Analytics Vidhya

MAY 24, 2021

ArticleVideo Book This article was published as a part of the Data Science Blogathon In terms of ML, what neural network means? A neural network. The post Neural network and hyperparameter optimization using Talos appeared first on Analytics Vidhya.

Neural Network 268

Neural Network Data Science ML Deep Learning 268

Marktechpost

MAY 18, 2024

Graph Neural Networks GNNs are advanced tools for graph classification, leveraging neighborhood aggregation to update node representations iteratively. Effective graph pooling is essential for downsizing and learning representations, categorized into global and hierarchical pooling. Baselines include hierarchical methods (e.g.,

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Neural Network Categorization Deep Learning Data Analysis 105

Marktechpost

APRIL 5, 2024

In their paper, the researchers aim to propose a theory that explains how transformers work, providing a definite perspective on the difference between traditional feedforward neural networks and transformers. Transformer architectures, exemplified by models like ChatGPT, have revolutionized natural language processing tasks.

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Neural Network Natural Language Processing Categorization Explainability 125

Marktechpost

DECEMBER 16, 2023

” This innovative code, which simulates spiking neural networks inspired by the brain’s efficient data processing methods, originates from the efforts of a team at UC Santa Cruz. This publication offers candid insights into the convergence of neuroscience principles and deep learning methodologies.

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Marktechpost

JANUARY 24, 2024

With the growth of Deep learning, it is used in many fields, including data mining and natural language processing. However, deep neural networks are inaccurate and can produce unreliable outcomes. It can improve deep neural networks’ reliability in inverse imaging issues.

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Neural Network Deep Learning Data Mining Natural Language Processing 98

Marktechpost

MARCH 30, 2024

In 2024, the landscape of Python libraries for machine learning and deep learning continues to evolve, integrating more advanced features and offering more efficient and easier ways to build, train, and deploy models. PyTorch PyTorch is a widely used open-source machine learning library based on the Torch library.

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Deep Learning Machine Learning Python Computer Vision 121

Mlearning.ai

SEPTEMBER 4, 2023

The realm of deep learning has been experiencing a swift transformation, characterized by groundbreaking advancements that are continually… Continue reading on MLearning.ai »

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Deep Learning Neural Network ML Data Science 98

Marktechpost

MAY 5, 2024

In deep learning, especially in NLP, image analysis, and biology, there is an increasing focus on developing models that offer both computational efficiency and robust expressiveness. This layer adapts its kernel using a conditioning neural network, significantly enhancing Orchid’s ability to filter long sequences effectively.

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Marktechpost

MARCH 9, 2024

The evolution of artificial intelligence, particularly in the realm of neural networks, has significantly advanced our data processing and analysis capabilities. Among these advancements, the efficiency of training and deploying deep neural networks has become a paramount focus.

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Neural Network Python Deep Learning Artificial Intelligence 129

Marktechpost

DECEMBER 30, 2023

A new research paper from CSIRO, Australia, addresses these challenges by introducing a novel solution, ML-SEISMIC, a physics-informed deep neural network designed to align stress orientation data with an elastic model autonomously. The research team introduces ML-SEISMIC as a groundbreaking alternative.

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Marktechpost

NOVEMBER 25, 2023

Addressing this, Jason Eshraghian from UC Santa Cruz developed snnTorch, an open-source Python library implementing spiking neural networks, drawing inspiration from the brain’s remarkable efficiency in processing data. Traditional neural networks lack the elegance of the brain’s processing mechanisms.

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Neural Network Python Deep Learning Artificial Intelligence 108

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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Marktechpost

MAY 2, 2024

This study’s research area is artificial intelligence (AI) and machine learning, specifically focusing on neural networks that can understand binary code. This dataset allowed them to train neural networks to understand binary code more effectively. If you like our work, you will love our newsletter.

