Towards AI

FEBRUARY 3, 2024

(Left) Photo by Pawel Czerwinski on Unsplash U+007C (Right) Unsplash Image adjusted by the showcased algorithm Introduction It’s been a while since I created this package ‘easy-explain’ and published on Pypi. A few weeks ago, I needed an explainability algorithm for a YoloV8 model. PLoS ONE 10(7), e0130140 (2015) [2] Montavon, G.,

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Flipboard

JULY 31, 2023

Importance and Role of Datasets in Machine Learning Data is king. Algorithms are important and require expert knowledge to develop and refine, but they would be useless without data. Datasets are to machine learning what fuel is to a car: they power the entire process. Object detection is useful for many applications (e.g.,

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AWS Machine Learning Blog

OCTOBER 5, 2023

Xin Huang is a Senior Applied Scientist for Amazon SageMaker JumpStart and Amazon SageMaker built-in algorithms. He focuses on developing scalable machine learning algorithms. From 2015–2018, he worked as a program director at the US NSF in charge of its big data program. He founded StylingAI Inc.,

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Flipboard

SEPTEMBER 4, 2023

Switching gears, imagine yourself being part of a high-tech research lab working with Machine Learning algorithms. Container runtimes are consistent, meaning they would work precisely the same whether you’re on a Dell laptop with an AMD CPU, a top-notch MacBook Pro , or an old Intel Lenovo ThinkPad from 2015.

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Towards AI

JULY 19, 2023

Basically crack is a visible entity and so image-based crack detection algorithms can be adapted for inspection. Deep learning algorithms can be applied to solving many challenging problems in image classification. Deep learning algorithms can be applied to solving many challenging problems in image classification.

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Towards AI

FEBRUARY 14, 2023

It involves using machine learning algorithms to generate new data based on existing data. Generative AI is a subset of artificial intelligence (AI) that involves using algorithms to create new data. Generative AI works by training algorithms on large datasets, which the algorithm can then use to generate new data.

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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.

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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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Dataconomy

MAY 8, 2023

AI drawing generators use machine learning algorithms to produce artwork What is AI drawing? You might think of AI drawing as a generative art where the artist combines data and algorithms to create something completely new. They use deep learning models to learn from large sets of images and make new ones that meet the prompts.

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Towards AI

JULY 23, 2023

Semi-Supervised Sequence Learning As we all know, supervised learning has a drawback, as it requires a huge labeled dataset to train. In 2015, Andrew M. To perform this, extractive summarization methods like tf-idf, and text-rank algorithms have been used.

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Pickl AI

JANUARY 23, 2023

A machine learning framework is a library, interface or any tool that is generally open source and enables the people to build various machine learning models with ease. People don’t even need the in-depth knowledge of the various machine learning algorithms as it contains pre-built libraries.

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PyImageSearch

JULY 3, 2023

Figure 1: Netflix Recommendation System (source: “Netflix Film Recommendation Algorithm,” Pinterest ). Netflix recommendations are not just one algorithm but a collection of various state-of-the-art algorithms that serve different purposes to create the complete Netflix experience.

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AWS Machine Learning Blog

OCTOBER 9, 2023

Users Amazon Personalize need to upload data containing their own customer’s interactions in order for the model to be able to learn these behavioral trends. He has 8 years of experience building out a variety of deep learning and other AI use cases and focuses on Personalization and Recommendation use cases with AWS.

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ML Review

MARCH 31, 2019

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. Source: Explaining and Harnessing Adversarial Examples , Goodfellow et al, ICLR 2015. Source: Robust Physical-World Attacks on Deep Learning Visual Classification.

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Pickl AI

AUGUST 22, 2024

Introduction Deep Learning frameworks are crucial in developing sophisticated AI models, and driving industry innovations. By understanding their unique features and capabilities, you’ll make informed decisions for your Deep Learning applications.

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Pickl AI

APRIL 8, 2024

This blog explores 13 major AI blunders, highlighting issues like algorithmic bias, lack of transparency, and job displacement. From the moment we wake up to the personalized recommendations on our phones to the algorithms powering facial recognition software, AI is constantly shaping our world.

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AWS Machine Learning Blog

APRIL 18, 2023

One of the major challenges in training and deploying LLMs with billions of parameters is their size, which can make it difficult to fit them into single GPUs, the hardware commonly used for deep learning. learning_rate – Controls the step size or learning rate of the optimization algorithm during training.

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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. Finally, one can use a sentence similarity evaluation metric to evaluate the algorithm. One such evaluation metric is the Bilingual Evaluation Understudy algorithm, or BLEU score.

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DagsHub

FEBRUARY 8, 2024

The advancements in deep learning have resulted in exceptional precision rates for object detection. 2) Deep learning algorithms with two stages, including examples such as various R-CNN models faster object separation from the background with faster speeds and higher accuracy. Sercan Çayır et al.

