Marktechpost

MAY 5, 2024

In recent years, the demand for AI and Machine Learning has surged, making ML expertise increasingly vital for job seekers. Machine Learning with Python This course covers the fundamentals of machine learning algorithms and when to use each of them. and evaluating the same.

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Heartbeat

AUGUST 21, 2023

In this guide, we’ll talk about Convolutional Neural Networks, how to train a CNN, what applications CNNs can be used for, and best practices for using CNNs. What Are Convolutional Neural Networks CNN? CNNs learn geometric properties on different scales by applying convolutional filters to input data.

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Analytics Vidhya

JANUARY 24, 2022

Introduction to Deep Learning Artificial Intelligence, deep learning, machine learning?—?whatever The post Introductory note on Deep Learning appeared first on Analytics Vidhya. whatever you’re doing if you don’t understand it?—?learn

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Marktechpost

SEPTEMBER 10, 2023

Researchers think that high-speed testing using Deep Learning models can help us understand these effects better and speed up catalyst development. The way a catalyst’s surface is shaped matters for certain chemical reactions due to various properties of the catalyst, which we study in Surface Chemistry.

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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.

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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

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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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

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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Heartbeat

MARCH 1, 2023

Before being fed into the network, the photos are pre-processed and shrunk to the same size. A convolutional neural network (CNN) is primarily used for image classification. Convolutional, pooling, and fully linked layers are some of the layers that make up a CNN. X_train = X_train / 255.0 X_test = X_test / 255.0

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Mlearning.ai

JULY 1, 2023

In this article, we will delve into two approaches: leveraging deep learning with facial recognition and employing logistic regression using tweet history in PySpark. Submission Suggestions Predicting Gender from Twitter: Unveiling Insights with Deep Learning and Machine Learning was originally published in MLearning.ai

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Towards AI

APRIL 7, 2024

With the emergence of ARCGISpro which will replace ArcMap by 2026 mainly focusing on data science and machine learning, all the signs that machine learning is the future of GIS and you might have to learn some principles of data science, but where do you start, let us have a look.

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ODSC - Open Data Science

JULY 18, 2023

Here at ODSC, we couldn’t be more excited to announce Microsoft Azure’s tutorial series on Deep Learning and NLP, now available for free on Ai+. This team has a passion for fostering proficiency in AI, and can’t wait to help you deepen your knowledge in machine learning and deep learning. Sign me up!

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Analytics Vidhya

OCTOBER 9, 2022

Introduction In recent times, whenever we wish to perform image segmentation in machine learning, the first model we think of is the U-Net. U-Net is an encoder-decoder convolutional neural network with […]. It has been revolutionary in performance improvement compared to previous state-of-the-art methods.

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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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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

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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Towards AI

SEPTEMBER 1, 2023

That’s where Machine Learning, and specifically Deep Learning, shines, abbreviating the processing time to mere minutes! ℝⁿ), which introduces greater complexity and requires the utilization of Deep Learning techniques. Applied in the more diverse domains such as aero spatial, automobile, energy, etc.,

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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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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!

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AI News

JULY 27, 2023

The collaboration combines the strengths of three industry leaders: Graphcore, renowned for its Intelligence Processing Units (IPUs) hardware; UbiOps, a powerful machine learning operations (MLOps) platform; and Gcore Cloud, known for its robust cloud infrastructure. The event is co-located with Digital Transformation Week.

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NYU Center for Data Science

NOVEMBER 23, 2022

A new study “Generalizable deep learning model for early Alzheimer’s disease detection from structural MRIs ” by CDS researchers has developed a new approach to the early detection of Alzheimer’s using 3D deep convolutional neural networks. By Meryl Phair

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Heartbeat

NOVEMBER 27, 2023

Deep learning automates and improves medical picture analysis. Convolutional neural networks (CNNs) can learn complicated patterns and features from enormous datasets, emulating the human visual system. Convolutional Neural Networks (CNNs) Deep learning in medical image analysis relies on CNNs.

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AI Weekly

OCTOBER 26, 2023

cryptopolitan.com Applied use cases Alluxio rolls out new filesystem built for deep learning Alluxio Enterprise AI is aimed at data-intensive deep learning applications such as generative AI, computer vision, natural language processing, large language models and high-performance data analytics. voxeurop.eu

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PyImageSearch

MARCH 30, 2023

If you want a gentle introduction to machine learning for computer vision, you’re in the right spot. Here at PyImageSearch we’ve been helping people just like you master deep learning for computer vision. Also, you might want to check out our computer vision for deep learning program before you go.

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Pickl AI

JULY 28, 2023

Image Recognition using Machine Learning and MATLAB: With the advent of Machine Learning , it is now possible for computers to recognize and decipher objects, patterns, and other properties in digital photographs. They consist of multiple layers, including convolutional, pooling, and fully connected layers.

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Towards AI

JULY 31, 2023

Photo by Tobias Reich on Unsplash In the ever-evolving world of artificial intelligence, Convolutional Neural Networks (CNNs) have emerged as a revolutionary technology, reshaping the fields of computer vision and image recognition. Filters, also known as kernels. red, green, blue).

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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 FGS is a flexible instrument for acute pain evaluation that can be used in a variety of contexts due to its ease of use and practicality.

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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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Mlearning.ai

JULY 11, 2023

Deep learning has transformed artificial intelligence, allowing machines to learn and make smart decisions. If you’re interested in exploring deep learning, this step-by-step guide will help you learn the basics and develop the necessary skills. Learn how to fine-tune model parameters effectively.

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AI Weekly

MARCH 21, 2024

nature.com Authorship attribution in twitter: a comparative study of machine learning and deep learning approaches This study proposes an AA approach using machine and deep learning algorithms to accurately predict the author of unknown posts on social media platforms.

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Marktechpost

DECEMBER 15, 2023

In this manner, from coarsely resolved data, the GAN learns how to produce realistic precipitation fields and determine their temporal sequence. Compared to trilinear interpolation and a classical convolutional neural network, the generative model reconstructs the resolution-dependent extreme value distribution with high skill.

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AWS Machine Learning Blog

MARCH 30, 2023

In this post, we introduce a new interactive tool called Snapper, powered by a machine learning (ML) model that reduces the effort required of annotators. The model extracts features from the image using a convolutional neural network. As input, the model takes an image and a corresponding bounding box annotation.

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How to Learn Machine Learning

MAY 14, 2023

Embarking on a career as a Machine Learning Engineer has become increasingly popular in recent years. This is because machine learning has evolved into a driving force for various industries such as finance, healthcare, marketing, and many more. The Machine Learning Engineer Career Path 1.

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Marktechpost

JULY 5, 2023

This is where machine learning techniques are proven to be useful. However, the main distinction in weather forecasting, spatial downscaling, and other CV tasks is that the machine learning model needs to utilize exogenous inputs in various modalities.

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Analytics Vidhya

FEBRUARY 16, 2022

Since 2012 after convolutional neural networks(CNN) were introduced, we moved away from handcrafted features to an end-to-end approach using deep neural networks. The post Classification without Training Data: Zero-shot Learning Approach appeared first on Analytics Vidhya.

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Heartbeat

JUNE 23, 2023

However, with the advent of deep learning, researchers have explored various neural network architectures to model and forecast time series data. In this post, we will look at deep learning approaches for time series analysis and how they might be used in real-world applications. Let’s dive in!

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