AssemblyAI

MARCH 21, 2023

While AI systems like ChatGPT or Diffusion models for Generative AI have been in the limelight in the past months, Graph Neural Networks (GNN) have been rapidly advancing. And why do Graph Neural Networks matter in 2023? We find that the term Graph Neural Network consistently ranked in the top 3 keywords year over year.

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Flipboard

NOVEMBER 11, 2024

This article explains, through clear guidelines, how to choose the right machine learning (ML) algorithm or model for different types of real-world and business problems.

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Analytics Vidhya

APRIL 1, 2021

ArticleVideo Book This article was published as a part of the Data Science Blogathon. The post Is Gradient Descent sufficient for Neural Network? Introduction An important factor that is the basis of any. appeared first on Analytics Vidhya.

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Analytics Vidhya

MARCH 10, 2022

This article was published as a part of the Data Science Blogathon. Apple’s Siri and Google’s voice search both use Recurrent Neural Networks (RNNs), which are the state-of-the-art method for sequential data. The post A Brief Overview of Recurrent Neural Networks (RNN) appeared first on Analytics Vidhya.

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Analytics Vidhya

JANUARY 28, 2022

This article was published as a part of the Data Science Blogathon. Introduction Feedforward Neural Networks, also known as Deep feedforward Networks or Multi-layer Perceptrons, are the focus of this article. We’ll do our best to […].

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Analytics Vidhya

OCTOBER 8, 2020

This article was published as a part of the Data Science Blogathon. Introduction I have been thinking of writing something related to Recurrent Neural. The post Recurrent Neural Networks for Sequence Learning appeared first on Analytics Vidhya.

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Analytics Vidhya

JULY 23, 2020

Note: This article was originally published on May 29, 2017, and updated on July 24, 2020 Overview Neural Networks is one of the most. The post Understanding and coding Neural Networks From Scratch in Python and R appeared first on Analytics Vidhya.

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Analytics Vidhya

DECEMBER 14, 2020

This article was published as a part of the Data Science Blogathon. The post An Approach towards Neural Network based Image Clustering appeared first on Analytics Vidhya. Introduction: Hi everyone, recently while participating in a Deep Learning competition, I.

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Analytics Vidhya

MARCH 1, 2022

This article was published as a part of the Data Science Blogathon. Introduction Neural Networks have acquired enormous popularity in recent years due to their usefulness and ease of use in the fields of Pattern Recognition and Data Mining. The post What are Graph Neural Networks, and how do they work?

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Analytics Vidhya

OCTOBER 28, 2020

This article was published as a part of the Data Science Blogathon. The post What is the Convolutional Neural Network Architecture? Introduction Working on a Project on image recognition or Object Detection but. appeared first on Analytics Vidhya.

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Analytics Vidhya

JANUARY 30, 2022

This article was published as a part of the Data Science Blogathon. The post Activation Functions for Neural Networks and their Implementation in Python appeared first on Analytics Vidhya.

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IBM Journey to AI blog

JULY 6, 2023

To keep up with the pace of consumer expectations, companies are relying more heavily on machine learning algorithms to make things easier. How do artificial intelligence, machine learning, deep learning and neural networks relate to each other? What is a neural network? Machine learning is a subset of AI.

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Marktechpost

SEPTEMBER 3, 2023

Credit assignment in neural networks for correcting global output mistakes has been determined using many synaptic plasticity rules in natural neural networks. Methods of biological neuromodulation have inspired several plasticity algorithms in models of neural networks.

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Unite.AI

MAY 8, 2024

The ability to effectively represent and reason about these intricate relational structures is crucial for enabling advancements in fields like network science, cheminformatics, and recommender systems. Graph Neural Networks (GNNs) have emerged as a powerful deep learning framework for graph machine learning tasks.

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Analytics Vidhya

JUNE 18, 2021

ArticleVideo Book This article was published as a part of the Data Science Blogathon This article explains the problem of exploding and vanishing gradients while. The post The Challenge of Vanishing/Exploding Gradients in Deep Neural Networks appeared first on Analytics Vidhya.

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Pickl AI

NOVEMBER 13, 2024

Summary: Backpropagation in neural network optimises models by adjusting weights to reduce errors. Despite challenges like vanishing gradients, innovations like advanced optimisers and batch normalisation have improved their efficiency, enabling neural networks to solve complex problems.

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Analytics Vidhya

NOVEMBER 6, 2020

This article was published as a part of the Data Science Blogathon. The post A Short Intuitive Explanation of Convolutional Recurrent Neural Networks appeared first on Analytics Vidhya. Introduction Hello! Today I am going to try my best in explaining.

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Marktechpost

MAY 14, 2024

This article lists the top Deep Learning and Neural Networks books to help individuals gain proficiency in this vital field and contribute to its ongoing advancements and applications. Neural Networks and Deep Learning The book explores both classical and modern deep learning models, focusing on their theory and algorithms.

