Mlearning.ai

APRIL 8, 2023

Charting the evolution of SOTA (State-of-the-art) techniques in NLP (Natural Language Processing) over the years, highlighting the key algorithms, influential figures, and groundbreaking papers that have shaped the field. NLP algorithms help computers understand, interpret, and generate natural language.

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Mlearning.ai

MARCH 9, 2023

Automated algorithms for image segmentation have been developed based on various techniques, including clustering, thresholding, and machine learning (Arbeláez et al., Understanding the robustness of image segmentation algorithms to adversarial attacks is critical for ensuring their reliability and security in practical applications.

Deep Learning 52

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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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Snorkel AI

JANUARY 10, 2023

In 2000, statistical algorithms came into existence, and these had the ability to handle tens of thousands of words. However, the problem with statistical algorithms was that, firstly, accuracy had reached a stable stage. So, you need to have that tight force alignment data to develop these statistical algorithm pipelines.

Deep Learning 52

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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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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.

Machine Learning 52

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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. AI began back in the 1950s as a simple series of “if, then rules” and made its way into healthcare two decades later after more complex algorithms were developed. AI drug discovery is exploding.

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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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AWS Machine Learning Blog

JANUARY 25, 2023

Apart from supporting explanations for tabular data, Clarify also supports explainability for both computer vision (CV) and natural language processing (NLP) using the same SHAP algorithm. Specifically, we show how you can explain the predictions of a text classification model that has been trained using the SageMaker BlazingText algorithm.

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Heartbeat

OCTOBER 9, 2023

These ground-breaking areas redefine how we connect with and learn from our collective past. Computer vision algorithms can reconstruct a highly detailed 3D model by photographing objects from different perspectives. But computer vision algorithms can assist us in digitally scanning and preserving these priceless manuscripts.

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Mlearning.ai

FEBRUARY 15, 2023

Uysal and Gunal, 2014). Figure 4 Data Cleaning Conventional algorithms are often biased towards the dominant class, ignoring the data distribution. Figure 11 Model Architecture The algorithms and models used for the first three classifiers are essentially the same.

Cross Validation 52

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AWS Machine Learning Blog

JULY 13, 2023

Recent years have shown amazing growth in deep learning neural networks (DNNs). Another way can be to use an AllReduce algorithm. For example, in the ring-allreduce algorithm, each node communicates with only two of its neighboring nodes, thereby reducing the overall data transfers.

Clustering 79

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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?

Deep Learning 52

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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.

Deep Learning 52

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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. Sharif et al.

Machine Learning 52

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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. Also in patient monitoring, image guided therapy, ultrasound and personal health teams have been creating ML algorithms and applications.

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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.

Deep Learning 52

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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.

Machine Learning 52

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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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AWS Machine Learning Blog

JANUARY 13, 2023

They were admitted to one of 335 units at 208 hospitals located throughout the US between 2014–2015. The eICU data is ideal for developing ML algorithms, decision support tools, and advancing clinical research. His research focuses on distributed/federated machine learning algorithms, systems, and applications.

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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.

Data Science 52

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Mlearning.ai

JUNE 8, 2023

Use algorithm to determine closeness/similarity of points. Doc2Vec: introduced in 2014, adds on to the Word2Vec model by introducing another ‘paragraph vector’. Knowledge graph embedding algorithms have become a powerful tool for representing and reasoning about complex structured data.

Machine Learning 52

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Heartbeat

APRIL 18, 2023

HAR systems typically use machine learning algorithms to learn and classify human actions based on the visual features extracted from the input data. The VGG model The VGG ( Visual Geometry Group ) model is a deep convolutional neural network architecture for image recognition tasks. Zisserman and K.

Deep Learning 52

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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.

Deep Learning 52

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ML Review

MARCH 5, 2019

Word embeddings Visualisation of word embeddings in AI Distillery Word2vec is a popular algorithm used to generate word representations (aka embeddings) for words in a vector space. Then, the algorithm proceeds with the following word as the new centre word, i.e. “learning”, sets up the new context, and repeats the same procedure.

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AWS Machine Learning Blog

FEBRUARY 29, 2024

Large-scale deep learning has recently produced revolutionary advances in a vast array of fields. is a startup dedicated to the mission of democratizing artificial intelligence technologies through algorithmic and software innovations that fundamentally change the economics of deep learning. and PyTorch 2.0

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DrivenData Labs

SEPTEMBER 10, 2024

Advance algorithms and analytic approaches for early prediction of AD/ADRD, with an emphasis on explainability of predictions. Top solvers from Phase 2 demonstrate algorithmic approaches on diverse datasets and share their results at an innovation event. Phase 2 [Build IT!] Phase 3 [Put IT All Together!]

Machine Learning 147

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ODSC - Open Data Science

FEBRUARY 29, 2024

The DeepMind Lab DeepMind, acquired by Google in 2014, has been responsible for groundbreaking advancements in AI, such as the AlphaGo program that defeated the world’s best Go players. Their research encompasses a broad spectrum of AI disciplines, including AI theory, reinforcement learning, and robotics. But that’s not all.

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