Towards AI

AUGUST 25, 2023

Deep learning (DL) is a subset of machine learning that uses neural networks which have a structure similar to the human neural system. There is often confusion between the terms artificial intelligence and machine learning, which is discussed in The AI Process. 12, 2014. [3] 3, IEEE, 2014. 16, 2020. [4]

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Viso.ai

JANUARY 2, 2024

In the following, we will explore Convolutional Neural Networks (CNNs), a key element in computer vision and image processing. Whether you’re a beginner or an experienced practitioner, this guide will provide insights into the mechanics of artificial neural networks and their applications. Howard et al.

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Viso.ai

OCTOBER 9, 2024

Emotion AI, also called Affective Computing, is a rapidly growing branch of Artificial Intelligence allowing computers to analyze and understand human language nonverbal signs such as facial expressions, body language, gestures, and voice tones to assess their emotional state.

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John Snow Labs

MAY 23, 2023

Visual question answering (VQA), an area that intersects the fields of Deep Learning, Natural Language Processing (NLP) and Computer Vision (CV) is garnering a lot of interest in research circles. A VQA system takes free-form, text-based questions about an input image and presents answers in a natural language format.

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Becoming Human

MARCH 16, 2023

Overhyped or not, investments in AI drug discovery jumped from $450 million in 2014 to a whopping $58 billion in 2021. All pharma giants, including Bayer, AstraZeneca, Takeda, Sanofi, Merck, and Pfizer, have stepped up spending in the hope to create new-age AI solutions that will bring cost efficiency, speed, and precision to the process.

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ODSC - Open Data Science

APRIL 28, 2023

If a Natural Language Processing (NLP) system does not have that context, we’d expect it not to get the joke. Deep learning refers to the use of neural network architectures, characterized by their multi-layer design (i.e. His major focus has been on Natural Language Processing (NLP) technology and applications.

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Pickl AI

NOVEMBER 27, 2024

Their applications span various fields, including natural language processing, time series forecasting, and speech recognition, making them a vital tool in modern AI. Introduction Recurrent Neural Networks (RNNs) are a cornerstone of Deep Learning. Introduced in 2014 by Cho et al.,

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Lexalytics

APRIL 5, 2021

This book effectively killed off interest in neural networks at that time, and Rosenblatt, who died shortly thereafter in a boating accident, was unable to defend his ideas. (I Around this time a new graduate student, Geoffrey Hinton, decided that he would study the now discredited field of neural networks.

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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).

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Mlearning.ai

APRIL 1, 2023

NLP A Comprehensive Guide to Word2Vec, Doc2Vec, and Top2Vec for Natural Language Processing In recent years, the field of natural language processing (NLP) has seen tremendous growth, and one of the most significant developments has been the advent of word embedding techniques.

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Pickl AI

SEPTEMBER 30, 2024

Introduction Generative Adversarial Networks (GANs) have emerged as one of the most exciting advancements in the field of Artificial Intelligence and Machine Learning since their introduction in 2014 by Ian Goodfellow and his collaborators. Discriminator : This network evaluates the data produced by the generator against real data.

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ML Review

MARCH 5, 2019

Conducting exploratory search is difficult in standard IR systems as terminology might differ even in closely related fields (network analyses vs graph neural networks). Crafting a dataset The number of papers added to ArXiv per month since 2014. How to find similar phrases without knowing what you’re searching for?

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Mlearning.ai

MARCH 9, 2023

Fully convolutional networks for semantic segmentation. Neural Networks, 64, 59–63. Intriguing properties of neural networks. Generative adversarial networks-based adversarial training for natural language processing. A simple guide for defending deep neural networks.

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Pickl AI

NOVEMBER 28, 2024

Understanding the Basics of GANs Generative Adversarial Networks (GANs) are a class of Machine Learning models introduced by Ian Goodfellow in 2014. At their core, GANs consist of two neural networks —a Generator and a Discriminator—that compete in a game-like scenario. How Generative Adversarial Networks (GANs) Work?

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Sebastian Ruder

AUGUST 18, 2019

Later approaches then scaled these representations to sentences and documents ( Le and Mikolov, 2014 ; Conneau et al., LM pretraining   Many successful pretraining approaches are based on variants of language modelling (LM). This goes back to layer-wise training of early deep neural networks ( Hinton et al.,

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Mlearning.ai

FEBRUARY 15, 2023

Introduction In natural language processing, text categorization tasks are common (NLP). Uysal and Gunal, 2014). The last Dense layer is the network’s output layer, it takes in a variable shape which can be either 4 or 3, denoting the number of classes for therapist and client. Dönicke, T., Manning C.

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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). Unsupervised Recurrent Neural Network Grammars Yoon Kim, Alexander Rush, Lei Yu, Adhiguna Kuncoro, Chris Dyer, Gábor Melis.

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ODSC - Open Data Science

JANUARY 29, 2024

GANs, introduced in 2014 paved the way for GenAI with models like Pix2pix and DiscoGAN. Generative AI Generative AI is another crucial skill for the role of prompt engineering, as it encompasses the core ability to leverage AI to create new content, whether it be text, images, or other forms of media.

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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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AWS Machine Learning Blog

FEBRUARY 29, 2024

In this post, we investigate of potential for the AWS Graviton3 processor to accelerate neural network training for ThirdAI’s unique CPU-based deep learning engine. He has won numerous paper awards, including Best Paper Awards at NIPS 2014 and MLSys 2022, as well as the Most Reproducible Paper Award at SIGMOD 2019.

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NLP People

JULY 24, 2019

Neural Networks are the workhorse of Deep Learning (cf. Convolutional Neural Networks have seen an increase in the past years, whereas the popularity of the traditional Recurrent Neural Network (RNN) is dropping. White (2014). Neural Network Methods in Natural Language Processing.

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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.

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ODSC - Open Data Science

FEBRUARY 29, 2024

The Stanford AI Lab Founded in 1963, the Stanford AI Lab has made significant contributions to various domains, including natural language processing, computer vision, and robotics. Similar to the MIT lab, its approach is multidisciplinary to enhance human-robot interactions using the power of AI.

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Flipboard

JANUARY 15, 2025

In particular, graph neural networks (GNNs) demonstrate an advantage over classical time series forecasting, due to their ability to capture structure information hidden in network topology and their capacity to generalize to unseen topologies when networks are dynamic.

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