Lexalytics

APRIL 5, 2021

This subjective impression is objectively backed up by the heat map below, constructed from a dump of the Microsoft Academic Graph (MAG) circa 2017 [ 21 ]. Since the MAG database petered out around 2017, I filled out the rest of the timeline with topics I knew were important. In this case, it was more like “shut up and optimize”.

Neural Network 52

Neural Network Convolutional Neural Networks Natural Language Processing 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. Some common datasets include the SemEval 2007 Task 14 , EmoBank , WASSA 2017 , The Emotion in Text Dataset , and the Affect Dataset.

Machine Learning 52

Machine Learning Neural Network Convolutional Neural Networks Deep Learning 52

Snorkel AI

MARCH 1, 2023

This process results in generalized models capable of a wide variety of tasks, such as image classification, natural language processing, and question-answering, with remarkable accuracy. Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks Radford et al.

BERT 83

BERT Natural Language Processing Large Language Models Neural Network 83

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. Summary It’s common to use pre-trained models for computer vision and natural language processing.

NLP 52

NLP Convolutional Neural Networks Neural Network Natural Language Processing 52

John Snow Labs

JUNE 2, 2023

Sentiment analysis is a popular natural language processing (NLP) task that involves determining the sentiment of a given text, whether it is positive, negative, or neutral. These models can capture more complex patterns in the data and may perform better on more nuanced tasks such as sarcasm detection or emotion recognition.

Deep Learning 98

Deep Learning NLP Convolutional Neural Networks Neural Network 98

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 ].

Neural Network 52

Neural Network Convolutional Neural Networks Computer Vision Deep Learning 52

AWS Machine Learning Blog

FEBRUARY 10, 2023

Advances in neural information processing systems 30 (2017). Advances in neural information processing systems 32 (2019). Grad-cam: Visual explanations from deep networks via gradient-based localization.” He is broadly interested in Deep Learning and Natural Language Processing.

Computer Vision 80

Computer Vision Machine Learning ML Convolutional Neural Networks 80

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

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). 2020 ) employed a CNN to compute image features, later models were completely convolution-free.

NLP 52

NLP ML Computer Vision Neural Network 52

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. ArXiv 2017. Google, Toronto.

Machine Learning 52

Machine Learning NLP Neural Network BERT 52

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). 2017) present their WLAS Network.

Neural Network 52

Neural Network Computer Vision Deep Learning Convolutional Neural Networks 52

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

NLP People

JULY 24, 2019

Neural Networks are the workhorse of Deep Learning (cf. Goldberg and Hirst (2017) for an introduction of the basic architectures in the NLP context). Convolutional Neural Networks have seen an increase in the past years, whereas the popularity of the traditional Recurrent Neural Network (RNN) is dropping.

NLP 52

NLP Neural Network Deep Learning Natural Language Processing 52

John Snow Labs

MAY 8, 2024

The Impact of Data and Training Methodologies The effectiveness of Large Language Models (LLMs) in pathology hinges on the depth and breadth of datasets used for their training, which encompass a wide array of medical texts, pathology reports, and histopathological imagery. A notable study by Esteva et al.

Large Language Models 40

Large Language Models Automation NLP Machine Learning 40