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Neural Network Machine Learning Large Language Models Automation 102

Marktechpost

MARCH 23, 2024

AI researchers are taking the game to a new level with geometric deep learning. At its core, TacticAI relies on a cutting-edge geometric deep learning pipeline to turn raw football data into structured inputs for AI models to understand. Check out the Paper and Blog. Also, don’t forget to follow us on Twitter.

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Deep Learning Neural Network Data Mining Artificial Intelligence 144

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.

Deep Learning 84

Deep Learning Convolutional Neural Networks Neural Network Automation 84

Towards AI

SEPTEMBER 10, 2023

What I’ve learned from the most popular DL course Photo by Sincerely Media on Unsplash I’ve recently finished the Practical Deep Learning Course from Fast.AI. I’ve passed many ML courses before, so that I can compare. So you definitely can trust his expertise in Machine Learning and Deep Learning.

Deep Learning 111

Deep Learning Neural Network ML Computer Vision 111

Mlearning.ai

FEBRUARY 4, 2024

This is the second part of my Neural Networks 101 series, in this blog we are going to discuss about the training of machine learning models. You can follow me on Twitter to learn more about this. Your input data travels through the neural network, layer by layer. I hope you are doing well. cat or dog).

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Marktechpost

DECEMBER 30, 2023

The remarkable potentials of Artificial Intelligence (AI) and Deep Learning have paved the way for a variety of fields ranging from computer vision and language modeling to healthcare, biology, and whatnot. SciML consists of three primary subfields, which include PDE solvers, PDE discovery, and operator learning.

Neural Network 121

Neural Network Deep Learning Computer Vision Machine Learning 121

Marktechpost

SEPTEMBER 1, 2023

Artificial intelligence’s allure has long been shrouded in mystique, especially within the enigmatic realm of deep learning. These intricate neural networks, with their complex processes and hidden layers, have captivated researchers and practitioners while obscuring their inner workings.

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Neural Network Deep Learning Categorization Artificial Intelligence 95

Marktechpost

OCTOBER 13, 2023

In the realm of deep learning, the challenge of developing efficient deep neural network (DNN) models that combine high performance with minimal latency across a variety of devices remains. Check out the Paper and Reference Article. All Credit For This Research Goes To the Researchers on This Project.

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Neural Network Deep Learning Algorithm Automation 129

Marktechpost

JULY 2, 2023

The exponentially expanding scale of deep learning models is a major force in advancing the state-of-the-art and a source of growing worry over the energy consumption, speed, and, therefore, feasibility of massive-scale deep learning. What happens if you run a Transformer model with an optical neural network?*

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Neural Network Deep Learning Computer Vision Machine Learning 95

Marktechpost

FEBRUARY 8, 2024

With the world of computational science continually evolving, physics-informed neural networks (PINNs) stand out as a groundbreaking approach for tackling forward and inverse problems governed by partial differential equations (PDEs). Join our 36k+ ML SubReddit , 41k+ Facebook Community, Discord Channel , and LinkedIn Gr oup.

Neural Network 128

Neural Network Deep Learning AI AI 128

Marktechpost

JANUARY 19, 2024

Neural network-based methods in estimating biological age have shown high accuracy but lack interpretability, prompting the development of a biologically informed tool for interpretable predictions in prostate cancer and treatment resistance. The most noteworthy result was probably obtained for the pan-tissue dataset.

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Neural Network Deep Learning Explainable AI Explainability 102

Marktechpost

NOVEMBER 8, 2023

The recent developments in the fields of Artificial Intelligence and Machine Learning have made everyone’s lives easier. With their incredible capabilities, AI and ML are diving into every industry and solving problems. Overconfidence is a prevalent issue, particularly in the context of deep neural networks.

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Deep Learning Neural Network Machine Learning ML 116

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. Graph-based ML models also lose important details about where the things are placed when molecules stick to each other. If you like our work, you will love our newsletter.