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Unite.AI

SEPTEMBER 12, 2024

In a significant leap forward, researchers at the University of Southern California (USC) have developed a new artificial intelligence algorithm that promises to revolutionize how we decode brain activity. DPAD: A New Approach to Neural Decoding The DPAD algorithm represents a paradigm shift in how we approach neural decoding.

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TensorFlow

FEBRUARY 6, 2024

Yet most machine learning (ML) algorithms allow only for regular and uniform relations between input objects, such as a grid of pixels, a sequence of words, or no relation at all. Apart from making predictions about graphs, GNNs are a powerful tool used to bridge the chasm to more typical neural network use cases.

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Analytics Vidhya

JULY 22, 2024

Introduction In this article, we dive into the top 10 publications that have transformed artificial intelligence and machine learning. We’ll take you through a thorough examination of recent advancements in neural networks and algorithms, shedding light on the key ideas behind modern AI.

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Marktechpost

OCTOBER 16, 2023

In a recent paper, “Towards Monosemanticity: Decomposing Language Models With Dictionary Learning,” researchers have addressed the challenge of understanding complex neural networks, specifically language models, which are increasingly being used in various applications. Check out the Research Article and Project Page.

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Analytics Vidhya

OCTOBER 17, 2022

This article was published as a part of the Data Science Blogathon. Introduction Neural networks (Artificial Neural Networks) are methods or algorithms that mimic a human brain’s operations to solve a complex problem that a normal algorithm can’t solve.

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Pickl AI

SEPTEMBER 15, 2024

Summary: Siamese Neural Networks use twin subnetworks to compare pairs of inputs and measure their similarity. Introduction Neural networks form the backbone of Deep Learning , allowing machines to learn from data by mimicking the human brain’s structure. What is a Siamese Neural Network?

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Analytics Vidhya

SEPTEMBER 12, 2023

Introduction Neuroevolution is a captivating field where AI merges neural networks and evolutionary algorithms to nurture its creative abilities. This article delves into neuroevolution, exploring its mechanics, applications, and significance.

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Analytics Vidhya

DECEMBER 8, 2021

After all, writing data science articles is from where it all started. The post Top 10 Articles Published in 2021 on Analytics Vidhya appeared first on Analytics Vidhya. Introduction Our long-time followers know how much writing is at the core of this organization. At the end of 2021, we are […].

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Analytics Vidhya

MARCH 20, 2024

Introduction Optimizing deep learning is a critical aspect of training efficient and accurate neural networks. Various optimization algorithms have been developed to improve the convergence speed.

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Viso.ai

MAY 17, 2024

Neural Networks have changed the way we perform model training. Neural networks, sometimes referred to as Neural Nets, need large datasets for efficient training. So, what if we have a neural network that can adapt itself to new data and has less complexity? What is a Liquid Neural Network?

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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. However, this approach tends to overlook optimizing the search space itself.

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Bugra Akyildiz

FEBRUARY 3, 2024

Articles Google wrote an article on the new graph based transformer architecture called Exphormer and how they scaled this model for their datasets. Graph transformers are a powerful architecture for machine learning on graph-structured data like molecules, social networks, and knowledge graphs.

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Towards AI

AUGUST 21, 2023

Deep Learning vs. Neural Networks: What’s the Difference? The rapid evolution of technology is molding our everyday existence as businesses turn more and more to sophisticated algorithms for efficiency. ML employs algorithms that learn patterns from data to perform tasks without explicit programming.

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Towards AI

FEBRUARY 21, 2023

Review: IDECNN-Improved Differential Evolution of Convolutional Neural Network (Image Classification) In this story, our research paper, titled: “Designing optimal convolutional neural network architecture using differential evolution algorithm” [1], is reviewed.

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ODSC - Open Data Science

SEPTEMBER 18, 2024

Convolutional neural networks (CNNs) differ from conventional, fully connected neural networks (FCNNs) because they process information in distinct ways. Combining this information with machine learning algorithms and data scientists could yield groundbreaking insights to advance sector research.

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Flipboard

DECEMBER 27, 2023

This article is part of our special report Top Tech 2024. If, however, the surveillance system fines one neglectful neighbor more than another because its algorithm favors one skin color or clothing style over another, opinions could change. This time, people care. Well, privacy activists do. “AI-driven

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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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Analytics Vidhya

AUGUST 23, 2021

This article was published as a part of the Data Science Blogathon Building a simple Machine Learning model using Pytorch from scratch. Image by my great learning Introduction Gradient descent is an optimization algorithm that is used to train machine learning models and is now used in a neural network.

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Unite.AI

NOVEMBER 6, 2023

In this article, we’ll look at the state of the traditional machine learning landscape concerning modern generative AI innovations. 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.

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