Heartbeat

JUNE 6, 2023

Photo by GuerrillaBuzz on Unsplash Graph Convolutional Networks (GCNs) are a type of neural network that operates on graphs, which are mathematical structures consisting of nodes and edges. GCNs have been successfully applied to many domains, including computer vision and social network analysis.

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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. Author(s): Dimitris Effrosynidis Originally published on Towards AI. And then came Transformers.

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Analytics Vidhya

DECEMBER 28, 2021

This article was published as a part of the Data Science Blogathon Objective In this blog, we will learn how to Fine-tune a Pre-trained BERT model for the Sentiment analysis task.

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Towards AI

MAY 11, 2023

Table of Contents: Mastering Large Language Models (LLMs) is a compelling endeavor in the realm of Natural Language Processing (NLP). The journey begins with understanding NLP basics, covering essential concepts, techniques, and algorithms. Join thousands of data leaders on the AI newsletter.

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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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Heartbeat

MARCH 15, 2023

But now, a computer can be taught to comprehend and process human language through Natural Language Processing (NLP), which was implemented, to make computers capable of understanding spoken and written language. How RoBERTa Works It works by pre-training a deep neural network on a large corpus of text.

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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. We will discuss each of these architectures in detail.

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Natural Language Processing @ Sapienza

FEBRUARY 6, 2024

Welcome to the Sapienza NLP course blog 2024! Cool things about to happen: The course will contain lots of up-to-date content on deep learning , neural networks , Large Language Model, and an improved hands-on with PyTorch ! We will include the most recent additions (including from 2024) from the world of NLP!

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Towards AI

JUNE 6, 2023

Since then, we have seen significant progress in all aspects, including Computer vision, NLP, … Attentions are considered a more powerful and capable version of Neural Networks for generalization on big datasets and are nothing more than routing between keys (K) and queries (Q), then non-linearity (Softmax), and then values (V).

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Pickl AI

APRIL 25, 2023

The following blog hence will contain detailed information on the 5 Important Subsets of AI (Artificial Intelligence). There are five different subsets of Artificial Intelligence which include Machine Learning, Deep Learning, Robotics, Neural Networks, and NLP. What is Neural Network? What is NLP?

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Mlearning.ai

MAY 5, 2023

In the 1980s and 1990s, the field of natural language processing (NLP) began to emerge as a distinct area of research within AI. NLP researchers focused on developing statistical models that could process and generate text based on patterns and probabilities, rather than strict rules. I think GPT-3 is as intelligent as a human.

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Becoming Human

MAY 15, 2023

When it comes to AI, there are a number of subfields, like Natural Language Processing (NLP). One of the models used for NLP is the Large Language Model (LLMs). As a result, LLMs have become a key tool for a wide range of NLP applications. This data comes everywhere, including articles, blogs, news sites, and journals.

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IBM Journey to AI blog

FEBRUARY 14, 2024

Voice-based queries use natural language processing (NLP) and sentiment analysis for speech recognition so their conversations can begin immediately. With text to speech and NLP, AI can respond immediately to texted queries and instructions. Humanize HR AI can attract, develop and retain a skills-first workforce.

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Towards AI

OCTOBER 8, 2023

This is exactly what this blog post is intended for. We’ll start from neural networks, brush up on deep learning, figure out what the heck is NLP, and eventually, after the not-so-painstaking process, learn about the working of large language models. What are neural networks? Fret no more!

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NYU Center for Data Science

DECEMBER 7, 2022

The NLP Community Survey , which asks many of these questions and more, was conducted from May to June 2022. The survey covers over thirty potentially controversial positions on NLP, tackling concerns often raised about AGI (artificial general intelligence) and ethics. What Do NLP Researchers Believe? Kind of like humans.

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Unite.AI

DECEMBER 15, 2023

Often foregoing the usual accompaniments of papers, blogs, or press releases, their strategy has been uniquely effective in capturing the AI community's attention. The Concept of MoE Mixture of Experts (MoE) represents a paradigm shift in neural network architecture.

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Pickl AI

APRIL 19, 2024

This blog delves into the essence of learning AI from scratch, unravelling its significance in today’s dynamic landscape of innovation and opportunity. Key Takeaways AI encompasses machine learning, neural networks, NLP, and robotics. Learning AI from scratch is more than staying relevant and shaping the future.

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IBM Journey to AI blog

SEPTEMBER 19, 2023

Generative AI for coding is possible because of recent breakthroughs in large language model (LLM) technologies and natural language processing (NLP). It uses deep learning algorithms and large neural networks trained on vast datasets of diverse existing source code.

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Towards AI

MARCH 20, 2023

How do neural networks contribute to generative AI? How does natural language processing (NLP) relate to generative AI? The breakthrough moment for generative AI came with the introduction of Generative Adversarial Networks (GANs) in 2014 by Ian Goodfellow and his team. How do neural networks contribute to generative AI?

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NLP People

APRIL 10, 2019

It’s Institute of Computational Linguistics , which includes the Phonetics Laboratory , lead by Martin Volk and Volker Dellwo, as well as the URPP Language and Space perform research in NLP topics, such as machine translation, sentiment analysis, speech recognition and dialect detection. University of St. Gallen The University of St.

