Mlearning.ai

APRIL 1, 2023

Doc2Vec Doc2Vec, also known as Paragraph Vector, is an extension of Word2Vec that learns vector representations of documents rather than words. Doc2Vec was introduced in 2014 by a team of researchers led by Tomas Mikolov. Doc2Vec learns vector representations of documents by combining the word vectors with a document-level vector.

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Viso.ai

JULY 9, 2024

StyleGAN is GAN (Generative Adversarial Network), a Deep Learning (DL) model, that has been around for some time, developed by a team of researchers including Ian Goodfellow in 2014. Before StyleGAN, NVIDIA did come up with the predecessor- ProGAN, however, this model could not fine-control the features of images generated.

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Towards AI

AUGUST 11, 2023

GANs are a part of the deep-learning world and were very introduced by Ian Goodfellow and his collaborators in 2014, After that GANs have rapidly captivated many researchers’ eyes which resulted in much research and also helped to redefine the boundaries of creativity and artificial intelligence in the world of AI 1.1

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John Snow Labs

MAY 23, 2023

Visual question answering (VQA), an area that intersects the fields of Deep Learning, Natural Language Processing (NLP) and Computer Vision (CV) is garnering a lot of interest in research circles. For visual question answering in Deep Learning using NLP, public datasets play a crucial role. Is aqua the maximum?

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PyImageSearch

NOVEMBER 13, 2023

Home Table of Contents Faster R-CNNs Object Detection and Deep Learning Measuring Object Detector Performance From Where Do the Ground-Truth Examples Come? One of the most popular deep learning-based object detection algorithms is the family of R-CNN algorithms, originally introduced by Girshick et al.

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NVIDIA

SEPTEMBER 6, 2023

Pioneering AI in Physics In 2014, her life’s work took her more than 7,000 miles from her Shanghai home to Princeton University’s prestigious plasma physics lab, where she earned a Ph.D. Then he explained why he wanted to take an approach, popular among researchers, of using high-temperature superconducting magnets to control the plasma.

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Mlearning.ai

MARCH 31, 2023

In this story, we talk about how to build a Deep Learning Object Detector from scratch using TensorFlow. The output layer is set to use Softmax Activation Function as usual in Deep Learning classifiers. That time, tensorflow/pytorch and the Deep Learning technology were not ready yet.

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ML Review

MARCH 31, 2019

In 2014, a group of researchers at Google and NYU found that it was far too easy to fool ConvNets with an imperceivable, but carefully constructed nudge in the input. Up to this point, machine learning algorithms simply didn’t work well enough for anyone to be surprised when it failed to do the right thing. Let’s look at an example.

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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., 2018; Sitawarin et al., 2018; Papernot et al., 2013; Goodfellow et al., For instance, Xu et al.

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Dlabs.ai

AUGUST 20, 2020

If you’re looking for the best free eBooks related to artificial intelligence, machine learning, or deep learning – this list is for you. Dive into Deep Learning Authors: Aston Zhang, Zack C. Smola The first eBook on our must-read list is a deep-dive into deep learning.

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ML Review

JUNE 4, 2018

These ideas also move in step with the explainability of results. Image captioning (circa 2014) Image captioning research has been around for a number of years, but the efficacy of techniques was limited, and they generally weren’t robust enough to handle the real world.

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Heartbeat

FEBRUARY 27, 2023

Photo by Markus Spiske on Unsplash Deep learning has grown in importance as a focus of artificial intelligence research and development in recent years. Deep Reinforcement Learning (DRL) and Generative Adversarial Networks (GANs) are two promising deep learning trends.

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AWS Machine Learning Blog

NOVEMBER 15, 2023

As an example downstream application, the fine-tuned model can be used in pre-labeling workflows such as the one described in Auto-labeling module for deep learning-based Advanced Driver Assistance Systems on AWS. His core interests include deep learning and serverless technologies.

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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. Do you think learning computer vision and deep learning has to be time-consuming, overwhelming, and complicated?

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Heartbeat

OCTOBER 13, 2023

Machine learning techniques are commonly used, such as ARIMA (AutoRegressive Integrated Moving Average), exponential smoothing, and deep learning models. Modeling Techniques: Time series data can be analyzed and modeled using various techniques, including statistical models, machine learning models, and deep learning models.

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Viso.ai

JULY 17, 2024

Output from Neural Style Transfer – source Neural Style Transfer Explained Neural Style Transfer follows a simple process that involves: Three images, the image from which the style is copied, the content image, and a starting image that is just random noise. With deep learning, the results were impressively good.

