Viso.ai

FEBRUARY 11, 2024

Image classification employs AI-based deep learning models to analyze images and perform object recognition, as well as a human operator. It is one of the largest resources available for training deep learning models in object recognition tasks. They applied deep CNN over pre-trained ImageNet-1K, with 24.2M

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Heartbeat

DECEMBER 8, 2023

He focused on generative AI trained on large language models, The strength of the deep learning era of artificial intelligence has lead to something of a renaissance in corporate R&D in information technology, according to Yann LeCun, chief AI. Hinton is viewed as a leading figure in the deep learning community.

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Mlearning.ai

AUGUST 4, 2023

In this blog, we will try to deep dive into the concept of 1x1 convolution operation which appeared in the paper ‘Network in Network’ by Lin et al in (2013) and ‘Going Deeper with Convolutions’ by Szegedy et al (2014) that proposed the GoogLeNet architecture. 21 million ops) gets reduced by a factor of ~11.

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Mlearning.ai

MAY 18, 2023

The development of region-based convolutional neural networks (R-CNN) in 2013 marked a crucial milestone. R-CNN introduced the idea of using region proposals to identify potential object locations, which were then processed by a convolutional neural network for classification.

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Convolutional Neural Networks Computer Vision Neural Network Algorithm 52

NVIDIA

JUNE 9, 2023

On average, about every 60 years, an asteroid that’s larger than 65 feet in diameter will appear, similar to the one that exploded over Chelyabinsk, Russia, in 2013 , with the energy equivalent of about 440,000 tons of TNT, according to NASA. Through machine learning, the group trained a neural network called You Only Look Once Darknet.

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Dlabs.ai

MARCH 17, 2021

And her research expertise spans AI, machine learning , deep learning , computer vision , and cognitive neuroscience. To find out more about deep learning, check this Dlabs.AI Yann himself is a founding father of convolutional networks and one of the DjVu image compression technology’s leading creators.

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NLP People

JULY 24, 2019

The rise of NLP in the past decades is backed by a couple of global developments – the universal hype around AI, exponential advances in the field of Deep Learning and an ever-increasing quantity of available text data. This is especially relevant for the advanced, complex algorithms of the Deep Learning family.

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ODSC - Open Data Science

FEBRUARY 29, 2024

One of their notable recent projects is a new interpretability method for training neural networks with sparse, discrete, and controllable hidden states. They recently introduced a new AI system that can learn to play a variety of Atari games from raw pixels. But that’s not all.

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Lexalytics

APRIL 5, 2021

They were not wrong: the results they found about the limitations of perceptrons still apply even to the more sophisticated deep-learning networks of today. This book effectively killed off interest in neural networks at that time, and Rosenblatt, who died shortly thereafter in a boating accident, was unable to defend his ideas.

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Mlearning.ai

MARCH 9, 2023

Recent studies have demonstrated that deep learning-based image segmentation algorithms are vulnerable to adversarial attacks, where carefully crafted perturbations to the input image can cause significant misclassifications (Xie et al., 2013; Goodfellow et al., Towards deep learning models resistant to adversarial attacks.

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TheSequence

NOVEMBER 22, 2023

I co-authored my first AI-related paper in 2000 ( using neural networks to manage on-CPU hardware resources ). Understanding biological neural networks is one current focus. I don't have a favorite area, there are a ton of research problems I'm interested in and I've historically worked across many areas.

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Mlearning.ai

JUNE 16, 2023

The common practice for developing deep learning models for image-related tasks leveraged the “transfer learning” approach with ImageNet. Practitioners first trained a Convolutional Neural Network (CNN) to perform image classification on ImageNet (i.e. October 5, 2013. pre-training). fine-tuning).

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Convolutional Neural Networks Computer Vision AI AI 52

PyImageSearch

OCTOBER 2, 2023

Jump Right To The Downloads Section A Deep Dive into Variational Autoencoder with PyTorch Introduction Deep learning has achieved remarkable success in supervised tasks, especially in image recognition. Before the rise of GANs, there were other foundational neural network architectures for generative modeling.

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ML Review

MAY 3, 2018

Much the same way we iterate, link and update concepts through whatever modality of input our brain takes — multi-modal approaches in deep learning are coming to the fore. While an oversimplification, the generalisability of current deep learning approaches is impressive.

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ML Review

JUNE 4, 2018

However, in 2014 a number of high-profile AI labs began to release new approaches leveraging deep learning to improve performance. The first paper, to the best of our knowledge, to apply neural networks to the image captioning problem was Kiros et al. Building on seminal work from Kiros et al.

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Kavita Ganesan

MAY 24, 2022

And they also had started neural networks research long ago, but that research stopped because insufficient computation power. And neural networks now has become deep learning. But around 2013 is where data science started to really become a thing. And that’s a big field of study now.

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Marktechpost

JUNE 7, 2023

After all, you can use deep learning more practically than just amusement. Last of Us Sony Interactive Entertainment’s 2013 AI game The Last of Us has garnered a passionate fanbase—a survival horror game. In today’s games, AI is used to improve aesthetics and fix any problems that may arise during play.

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Topbots

SEPTEMBER 11, 2023

Simpler problems such as sentiment analysis for a specific type of text can often be solved with established machine learning methods like logistic regression, which are computationally less expensive than fancy deep learning methods. 3] Don Norman (2013). 4] Google, Gartner and Motista (2013).

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