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IBM Journey to AI blog

AUGUST 11, 2023

Machine learning (ML), a subset of artificial intelligence (AI), is an important piece of data-driven innovation. Machine learning engineers take massive datasets and use statistical methods to create algorithms that are trained to find patterns and uncover key insights in data mining projects.

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Explosion

NOVEMBER 21, 2019

In 2016 we trained a sense2vec model on the 2015 portion of the Reddit comments corpus, leading to a useful library and one of our most popular demos. Try the new interactive demo to explore similarities and compare them between 2015 and 2019 sense2vec (Trask et. Interestingly, “to ghost” wasn’t very common in 2015.

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Pickl AI

FEBRUARY 14, 2023

Significantly, by leveraging technologies like deep learning and proprietary algorithms for analytics, Artivatic.ai Arya.ai One of the growing AI companies in India, Arya.ai, deploys Deep Learning solutions for the BFSI sector. launched its meta framework on TensorFlow in 2015. Artivatic.ai Artivatic.ai

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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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The MLOps Blog

NOVEMBER 30, 2022

These days enterprises are sitting on a pool of data and increasingly employing machine learning and deep learning algorithms to forecast sales, predict customer churn and fraud detection, etc., Most of its products use machine learning or deep learning models for some or all of their features.

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Snorkel AI

APRIL 11, 2023

The unprecedented amount of available data has been critical to many of deep learning’s recent successes, but this big data brings its own problems. Active learning is a really powerful data selection technique for reducing labeling costs. So for example, in 2015, fidget spinners were all the rage. AB : Got it.

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Snorkel AI

APRIL 11, 2023

The unprecedented amount of available data has been critical to many of deep learning’s recent successes, but this big data brings its own problems. Active learning is a really powerful data selection technique for reducing labeling costs. So for example, in 2015, fidget spinners were all the rage. AB : Got it.

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K-nearest Neighbors Clustering Deep Learning Deep Learning 52

Snorkel AI

APRIL 11, 2023

The unprecedented amount of available data has been critical to many of deep learning’s recent successes, but this big data brings its own problems. Active learning is a really powerful data selection technique for reducing labeling costs. So for example, in 2015, fidget spinners were all the rage. AB : Got it.

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AWS Machine Learning Blog

APRIL 18, 2023

One of the major challenges in training and deploying LLMs with billions of parameters is their size, which can make it difficult to fit them into single GPUs, the hardware commonly used for deep learning. learning_rate – Controls the step size or learning rate of the optimization algorithm during training.

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AWS Machine Learning Blog

NOVEMBER 19, 2024

Discover how to use pre-built algorithms, integrate custom models seamlessly, and harness the power of popular Python libraries within the SageMaker platform. You must bring your laptop to participate. Explore how this powerful tool streamlines the entire ML lifecycle, from data preparation to model deployment.

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Snorkel AI

FEBRUARY 13, 2023

For example, building algorithms that run on our cameras and detect if somebody is coming home, or if a package gets delivered for you. The voice remote was launched for Comcast in 2015. We also have a team focused on the Digital Home and the applications of AI inside of it. And finally, also, AI/ML innovation and educational efforts.

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Snorkel AI

FEBRUARY 13, 2023

For example, building algorithms that run on our cameras and detect if somebody is coming home, or if a package gets delivered for you. The voice remote was launched for Comcast in 2015. We also have a team focused on the Digital Home and the applications of AI inside of it. And finally, also, AI/ML innovation and educational efforts.

Machine Learning 52

Machine Learning Machine Learning Deep Learning Deep Learning 52

Dataconomy

NOVEMBER 20, 2023

Co-inventing AlexNet with Krizhevsky and Hinton, he laid the groundwork for modern deep learning. His work on the sequence-to-sequence learning algorithm and contributions to TensorFlow underscore his commitment to pushing AI’s boundaries. But, in 2015, he takes a leap of faith, leaving Google to co-found OpenAI.

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Towards AI

MAY 31, 2024

In deep learning, diffusion models have already replaced State-of-the-art generative frameworks like GANs or VAEs. In 2015 there was a paper published “Deep Unsupervised Learning using Nonequilibrium Thermodynamics” [1]. Inspired by these challenges came the origin story of diffusion models.

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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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Mlearning.ai

JUNE 5, 2023

Many Libraries: Python has many libraries and frameworks (We will be looking some of them below) that provide ready-made solutions for common computer vision tasks, such as image processing, face detection, object recognition, and deep learning. TensorFlow An open-source framework for machine learning and deep learning.

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Heartbeat

JULY 24, 2023

This article will cover briefly the architecture of the deep learning model used for the purpose. This algorithm also does tissue chopping to remove computational complexities. This particular algorithm is not restricted to human anatomy. which was published on March 28, 2023. The whole process is shown below.

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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!]

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