Unite.AI

JULY 24, 2024

Over the past decade, advancements in machine learning, Natural Language Processing (NLP), and neural networks have transformed the field. In 2017, Apple introduced Core ML , a machine learning framework that allowed developers to integrate AI capabilities into their apps.

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Towards AI

FEBRUARY 20, 2024

Exploring the encoder-decoder magic in NLP behind LLMsImage created by the author. The state-of-the-art Natural Language Processing (NLP) models used to be Recurrent Neural Networks (RNN) among others. And then came Transformers. Developed by Vaswani et al. Developed by Vaswani et al.

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Towards AI

JUNE 6, 2023

unsplash Attention-based transformers have revolutionized the AI industry since 2017. Now it’s possible to have deep learning models with no limitation for the input size.

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Marktechpost

MAY 28, 2024

The Generative Pre-trained Transformer (GPT) series, developed by OpenAI, has revolutionized the field of NLP with its groundbreaking advancements in language generation and understanding. in 2017 , which relies on self-attention mechanisms to process input data in parallel, enhancing computational efficiency and scalability.

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Explosion

JANUARY 12, 2018

Declined 36 opportunities to “touch base” with investors and other professional networkers, who were confused by our radical we-spend-our-time-working approach. spaCy In 2017 spaCy grew into one of the most popular open-source libraries for Artificial Intelligence. spaCy’s Machine Learning library for NLP in Python.

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John Snow Labs

MAY 25, 2023

Rule-based sentiment analysis in Natural Language Processing (NLP) is a method of sentiment analysis that uses a set of manually-defined rules to identify and extract subjective information from text data. Using Spark NLP, it is possible to analyze the sentiment in a text with high accuracy.

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John Snow Labs

JUNE 5, 2023

Sentence detection in Spark NLP is the process of identifying and segmenting a piece of text into individual sentences using the Spark NLP library. Sentence Detection in Spark NLP is the process of automatically identifying the boundaries of sentences in a given text.

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Topbots

APRIL 11, 2023

BERT by Google Summary In 2018, the Google AI team introduced a new cutting-edge model for Natural Language Processing (NLP) – BERT , or B idirectional E ncoder R epresentations from T ransformers. This model marked a new era in NLP with pre-training of language models becoming a new standard. What is the goal? accuracy on SQuAD 1.1

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Towards AI

SEPTEMBER 7, 2023

How is attention computed using Recurrent Neural Networks (RNNs)? Machine Translation We will look at Neural machine translation (NMT) as a running example in this article. NMT aims to build and train a single, large neural network that reads a sentence and outputs a correct translation.

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Unite.AI

MARCH 21, 2024

In the world of natural language processing (NLP), the pursuit of building larger and more capable language models has been a driving force behind many recent advancements. in 1991, which proposed a supervised learning framework for an ensemble of neural networks, each specializing in a different region of the input space.

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Flipboard

MARCH 27, 2023

Project Structure Accelerating Convolutional Neural Networks Parsing Command Line Arguments and Running a Model Evaluating Convolutional Neural Networks Accelerating Vision Transformers Evaluating Vision Transformers Accelerating BERT Evaluating BERT Miscellaneous Summary Citation Information What’s New in PyTorch 2.0?

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John Snow Labs

MAY 26, 2023

Transformers, a type of neural network architecture, are a popular method for generating these embeddings. Transformers have revolutionized the field of NLP by enabling the creation of language models that can understand context and generate coherent text, and sentence embeddings are one of the many applications of this technology.

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AssemblyAI

MAY 3, 2023

These architectures are based on artificial neural networks , which are computational models loosely inspired by the structure and functioning of biological neural networks, such as those in the human brain. A simple artificial neural network consisting of three layers.

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Sebastian Ruder

JANUARY 6, 2020

This post gathers ten ML and NLP research directions that I found exciting and impactful in 2019.  Unsupervised pretraining was prevalent in NLP this year, mainly driven by BERT ( Devlin et al., 2019) find winning ticket initialisations also for LSTMs and Transformers in NLP and RL models. 2019 ) and other variants.

