AWS Machine Learning Blog

JUNE 3, 2025

For over a decade in the world of technology, Taras has led everything from tight-knit agile teams of 5 or more to a company of 90 people that became the best small IT company in Ukraine under 100 people in 2015.

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Flipboard

JULY 13, 2025

Modern machine learning algorithms can now distinguish between direct whale calls and their seafloor echoes with over 95% accuracy, even in noisy underwater environments. The International Quiet Ocean Experiment (IQOE), launched in 2015, now incorporates whale acoustics as a mapping tool in its worldwide research efforts.

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Dataconomy

MARCH 20, 2025

TensorFlow has revolutionized the field of machine learning and deep learning since its inception. TensorFlow is an open-source framework designed for machine learning and deep learning applications. Released as open-source in 2015 under the Apache 2.0 What is TensorFlow? in early 2017.

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ODSC - Open Data Science

MARCH 10, 2025

Over the past decade, data science has undergone a remarkable evolution, driven by rapid advancements in machine learning, artificial intelligence, and big data technologies. This blog dives deep into these changes of trends in data science, spotlighting how conference topics mirror the broader evolution of datascience.

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Analytics Vidhya

AUGUST 26, 2021

This article was published as a part of the Data Science Blogathon Introduction Tensorflow (hereinafter – TF) is a fairly young framework for deep machine learning, being developed in Google Brain. The post Tensorflow- An impressive deep learning library! appeared first on Analytics Vidhya.

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Analytics Vidhya

FEBRUARY 6, 2023

Introduction Deep learning has revolutionized computer vision and paved the way for numerous breakthroughs in the last few years. One of the key breakthroughs in deep learning is the ResNet architecture, introduced in 2015 by Microsoft Research.

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Towards AI

AUGUST 8, 2024

Photo by Marius Masalar on Unsplash Deep learning. A subset of machine learning utilizing multilayered neural networks, otherwise known as deep neural networks. If you’re getting started with deep learning, you’ll find yourself overwhelmed with the amount of frameworks. What is TensorFlow? In TensorFlow 2.0,

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Analytics Vidhya

JULY 3, 2024

Fully Convolutional Networks (FCNs) were first introduced in a seminal publication by Trevor Darrell, Evan Shelhamer, and Jonathan Long in 2015. Introduction Semantic segmentation, categorizing images pixel-by-pixel into specified groups, is a crucial problem in computer vision.

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PyImageSearch

JANUARY 30, 2023

This last blog of the series will cover the benefits, applications, challenges, and tradeoffs of using deep learning in the education sector. To learn about Computer Vision and Deep Learning for Education, just keep reading. As soon as the system adapts to human wants, it automates the learning process accordingly.

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insideBIGDATA

MARCH 4, 2023

analyzed over 10,000 AI companies and their funding data between 2015 and 2023. Our friends over at writerbuddy.ai The data was collected from CrunchBase, NetBase Quid, S&P Capital IQ, and NFX. Corporate AI investment has risen consistently to the tune of billions.

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Data Science Dojo

SEPTEMBER 13, 2023

Deep learning And NLP Deep Learning and Natural Language Processing (NLP) are like best friends in the world of computers and language. Deep Learning is when computers use their brains, called neural networks, to learn lots of things from a ton of information.

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Data Science Dojo

MARCH 4, 2024

Emerging as a key player in deep learning (2010s) The decade was marked by focusing on deep learning and navigating the potential of AI. Introduction of cuDNN Library: In 2014, the company launched its cuDNN (CUDA Deep Neural Network) Library. It provided optimized codes for deep learning models.

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DagsHub

JULY 25, 2024

Source: Author Introduction Deep learning, a branch of machine learning inspired by biological neural networks, has become a key technique in artificial intelligence (AI) applications. Deep learning methods use multi-layer artificial neural networks to extract intricate patterns from large data sets.

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Hacker News

OCTOBER 11, 2024

Deep learning — a software model that relies on billions of neurons and trillions of connections — requires immense computational power. What once seemed like science fiction — computers learning and adapting from vast amounts of data — was now a reality, driven by the raw power of GPUs.

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Dataconomy

OCTOBER 2, 2024

cum laude in machine learning from the University of Amsterdam in 2017. His academic work, particularly in deep learning and generative models, has had a profound impact on the AI community. In 2015, Kingma co-founded OpenAI, a leading research organization in AI, where he led the algorithms team. He earned his Ph.D.

