Mlearning.ai

APRIL 8, 2023

Charting the evolution of SOTA (State-of-the-art) techniques in NLP (Natural Language Processing) over the years, highlighting the key algorithms, influential figures, and groundbreaking papers that have shaped the field. Evolution of NLP Models To understand the full impact of the above evolutionary process.

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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. The core idea behind MoE is to have multiple “expert” networks, each responsible for processing a subset of the input data.

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Large Language Models Neural Network Natural Language Processing LLM 147

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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Explainability Neural Network Natural Language Processing NLP 121

AssemblyAI

MAY 3, 2023

The core process is a general technique known as self-supervised learning , a learning paradigm that leverages the inherent structure of the data itself to generate labels for training. This concept is not exclusive to natural language processing, and has also been employed in other domains.

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Large Language Models Neural Network LLM ChatGPT 363

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. Packt, ISBN: 978–1787125933, 2017. Klein, and E.

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Unite.AI

NOVEMBER 8, 2023

Natural Language Processing (NLP) has experienced some of the most impactful breakthroughs in recent years, primarily due to the the transformer architecture. Recurrent Neural Networks (RNNs) became the cornerstone for these applications due to their ability to handle sequential data by maintaining a form of memory.

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Towards AI

JULY 20, 2023

Natural Language Processing Getting desirable data out of published reports and clinical trials and into systematic literature reviews (SLRs) — a process known as data extraction — is just one of a series of incredibly time-consuming, repetitive, and potentially error-prone steps involved in creating SLRs and meta-analyses.

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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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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. Since then, in the realms of AI and machine learning, transformer models have emerged as a groundbreaking approach to various language-related tasks.

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Neural Network Natural Language Processing Convolutional Neural Networks NLP 52

Towards AI

APRIL 8, 2024

While numerous techniques have been explored, methods harnessing natural language processing (NLP) have demonstrated strong performance. Understanding Word2Vec Word2Vec is a pioneering natural language processing (NLP) technique that revolutionized the way we represent words in vector space.

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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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Deep Learning Computer Vision Neural Network Natural Language Processing 52

Unite.AI

OCTOBER 27, 2023

For example, multimodal generative models of neural networks can produce such images, literary and scientific texts that it is not always possible to distinguish whether they are created by a human or an artificial intelligence system. Stiglitz, will lead to the transformation of civilization (Stiglitz, 2017).

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Viso.ai

JULY 9, 2024

Neural Networks For now, most attempts to develop ASI are still grounded in well-known models, such as neural networks , machine learning/deep learning , and computational neuroscience. Estimates for when we would achieve AGI range from the next 5 to 30 years.

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Neural Network Convolutional Neural Networks Computer Vision Deep Learning 52

Mlearning.ai

JULY 2, 2023

Let’s explore the Transformer model that does that precise magic, the encoder-decoder structure that powers its translation prowess, and witness the magic unfold as the model generates rich representations, transforming language from one form to another. The feed-forward network adds non-linearity and capacity to the model.

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Neural Network NLP Natural Language Processing Large Language Models 52

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. The Process of Image Recognition Systems There are a few steps that are at the backbone of how image recognition systems work.

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Convolutional Neural Networks Computer Vision Neural Network Deep Learning 98

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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Convolutional Neural Networks Computer Vision Neural Network Natural Language Processing 52

AWS Machine Learning Blog

NOVEMBER 9, 2023

This leads to the same size and architecture as the original neural network. Her research interests lie in Natural Language Processing, AI4Code and generative AI. She received her PhD from Virginia Tech in 2017. He received his PhD from University of Illinois at Urbana-Champaign in 2017.

Natural Language Processing 101

Natural Language Processing NLP Large Language Models LLM 101

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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Neural Network Convolutional Neural Networks Natural Language Processing BERT 52

Explosion

FEBRUARY 12, 2017

The Quora dataset is an example of an important type of Natural Language Processing problem: text-pair classification. An independent representation means that the network can read a text in isolation, and produce a vector representation for it. Most NLP neural networks start with an embedding layer.

