Pickl AI

JULY 24, 2024

” During this time, researchers made remarkable strides in natural language processing, robotics, and expert systems. Notable achievements included the development of ELIZA, an early natural language processing program created by Joseph Weizenbaum, which simulated human conversation.

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AWS Machine Learning Blog

JUNE 6, 2023

PyTorch is a machine learning (ML) framework that is widely used by AWS customers for a variety of applications, such as computer vision, natural language processing, content creation, and more. These are basically big models based on deep learning techniques that are trained with hundreds of billions of parameters.

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IBM Journey to AI blog

OCTOBER 12, 2023

However, AI capabilities have been evolving steadily since the breakthrough development of artificial neural networks in 2012, which allow machines to engage in reinforcement learning and simulate how the human brain processes information.

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Artificial Intelligence Artificial Intelligence Machine Learning Machine Learning 83

Towards AI

JULY 21, 2023

Photo by Will Truettner on Unsplash NATURAL LANGUAGE PROCESSING (NLP) WEEKLY NEWSLETTER NLP News Cypher | 07.26.20 Last Updated on July 21, 2023 by Editorial Team Author(s): Ricky Costa Originally published on Towards AI. Primus The Liber Primus is unsolved to this day.

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Explosion

SEPTEMBER 3, 2015

Natural Language Processing moves fast, so maintaining a good library means constantly throwing things away. The new, awesome deep-learning model is there, but so are lots of others. But most Natural Language Processing libraries do, and it’s terrible. The new models supercede the old ones.

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Heartbeat

AUGUST 21, 2023

AlexNet is a more profound and complex CNN architecture developed by Alex Krizhevsky, Ilya Sutskever, and Geoffrey Hinton in 2012. AlexNet significantly improved performance over previous approaches and helped popularize deep learning and CNNs. It has eight layers, five of which are convolutional and three fully linked.

Natural Language Processing 52

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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., 2012; Otsu, 1979; Long et al., 2019) proposed a novel adversarial training framework for improving the robustness of deep learning-based segmentation models.

Deep Learning 52

Deep Learning Deep Learning Machine Learning Machine Learning 52

AWS Machine Learning Blog

SEPTEMBER 11, 2024

in 2012 is now widely referred to as ML’s “Cambrian Explosion.” Thirdly, the presence of GPUs enabled the labeled data to be processed. Together, these elements lead to the start of a period of dramatic progress in ML, with NN being redubbed deep learning. Work by Hinton et al.

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AWS Machine Learning Blog

OCTOBER 11, 2024

For example: Data such as images, text, and audio need to be represented in a structured and efficient manner Understanding the semantic similarity between data points is essential in generative AI tasks like natural language processing (NLP), image recognition, and recommendation systems As the volume of data continues to grow rapidly, scalability (..)

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AWS Machine Learning Blog

FEBRUARY 12, 2025

This concept is similar to knowledge distillation used in deep learning, except that were using the teacher model to generate a new dataset from its knowledge rather than directly modifying the architecture of the student model. The following diagram illustrates the overall flow of the solution. Yiyue holds a Ph.D.

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