Towards AI

JULY 23, 2023

In the first part of the series, we talked about how Transformer ended the sequence-to-sequence modeling era of Natural Language Processing and understanding. 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.

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Heartbeat

APRIL 23, 2023

One of the key components of chatbot development is natural language processing (NLP), which allows the bot to understand and respond to human language. SpaCy is a popular open-source NLP library developed in 2015 by Matthew Honnibal and Ines Montani, the founders of the software company Explosion.

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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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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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NYU Center for Data Science

SEPTEMBER 28, 2023

It’s a pivotal time in Natural Language Processing (NLP) research, marked by the emergence of large language models (LLMs) that are reshaping what it means to work with human language technologies. Cho’s work on building attention mechanisms within deep learning models has been seminal in the field.

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ML Review

FEBRUARY 26, 2018

Launched in July 2015, AliMe is an IHCI-based shopping guide and assistant for e-commerce that overhauls traditional services, and improves the online user experience. Chitchatting, such as “I’m in a bad mood”, pulls up a method that marries the retrieval model with deep learning (DL). 5] Mnih V, Badia A P, Mirza M, et al.

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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 significantly improved performance over previous approaches and helped popularize deep learning and CNNs. ResNet is a deep CNN architecture developed by Kaiming He and his colleagues at Microsoft Research in 2015. It consists of 16 layers, all of which are convolutional or fully connected layers.

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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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AWS Machine Learning Blog

APRIL 18, 2023

Large language models (LLMs) with billions of parameters are currently at the forefront of natural language processing (NLP). These models are shaking up the field with their incredible abilities to generate text, analyze sentiment, translate languages, and much more.

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ML Review

JUNE 4, 2018

Recent Intersections Between Computer Vision and Natural Language Processing (Part Two) This is the second instalment of our latest publication series looking at some of the intersections between Computer Vision (CV) and Natural Language Processing (NLP). Source : Johnson et al. using Faster-RCNN[ 82 ].

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IBM Journey to AI blog

AUGUST 11, 2023

MLOps is the next evolution of data analysis and deep learning. Simply put, MLOps uses machine learning to make machine learning more efficient. Origins of the MLOps process MLOps was born out of the realization that ML lifecycle management was slow and difficult to scale for business application.

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Explosion

FEBRUARY 12, 2017

In this post, I’ll explain how to solve text-pair tasks with deep learning, using both new and established tips and technologies. The Quora dataset is an example of an important type of Natural Language Processing problem: text-pair classification. This data set is large, real, and relevant — a rare combination.

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AWS Machine Learning Blog

APRIL 18, 2023

Large language models (LLMs) with billions of parameters are currently at the forefront of natural language processing (NLP). These models are shaking up the field with their incredible abilities to generate text, analyze sentiment, translate languages, and much more.

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Snorkel AI

FEBRUARY 13, 2023

The voice remote was launched for Comcast in 2015. You can really think about intent as a translation of (as I mentioned) the lexical query, using NLP [natural language processing] technologies to understand the semantics of the query, which is the intent, and then translating it into a business action.

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Snorkel AI

FEBRUARY 13, 2023

The voice remote was launched for Comcast in 2015. You can really think about intent as a translation of (as I mentioned) the lexical query, using NLP [natural language processing] technologies to understand the semantics of the query, which is the intent, and then translating it into a business action.

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ML Review

MAY 3, 2018

Recent Intersections Between Computer Vision and Natural Language Processing (Part One) This is the first instalment of our latest publication series looking at some of the intersections between Computer Vision (CV) and Natural Language Processing (NLP). Thanks for reading!

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Google Research AI blog

MARCH 7, 2023

books, magazines, newspapers, forms, street signs, restaurant menus) so that they can be indexed, searched, translated, and further processed by state-of-the-art natural language processing techniques. words per image on average, which is more than 3x the density of TextOCR and 25x more dense than ICDAR-2015.

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Snorkel AI

MAY 24, 2023

From generative modeling to automated product tagging, cloud computing, predictive analytics, and deep learning, the speakers present a diverse range of expertise. chief data scientist, a role he held under President Barack Obama from 2015 to 2017. He has over 30 publications and more than 20 patents in machine learning and NLP.

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Snorkel AI

MAY 24, 2023

From generative modeling to automated product tagging, cloud computing, predictive analytics, and deep learning, the speakers present a diverse range of expertise. chief data scientist, a role he held under President Barack Obama from 2015 to 2017. He has over 30 publications and more than 20 patents in machine learning and NLP.

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