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Sebastian Ruder

AUGUST 18, 2019

This post expands on the NAACL 2019 tutorial on Transfer Learning in NLP. In the span of little more than a year, transfer learning in the form of pretrained language models has become ubiquitous in NLP and has contributed to the state of the art on a wide range of tasks. However, transfer learning is not a recent phenomenon in NLP.

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ODSC - Open Data Science

JUNE 9, 2023

In the 90’s we got grunge, statistical models, recurrent neural networks and long short-term memory models (LSTM). The last 12 years though, is where some of the big magic has happened in NLP. It uses a neural network to learn the vector representations of words from a large corpus of text.

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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.

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Mlearning.ai

JULY 2, 2023

In natural language processing (NLP), the “Attention is All You Need” paper introduced a groundbreaking neural network architecture called the Transformer. Recurrent neural networks (RNNs) struggle with parallel computation due to sequential processing, and convolutional networks have fixed context windows.

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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. ArXiv 2017.

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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.

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Towards AI

APRIL 8, 2024

While numerous techniques have been explored, methods harnessing natural language processing (NLP) have demonstrated strong performance. Word2Vec, a widely-adopted NLP algorithm has proven to be an efficient and valuable tool that is now applied across multiple domains, including recommendation systems.

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John Snow Labs

JUNE 6, 2023

Many different transformer models have already been implemented in Spark NLP, and specifically for text classification, Spark NLP provides various annotators that are designed to work with pretrained language models. The table shows the language models and the corresponding annotators for text classification provided by Spark NLP.

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AWS Machine Learning Blog

FEBRUARY 6, 2024

Transformers, BERT, and GPT The transformer architecture is a neural network architecture that is used for natural language processing (NLP) tasks. This allows the model to learn long-range dependencies between words, which is essential for many NLP tasks, such as machine translation and text summarization.

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Viso.ai

DECEMBER 10, 2023

The most popular machine learning method is deep learning, where multiple hidden layers of a neural network are used in a model. Neural networks need those training images from an acquired dataset to create perceptions of how certain classes look.

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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. Very deep networks may be downright chaotic. Coeff controls bias/variance tradeoff.

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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. without conventional neural networks.

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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).

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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.

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Sebastian Ruder

JANUARY 24, 2022

2021) 2021 saw many exciting advances in machine learning (ML) and natural language processing (NLP). If CNNs are pre-trained the same way as transformer models, they achieve competitive performance on many NLP tasks [28].   Popularized by GPT-3 [32] , prompting has emerged as a viable alternative input format for NLP models.

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Explosion

FEBRUARY 12, 2017

The question of how idealised NLP experiments should be is not new. A neural bag-of-words model for text-pair classification When designing a neural network for a text-pair task, probably the most important decision is whether you want to represent the meanings of the texts independently , or jointly.

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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. LSTMs and other recurrent neural networks RNNs are probably the most commonly used deep learning models for NLP and with good reason.

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Viso.ai

JUNE 12, 2024

The authors described how to improve language understanding performances in NLP by using GPT. GPT models are based on transformer-based deep learning neural network architecture. GPT models are based on transformer-based deep learning neural network architecture. without supervised pre-training. billion parameters.

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AWS Machine Learning Blog

NOVEMBER 9, 2023

See Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks , REALM , kNN-LM and RETRO. This leads to the same size and architecture as the original neural network. She received her PhD from Virginia Tech in 2017. He received his PhD from University of Illinois at Urbana-Champaign in 2017.

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Mlearning.ai

MARCH 2, 2023

Pre-training of Deep Bidirectional Transformers for Language Understanding BERT is a language model that can be fine-tuned for various NLP tasks and at the time of publication achieved several state-of-the-art results. Conclusion: BERT as Trend-Setter in NLP and Deep Learning References I.

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deepsense.ai

APRIL 16, 2023

Large language models (LLMs) are yielding remarkable results for many NLP tasks, but training them is challenging due to the demand for a lot of GPU memory and extended training time. 2017) is ensemble learning. This can make it difficult to train a deep neural network effectively.

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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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Viso.ai

JANUARY 16, 2024

Artificial Neural Networks (ANNs) are the cornerstone of modern artificial intelligence (AI). This diagram showcases how various layers interact in a neural network – source. ’ It uses natural language processing (NLP) for the descriptions, allowing the ANN to develop a semantic understanding.

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