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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Towards AI

APRIL 23, 2024

However, generative models is not a new term and it has come a long way since Generative Adversarial Network (GAN) was published in 2014 [1]. It is one of the first algorithms to combine images based on deep learning. Neural Style Transfer (NST) was born in 2015 [2], slightly later than GAN.

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Towards AI

JULY 19, 2023

Deep learning algorithms can be applied to solving many challenging problems in image classification. Therefore, Now we conquer this problem of detecting the cracks using image processing methods, deep learning algorithms, and Computer Vision. 180–194, 2014. A4014004, 2014. Golparvar-Fard, and K.

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Towards AI

AUGUST 25, 2023

Machine learning (ML) is a subset of AI that provides computer systems the ability to automatically learn and improve from experience without being explicitly programmed. Deep learning (DL) is a subset of machine learning that uses neural networks which have a structure similar to the human neural system.

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Flipboard

JUNE 20, 2023

Deep learning Deep learning is a specific type of machine learning used in the most powerful AI systems. It imitates how the human brain works using artificial neural networks (explained below), allowing the AI to learn highly complex patterns in data.

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AssemblyAI

SEPTEMBER 12, 2023

Though once the industry standard, accuracy of these classical models had plateaued in recent years, opening the door for new approaches powered by advanced Deep Learning technology that’s also been behind the progress in other fields such as self-driving cars. The data does not need to be force-aligned.

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Snorkel AI

JANUARY 10, 2023

And, in 2014, deep learning came into play addressing some of these challenges, and it revolutionized the way ASR is done. Firstly, the quality is enough that companies were able to deploy deep-learning ASR in production. Then, this is deep-learning-based text-to-speech.

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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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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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Towards AI

AUGUST 11, 2023

The Trademark of GPT-5 In a 2014 BBC interview, Stephen Hawking said the following words – The development of full artificial intelligence could spell the end of the human race. The state of AI in 2014 was different from today. In that year, Google bought DeepMind — a machine learning startup — for over $600 Million.

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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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How to Learn Machine Learning

JULY 20, 2024

It falls under machine learning and uses deep learning algorithms and programs to create music, art, and other creative content based on the user’s input. However, significant strides were made in 2014 when Lan Goodfellow and his team introduced Generative adversarial networks (GANs).

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Mlearning.ai

APRIL 8, 2023

Deep Learning (Late 2000s — early 2010s) With the evolution of needing to solve more complex and non-linear tasks, The human understanding of how to model for machine learning evolved. 2014) Significant people : Geoffrey Hinton Yoshua Bengio Ilya Sutskever 5.

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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.

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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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Becoming Human

MARCH 16, 2023

Overhyped or not, investments in AI drug discovery jumped from $450 million in 2014 to a whopping $58 billion in 2021. Since the advent of deep learning in the 2000s, AI applications in healthcare have expanded. AI drug discovery is exploding. A few AI technologies are empowering drug design.

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Data Science Blog

MARCH 19, 2024

Über Exasol-CEO Martin Golombek Mathias Golombek ist seit Januar 2014 Mitglied des Vorstands der Exasol AG. Durch die Ausführung von ML-Modellen direkt in der Exasol-Datenbank können sie so die maximale Menge an Daten nutzen und das volle Potenzial ihrer Datenschätze ausschöpfen.

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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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Flipboard

AUGUST 25, 2023

Zhavoronkov has a narrower definition of AI drug discovery, saying it refers specifically to the application of deep learning and generative learning in the drug discovery space. The “deep learning revolution” — a time when development and use of the technology exploded — took off around 2014, Zhavoronkov said.

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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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ML Review

JUNE 4, 2018

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. However, in 2014 a number of high-profile AI labs began to release new approaches leveraging deep learning to improve performance.

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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. February 23, 2014. Images from ImageNet: the top row is from the mammal subtree, and the bottom is from the vehicle subtree.¹⁰ pre-training). January 29, 2015.

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Mlearning.ai

JULY 31, 2023

Things become more complex when we apply this information to Deep Learning (DL) models, where each data type presents unique challenges for capturing its inherent characteristics. 2014; Bojanowski et al., Likewise, sound and text have no meaning to a computer. Instead, why not use a set of embeddings that are already trained?

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AWS Machine Learning Blog

AUGUST 2, 2024

About the Author Xiang Song is a senior applied scientist at AWS AI Research and Education (AIRE), where he develops deep learning frameworks including GraphStorm, DGL and DGL-KE. He is now leading the development of GraphStorm, an open-source graph machine learning framework for enterprise use cases. He received his Ph.D.

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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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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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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. Kurakin et al, ICLR 2017.

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DagsHub

OCTOBER 23, 2024

Deep Learning Techniques Used to Manage Unstructured Data Now that you have seen some of the tools used in unstructured data management let’s explore the deep learning techniques you can use to process and understand unstructured data. The tool offers a web UI as well as Python and TypeScript SDKs for developers.

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Mlearning.ai

FEBRUARY 15, 2023

Uysal and Gunal, 2014). Deep learning models with multilayer processing architecture are now outperforming shallow or standard classification models in terms of performance [5]. Prediction of Solar Irradiation Using Quantum Support Vector Machine Learning Algorithm. Ensemble deep learning: A review.

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DagsHub

MARCH 27, 2024

The panorama evolved with the introduction of Generative Adversarial Networks or GANs in 2014. The creative potential expanded with Neural Style Transfer , Conditional GANs , transfer learning and diffusion techniques. Initially relying on traditional computer graphics , where artists created images using design applications.

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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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AWS Machine Learning Blog

JULY 13, 2023

Recent years have shown amazing growth in deep learning neural networks (DNNs). International Conference on Machine Learning. On large-batch training for deep learning: Generalization gap and sharp minima.” Toward understanding the impact of staleness in distributed machine learning.” PMLR, 2018. [2]

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PyImageSearch

DECEMBER 4, 2023

Built in 2014 along the Ankhor Canal, it’s fondly known as the “Snow Mosque” due to its pristine white marble construction. The courtyard of the Barak Khan Madrasa is open to visitors, offering a serene and introspective experience. Minor Mosque The Minor Mosque is a relatively new yet magnificent addition to Tashkent’s skyline.

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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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ML Review

FEBRUARY 26, 2018

Tasks such as “I’d like to book a one-way flight from New York to Paris for tomorrow” can be solved by the intention commitment + slot filing matching or deep reinforcement learning (DRL) model. Chitchatting, such as “I’m in a bad mood”, pulls up a method that marries the retrieval model with deep learning (DL).

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AWS Machine Learning Blog

NOVEMBER 16, 2023

Since 2014, the company has been offering customers its Philips HealthSuite Platform, which orchestrates dozens of AWS services that healthcare and life sciences companies use to improve patient care. Improve the quality and time to market for deep learning models in diagnostic medical imaging.

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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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