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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

APRIL 23, 2024

Neural Style Transfer (NST) was born in 2015 [2], slightly later than GAN. It is one of the first algorithms to combine images based on deep learning. 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].

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Flipboard

NOVEMBER 16, 2024

Raw images are processed and utilized as input data for a 2-D convolutional neural network (CNN) deep learning classifier, demonstrating an impressive 95% overall accuracy against new images. The glucose predictions done by CNN are compared with ISO 15197:2013/2015 gold standard norms.

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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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Explosion

NOVEMBER 9, 2016

now features deep learning models for named entity recognition, dependency parsing, text classification and similarity prediction based on the architectures described in this post. You can now also create training and evaluation data for these models with Prodigy , our new active learning-powered annotation tool. Bowman et al.

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Towards AI

JULY 8, 2024

Introduction to AI Accelerators AI accelerators are specialized hardware designed to enhance the performance of artificial intelligence (AI) tasks, particularly in machine learning and deep learning. Sometime later, around 2015, the focus of CUDA transitioned towards supporting neural networks.

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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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Flipboard

SEPTEMBER 4, 2023

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. These images also support interfacing with the GPU, meaning you can leverage it for training your Deep Learning networks written in TensorFlow.

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AWS Machine Learning Blog

OCTOBER 5, 2023

He focuses on developing scalable machine learning algorithms. His research interests are in the area of natural language processing, explainable deep learning on tabular data, and robust analysis of non-parametric space-time clustering. Yida Wang is a principal scientist in the AWS AI team of Amazon. He founded StylingAI Inc.,

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Towards AI

AUGUST 11, 2023

loc[tabular_competition_ids]tabular_competitions.describe()[["RewardQuantity","TotalTeams"]] Code output Over the last decade, Kaggle has hosted numerous competitions centered around tabular data, with several since 2015 offering cash prizes of up to $100,000 for the winning team. The dataset is under Apache 2.0,

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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. 196–210, 2015. irregular illuminated conditions, shading, and blemishes.

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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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Google Research AI blog

MARCH 7, 2023

words per image on average, which is more than 3x the density of TextOCR and 25x more dense than ICDAR-2015. Dataset Training split Validation split Testing split Words per image ICDAR-2015 1,000 0 500 4.4 These OCR products digitize and democratize the valuable information that is stored in paper or image-based sources (e.g.,

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Towards AI

FEBRUARY 3, 2024

PLoS ONE 10(7), e0130140 (2015) [2] Montavon, G., eds) Explainable AI: Interpreting, Explaining and Visualizing Deep Learning. .: On pixel-wise explanations for non-linear classifier decisions by layer-wise relevance propagation. Lapuschkin, S., Müller, KR. Layer-Wise Relevance Propagation: An Overview. In: Samek, W.,

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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., 2015; Huang et al., an image) with the intention of causing a machine learning model to misclassify it (Goodfellow et al., 2012; Otsu, 1979; Long et al.,

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Towards AI

FEBRUARY 27, 2024

The whole machine learning industry since the early days was growing on open source solutions like scikit learn (2007) and then deep learning frameworks — TensorFlow (2015) and PyTorch (2016). More than 99% of Fortune 500 companies use open-source code [2].

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Towards AI

APRIL 19, 2024

Intelligent Medical Objects 👉Industry domain: AI, Health Tech, IT, NLP, Software, Analytics, Generative AI 👉Location: 3 offices 👉Year founded: 1994 👉Programming languages deployed: Angular, C#, SQL, Scikit, TensorFlow, Spark, GitHub, R, Python 👉Benefits: Flexible time off, family medical leave, pet insurance, (..)

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Towards AI

FEBRUARY 14, 2023

OpenAI, on the other hand, is an AI research laboratory that was founded in 2015. The first step is to learn the basics of machine learning and deep learning, which are the technologies that underpin generative AI. How to Get Started With Generative AI?

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Towards AI

AUGUST 11, 2023

Development in Deep Learning was at its golden state. A small startup named OpenAI got formed then after a year, in Dec 2015. Facebook then, on the other hand, was creating a system that could predict if two picture showed the same person.

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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. In the NLP domain, it has been a challenge to procure large amounts of data, to train a model, in order for the model to get proper context and embeddings of words.

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Ines Montani

AUGUST 6, 2024

Around 2015 when deep learning was widely adopted and conversational AI became more viable, the industry got very excited about chat bots. So whenever you’re tasked with developing a system to replace and automate a human task, ask yourself: Am I building a window-knocking machine or an alarm clock?

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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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