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AWS Machine Learning Blog

JULY 13, 2023

Recent years have shown amazing growth in deep learning neural networks (DNNs). There are a number of theories that try to explain this effect: When tensor updates are big in size, traffic between workers and the parameter server can get congested. International Conference on Machine Learning. PMLR, 2018. [2]

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Heartbeat

AUGUST 21, 2023

AlexNet significantly improved performance over previous approaches and helped popularize deep learning and CNNs. GoogLeNet: is a highly optimized CNN architecture developed by researchers at Google in 2014. VGG-16: does the Visual Geometry Group develop an intense CNN architecture at the University of Oxford?

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Viso.ai

DECEMBER 6, 2023

The most common example is security analytics , where deep learning models analyze CCTV footage to detect theft, traffic violations, or intrusions in real-time. ResNet Residual Neural Networks ( ResNets ) use the CNN architecture to learn complex visual patterns. This is the result of very small gradients during backpropagation.

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deepsense.ai

MAY 7, 2023

We talked about diffusion in deep learning, models that utilize it to generate images, and several ways of fine-tuning it to customize your generative model. We also explained the building blocks of Stable Diffusion and highlighted why its release last year was such a groundbreaking achievement. But don’t worry!

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ODSC - Open Data Science

JANUARY 29, 2024

GANs, introduced in 2014 paved the way for GenAI with models like Pix2pix and DiscoGAN. Open Source ML/DL Platforms: Pytorch, Tensorflow, and scikit-learn Hiring managers continue to favor the most popular open-source machine/deep learning platforms including Pytorch, Tensorflow, and scikit-learn.

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Heartbeat

OCTOBER 9, 2023

Artificial Intelligence (AI) Integration: AI techniques, including machine learning and deep learning, will be combined with computer vision to improve the protection and understanding of cultural assets. International Journal of Heritage in the Digital Era , 1 (1_suppl), 1–6. We pay our contributors, and we don't sell ads.

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Sebastian Ruder

MAY 22, 2020

While a PhD encourages you to delve deep into a specific topic, you can add value by making connections between different topics or entirely different fields. Many ideas in deep learning take inspiration from other fields such as biology ( Hinton et al., 2014 ), neuroscience ( Wang et al., 2016 ), physics ( Cohen et al.,

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Mlearning.ai

JUNE 8, 2023

Vector Embeddings for Developers: The Basics | Pinecone Used geometry concept to explain what is vector, and how raw data is transformed to embedding using embedding model. Pinecone Used a picture of phrase vector to explain vector embedding. What are Vector Embeddings?

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John Snow Labs

AUGUST 7, 2024

A significant milestone was reached in 2014 with the introduction of Generative Adversarial Networks (GANs). Gen AI, particularly through deep learning models, has shown remarkable accuracy in diagnosing diseases from medical images. However as AI technology progressed its potential within the field also grew.

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Viso.ai

JANUARY 2, 2024

VGGNet , introduced by Simonyan and Zisserman in 2014, emphasized the importance of depth in CNN architectures through its 16-19 layer CNN network. Making CNN models more interpretable and explainable. However, these advancements come with their own set of challenges: Overcoming the heavy reliance on large, labeled datasets.

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TheSequence

NOVEMBER 22, 2023

In the past, I’ve spent a lot of time working on more efficient silicon architectures, particularly dataflow architectures, and advanced numerical quantization approaches for deep learning. One is a more formal view of explainability.

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John Snow Labs

FEBRUARY 29, 2024

A significant milestone was reached in 2014 with the introduction of Generative Adversarial Networks (GANs). Gen AI, particularly through deep learning models, has shown remarkable accuracy in diagnosing diseases from medical images. However as AI technology progressed its potential within the field also grew.

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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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Heartbeat

APRIL 18, 2023

The VGG model The VGG ( Visual Geometry Group ) model is a deep convolutional neural network architecture for image recognition tasks. It was introduced in 2014 by a group of researchers (A. Deep learning architectures called VGG models have attained state-of-the-art performance in various image recognition tasks, including HAR.

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Viso.ai

JANUARY 19, 2024

In the following, we will explain what Deepfakes are, how to identify them and discuss the impact of AI-generated photos and videos. History and Rise of Deep-Fake Technology The concept emerged from academic research in the early 2010s, focusing on facial recognition and computer vision. What are Deepfakes?

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