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ODSC - Open Data Science

FEBRUARY 13, 2023

These videos are a part of the ODSC/Microsoft AI learning journe y which includes videos, blogs, webinars, and more. How Deep Neural Networks Work and How We Put Them to Work at Facebook Deep learning is the technology driving today’s artificial intelligence boom.

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Pickl AI

JUNE 5, 2023

In this blog, we have hand-picked some unconventional yet outstanding Deep learning applications examples. Deep Learning is a subset of Machine Learning where neural networks have a significant role. The neural networks are trained on sample data, and the insights are used to replicate applications on new datasets.

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NLP People

JUNE 5, 2018

Are you looking to study or work in the field of NLP? For this series, NLP People will be taking a closer look at the NLP education & development landscape in different parts of the world, including the best sites for job-seekers and where you can go for the leading NLP-related education programs on offer.

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Heartbeat

MARCH 22, 2023

Learn NLP data processing operations with NLTK, visualize data with Kangas , build a spam classifier, and track it with Comet Machine Learning Platform Photo by Stephen Phillips — Hostreviews.co.uk on Unsplash At its core, the discipline of Natural Language Processing (NLP) tries to make the human language “palatable” to computers.

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IBM Journey to AI blog

JULY 6, 2023

While artificial intelligence (AI), machine learning (ML), deep learning and neural networks are related technologies, the terms are often used interchangeably, which frequently leads to confusion about their differences. This blog post will clarify some of the ambiguity. What is a neural network?

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Heartbeat

JUNE 23, 2023

However, with the advent of deep learning, researchers have explored various neural network architectures to model and forecast time series data. Recurrent neural networks (RNNs), are a significant deep learning approach used in time series analysis .

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Natural Language Processing @ Sapienza

FEBRUARY 5, 2023

Welcome to the Sapienza NLP course blog 2023! Cool things about to happen: The course will contain lots of up-to-date content on deep learning , neural networks , and an improved hands-on with PyTorch and PyTorch Lightning ! Please sign up to the NLP class ! The course is held at DIAG !

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IBM Journey to AI blog

APRIL 18, 2024

Achieving these feats is accomplished through a combination of sophisticated algorithms, natural language processing (NLP) and computer science principles. NLP techniques help them parse the nuances of human language, including grammar, syntax and context.

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Mlearning.ai

FEBRUARY 28, 2023

Read the full blog here —  [link] Technology Stack HTML, CSS for frontend Flask(Python) for backend Introduction to Flask Flask is a web application framework written in Python. The model uses a convolutional neural network (CNN) to extract features from the images and classify them as healthy or diseased.

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IBM Journey to AI blog

JUNE 14, 2023

With a foundation model, often using a kind of neural network called a “transformer” and leveraging a technique called self-supervised learning, you can create pre-trained models for a vast amount of unlabeled data. But that’s all changing thanks to pre-trained, open source foundation models.

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IBM Journey to AI blog

OCTOBER 16, 2023

Voice-based queries use Natural Language Processing (NLP) and sentiment analysis for speech recognition. This communication can involve speech recognition, speech-to-text conversion, NLP, or text-to-speech. Google Translate uses NLP to translate words across more than 130 languages.

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IBM Journey to AI blog

DECEMBER 15, 2023

Keras, an open-source neural network library written in Python, is known for its user-friendliness and modularity, allowing for easy and fast prototyping of deep learning models. earn more about how to train, validate, tune and deploy AI models The post Five open-source AI tools to know appeared first on IBM Blog.

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Pragnakalp

MAY 1, 2024

In this blog, we’ll look at some of the top open-source models that empower you to turn static videos and images into speaking videos. Cons : Needs a powerful computer (GPU) to run due to complex neural network architecture. So, without delay, let’s jump in! Lip Sync Models 1. It requires only a video file as input.

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Mlearning.ai

MARCH 26, 2023

This blog post aims to provide you with a step-by-step guide to kickstart your data science journey, with a focus on learning through comprehensive projects and hands-on experiences. Learn the basics of neural networks for image datasets and how convolutional neural networks (CNNs) work their magic in image classification tasks.

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Allen AI

FEBRUARY 1, 2024

It’s the equivalent of drug discovery without clinical trials or studying the solar system without a telescope,” said Hanna Hajishirzi , OLMo project lead, a senior director of NLP Research at AI2, and a professor in the UW’s Allen School.

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Topbots

OCTOBER 4, 2023

Here are a few examples across various domains: Natural Language Processing (NLP) : Predictive NLP models can categorize text into predefined classes (e.g., spam vs. not spam), while generative NLP models can create new text based on a given prompt (e.g., a social media post or product description).

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NVIDIA

NOVEMBER 15, 2023

Under the hood, LLMs are neural networks, typically measured by how many parameters they contain. A blog by Lewis and three of the paper’s coauthors said developers can implement the process with as few as five lines of code. In other words, it fills a gap in how LLMs work.

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