Convolutional Neural Networks 104

Convolutional Neural Networks Neural Network Deep Learning ML 104

Marktechpost

DECEMBER 15, 2023

Several new innovations have been made possible because of the advancements in the field of Artificial intelligence and Deep Learning. Complex tasks like text or picture synthesis, segmentation, and classification are being successfully handled with the help of neural networks. Check out the Paper.

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Neural Network Machine Learning Algorithm Deep Learning 121

Marktechpost

APRIL 15, 2024

The absence of a standardized benchmark for Graph Neural Networks GNNs has led to overlooked pitfalls in system design and evaluation. Existing benchmarks like Graph500 and LDBC need to be revised for GNNs due to differences in computations, storage, and reliance on deep learning frameworks. Million AI Audience?

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Marktechpost

APRIL 17, 2024

Transformers have revolutionized deep learning, yet their quadratic attention complexity limits their ability to process infinitely long inputs. The study hints at a path toward resolving the memory challenge in deep learning, a crucial precursor to tackling broader issues like reasoning. Check out the Paper.

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Marktechpost

JANUARY 10, 2024

The first perspective discussed using deep learning to pre-process intensity measurements before phase recovery. In the second perspective, the researchers focused on the Deep-learning-post-processing technique for phase recovery. They used deep learning during the phase recovery process.

Deep Learning 105

Deep Learning Neural Network ML Artificial Intelligence 105

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.,

Deep Learning 52

Deep Learning Convolutional Neural Networks Neural Network Machine Learning 52

Marktechpost

MAY 5, 2024

Physics-informed neural networks (PINNs) utilize PDE residuals in training to learn smooth solutions of known nonlinear PDEs, proving valuable in solving inverse problems. They also clarify how the deep learning structure of neural operators dilutes local dependency.

Deep Learning 98

Deep Learning Neural Network ML Artificial Intelligence 98

Marktechpost

OCTOBER 8, 2023

Can we adapt these hierarchy organization and parallel processing techniques in deep learning? Yes, the field of study is called Neural networks. This provides an advantage to NDP as it can operate upon any neural network of arbitrary size or architecture. Check out the Paper.

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Neural Network Automation Deep Learning Algorithm 106

Marktechpost

DECEMBER 25, 2023

A considerable amount of research has been done to address this problem, focusing mainly on using artificial neural networks. This unsupervised neural network reconstruction method retains a significant speedup of previous deep learning-based methods while improving the quality simultaneously.

Deep Learning 113

Deep Learning Neural Network AI Researcher AI Research 113

Marktechpost

OCTOBER 8, 2023

By utilizing finely developed neural network architectures, we have models that are distinguished by extraordinary accuracy within their respective sectors. Despite their accurate performance, we must still fully understand how these neural networks function. We have new advancements that have been there with each day.

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Deep Learning Neural Network Explainability Artificial Intelligence 135

Marktechpost

DECEMBER 28, 2023

Purdue University’s researchers have developed a novel approach, Graph-Based Topological Data Analysis (GTDA), to simplify interpreting complex predictive models like deep neural networks. The method was also applied to study chest X-ray diagnostics and compare deep-learning frameworks, showcasing its versatility.

Deep Learning 102

Deep Learning Data Analysis Neural Network Algorithm 102

Marktechpost

JANUARY 4, 2024

Qu Kun from the University of Science and Technology of the Chinese Academy of Sciences has created a solution called Spatial Architecture Characterization by Deep Learning (SPACEL). The post Meet SPACEL: A New Deep-Learning-based Analysis Toolkit for Spatial Transcriptomics appeared first on MarkTechPost.

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Deep Learning Neural Network Automation Algorithm 118

Towards AI

JANUARY 9, 2024

Now, I know, I know, we all want to work straight away on the latest and greatest technologies, which all work with Neural Networks. In other words, we all want to get directly into Deep Learning. But this is really a mistake if you want to take studying Machine Learning seriously and get a job in AI.

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