional neural network architectures like convolutional or recurrent layers. without conventional neural networks. Vaswani et al.

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