Explosion

AUGUST 22, 2017

#ML — Jason Eisner (@adveisner) August 12, 2017 E.g., regularize toward word embeddings θ that were pretrained on big data for some other objective. — Jason Eisner (@adveisner) August 12, 2017 I have this in the book btw (p. Coeff controls bias/variance tradeoff.

NLP 52

NLP Convolutional Neural Networks Neural Network Natural Language Processing 52

Sebastian Ruder

JANUARY 19, 2021

The selection of areas and methods is heavily influenced by my own interests; the selected topics are biased towards representation and transfer learning and towards natural language processing (NLP). This is less of a problem in NLP where unsupervised pre-training involves classification over thousands of word types.

NLP 52

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John Snow Labs

JUNE 2, 2023

Spark NLP’s deep learning models have achieved state-of-the-art results on sentiment analysis tasks, thanks to their ability to automatically learn features and representations from raw text data. Spark NLP has multiple approaches for detecting the sentiment (which is actually a text classification problem) in a text.

Deep Learning 76

Deep Learning NLP Convolutional Neural Networks Neural Network 76

Viso.ai

FEBRUARY 25, 2023

Vision Transformer (ViT) have recently emerged as a competitive alternative to Convolutional Neural Networks (CNNs) that are currently state-of-the-art in different image recognition computer vision tasks. Transformer models have become the de-facto status quo in Natural Language Processing (NLP).

Convolutional Neural Networks 52

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Marek Rei

NOVEMBER 12, 2019

Below you will find short summaries of a number of different research papers published in the areas of Machine Learning and Natural Language Processing in the past couple of years (2017-2019). Robust Incremental Neural Semantic Graph Parsing Jan Buys, Phil Blunsom. link] The paper describes a neural semantic graph parser.

Machine Learning 52

Machine Learning NLP Neural Network BERT 52

DataRobot Blog

MARCH 9, 2022

This is one of the reasons why detecting sentiment from natural language (NLP or natural language processing ) is a surprisingly complex task. The idea is that instead of performing convolutions on image pixels, the model can instead perform those convolutions in the embedded feature space of the words in a sentence.

Machine Learning 52

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Snorkel AI

MARCH 1, 2023

Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks Radford et al. 2016) This paper introduced DCGANs, a type of generative model that uses convolutional neural networks to generate images with high fidelity. Attention Is All You Need Vaswani et al.

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Pragnakalp

JANUARY 1, 2024

Transformer models are a type of neural network architecture designed to process sequential material, such as sentences or time-series data. They’re used widely in neural machine translation (NMT). They’re used to perform or improve AI and NLP business tasks, as well as streamline enterprise workflows.

Neural Network 52

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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). 2017) [ 96 ]. 2017) [ 99 ]. Source: Lu et al. Source : You et al.

Neural Network 52

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Viso.ai

MARCH 26, 2024

This enhances the interpretability of AI systems for applications in computer vision and natural language processing (NLP). described this model in the seminal paper titled “Attention is All You Need” in 2017. without conventional neural networks. Vaswani et al.

Deep Learning 52

Deep Learning Computer Vision Neural Network Natural Language Processing 52

Lexalytics

APRIL 5, 2021

I was out of the neural net biz. Fast-forward a couple of decades: I was (and still am) working at Lexalytics, a text-analytics company that has a comprehensive NLP stack developed over many years. Since the MAG database petered out around 2017, I filled out the rest of the timeline with topics I knew were important.

Neural Network 52

Neural Network Convolutional Neural Networks Natural Language Processing BERT 52

Explosion

AUGUST 3, 2017

We’ve been working on Prodigy since we first launched Explosion last year, alongside our open-source NLP library spaCy and our consulting projects (it’s been a busy year!). — Andrew Ng (@AndrewYNg) July 7, 2017 Unsupervised algorithms return meaning representations, based on the internal structure of the data.

Python 52

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Mlearning.ai

JUNE 6, 2023

We have the IPL data from 2008 to 2017. Emotion Detector using Keras In this blog, we will be building an Emotion Detector model in Keras using Convolutional Neural Networks. Cats and Dogs Classifier In this blog, we will be building a Cats and Dogs Classifier using Convolutional Neural Networks.

Machine Learning 52

Machine Learning Python Computer Vision Convolutional Neural Networks 52

Marek Rei

JANUARY 17, 2018

Thousands of new papers are published every year at the main ML and NLP conferences , not to mention all the specialised workshops and everything that shows up on ArXiv. So at the end of the list you will also find brief summaries of the papers I published in 2017. ArXiv 2017. Let’s get started. Google Brain, DeepMind.

Machine Learning 40

Machine Learning NLP Neural Network Convolutional Neural Networks 40

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!

Neural Network 52

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Unite.AI

FEBRUARY 8, 2025

In 2017, a significant change reshaped Artificial Intelligence (AI). Transformers in Diverse Applications Beyond NLP The adaptability of transformers has extended their use well beyond natural language processing. A paper titled Attention Is All You Need introduced transformers.

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NLP People

JULY 24, 2019

We took the opportunity to review major research trends in the animated NLP space and formulate some implications from the business perspective. The article is backed by a statistical and – guess what – NLP-based analysis of ACL papers from the last 20 years. Neural Networks are the workhorse of Deep Learning (cf.

NLP 52

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Viso.ai

DECEMBER 21, 2023

From the development of sophisticated object detection algorithms to the rise of convolutional neural networks (CNNs) for image classification to innovations in facial recognition technology, applications of computer vision are transforming entire industries. Thus, positioning him as one of the top AI influencers in the world.

Convolutional Neural Networks 52

Convolutional Neural Networks Neural Network Computer Vision Deep Learning 52

John Snow Labs

MAY 8, 2024

in 2017 highlighted this by demonstrating a deep learning algorithm’s ability to classify skin cancer with accuracy comparable to that of human dermatologists, based on an extensive dataset of 129,450 clinical images. A notable study by Esteva et al.

Large Language Models 40

Large Language Models Automation NLP Machine Learning 40