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Topbots

APRIL 11, 2023

In the rapidly evolving field of artificial intelligence, natural language processing has become a focal point for researchers and developers alike. The Most Important Large Language Models (LLMs) in 2023 1. Its design allowed the model to consider the context from both the left and the right sides of each word.

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Viso.ai

JUNE 12, 2024

GPT models are based on transformer-based deep learning neural network architecture. Their applications include various Natural Language Processing ( NLP ) tasks, including question answering, text summarization, sentiment analysis , etc. All three GPT generations utilize artificial neural networks.

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Large Language Models ChatGPT Computer Vision Neural Network 52

Chatbots Life

MARCH 17, 2023

Timeline by Antoine Louis on A Brief History of Natural Language Processing Siri, Google Assistant, Cortana, and Alexa, are the successive technologies rolled out in the 20th century. Transformers differ from LSTMs in that they can process all input data at the same time. (B)

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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.

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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.

Machine Learning 52

Machine Learning Neural Network Convolutional Neural Networks Deep Learning 52

Viso.ai

JANUARY 16, 2024

Artificial Neural Networks (ANNs) are the cornerstone of modern artificial intelligence (AI). They mimic the human brain’s structure, with layers of nodes or “neurons” to process data non-linearly. However, ANNs can also process other forms of content, like music , by analyzing relevant patterns.

Neural Network 52

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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. Encryption at rest and in transit has to be incorporated into the solution to meet these requirements.

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Applied Data Science

JANUARY 4, 2022

Transformers taking the AI world by storm The family of artificial neural networks (ANNs) saw a new member being born in 2017, the Transformer. Initially introduced for Natural Language Processing (NLP) applications like translation, this type of network was used in both Google’s BERT and OpenAI’s GPT-2 and GPT-3.

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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: Lu et al.

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Neural Network Convolutional Neural Networks Computer Vision Deep Learning 52

John Snow Labs

JUNE 6, 2023

Sequence classification with transformers is a powerful technique in the field of natural language processing (NLP) that involves predicting a label or category for a sequence of data. This sequence can be made up of text, as well as other types of data like audio, images, or time series.

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deepsense.ai

JULY 13, 2023

Today, everyone is talking about LLMs, especially ChatGPT and its breakthrough capabilities, but the field of language modeling is definitely not new, and ChatGPT is not the first model of its kind. Since then, the world of natural language processing has evolved into a multitude of similar yet different approaches.

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Large Language Models LLM ChatGPT Neural Network 98

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.

Machine Learning 52

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Sebastian Ruder

AUGUST 18, 2019

Throughout its history, most of the major improvements on this task have been driven by different forms of transfer learning: from early self-supervised learning with auxiliary tasks ( Ando and Zhang, 2005 ) and phrase & word clusters ( Lin and Wu, 2009 ) to the language model embeddings ( Peters et al., 2017 ; Peters et al.,

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Flipboard

FEBRUARY 5, 2023

First, to ensure expertise, we employ natural-language-processing tools to assist our content developers in auditing and improving our 100-odd courses in more than 40 different languages. Duolingo uses machine learning and other cutting-edge technologies to mimic these three qualities of a good tutor.

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John Snow Labs

JUNE 5, 2023

Sentence detection is an essential component in many natural language processing (NLP) tasks, as it enables the analysis of text at a more granular level by breaking it down into individual sentences. Sentence Detection in Spark NLP is the process of automatically identifying the boundaries of sentences in a given text.

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John Snow Labs

MAY 26, 2023

Sentence embeddings are a powerful tool in natural language processing that helps analyze and understand language. Transformers, a type of neural network architecture, are a popular method for generating these embeddings. " sentence_df = spark.createDataFrame([[sentence_0], [sentence_1], [sentence_2]]).toDF("text")

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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. Sentiment analysis is an automated process capable of understanding the feelings or opinions that underlie a text.

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Heartbeat

AUGUST 2, 2023

The foundation of many cutting-edge language models stems from the transformer architecture, first introduced in the influential paper “Attention Is All You Need” by Vaswani et al. However, to derive the context of the language, only the encoder component is typically used.

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