IBM Journey to AI blog

AUGUST 1, 2023

A foundation model is built on a neural network model architecture to process information much like the human brain does. BERT (Bi-directional Encoder Representations from Transformers) is one of the earliest LLM foundation models developed. An open-source model, Google created BERT in 2018.

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Viso.ai

JULY 9, 2024

As the name suggests, this technique involves transferring the learnings of one trained machine learning model to another, in the form of neural network weights. But, there are open source models like German-BERT that are already trained on huge data corpora, with many parameters. Book a demo to learn more.

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Marktechpost

JULY 4, 2023

Natural language processing (NLP) activities, including speech-to-text, sentiment analysis, text summarization, spell-checking, token categorization, etc., Unigrams, N-grams, exponential, and neural networks are valid forms for the Language Model. rely on Language Models as their foundation.

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Large Language Models BERT Natural Language Processing Categorization 105

Viso.ai

JULY 18, 2024

Categorization of LLMs – Source One of the most common examples of an LLM is a virtual voice assistant such as Siri or Alexa. This early research was not about designing a system but exploring the fundamentals of Artificial Neural Networks. When you ask, “What is the weather today?”,

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Artificial Corner

JULY 30, 2023

Working of Large Language Models (LLMs) Deep neural networks are used in Large language models to produce results based on patterns discovered from training data. Machine translation, summarization, ticket categorization, and spell-checking are among the examples. T5 (Text-to-Text Transfer Transformer) — developed by Google.

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Explosion

AUGUST 1, 2019

Huge transformer models like BERT, GPT-2 and XLNet have set a new standard for accuracy on almost every NLP leaderboard. Deep neural networks have offered a solution, by building dense representations that transfer well between tasks. In this post we introduce our new wrapping library, spacy-transformers.

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Google Research AI blog

FEBRUARY 23, 2023

In the same way that BERT or GPT-3 models provide general-purpose initialization for NLP, large RL–pre-trained models could provide general-purpose initialization for decision-making. A few crucial design decisions made this possible: Neural network size: We found that multi-game Q-learning required large neural network architectures.

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AWS Machine Learning Blog

JANUARY 17, 2023

In addition to textual inputs, this model uses traditional structured data inputs such as numerical and categorical fields. We show you how to train, deploy and use a churn prediction model that has processed numerical, categorical, and textual features to make its prediction. BERT + Random Forest.

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Categorization BERT Machine Learning Neural Network 80

Snorkel AI

MAY 25, 2023

The potential of these enormous neural networks has both excited and frightened the public; the same technology that promises to help you digest long email chains also threatens to dethrone the essay as the default classroom assignment. All of this made it easy for researchers and practitioners to use BERT.

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Snorkel AI

MAY 25, 2023

The potential of these enormous neural networks has both excited and frightened the public; the same technology that promises to help you digest long email chains also threatens to dethrone the essay as the default classroom assignment. All of this made it easy for researchers and practitioners to use BERT.

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Large Language Models BERT Neural Network LLM 52

Unite.AI

AUGUST 28, 2023

Neural Networks & Deep Learning : Neural networks marked a turning point, mimicking human brain functions and evolving through experience. Systems like ChatGPT by OpenAI, BERT, and T5 have enabled breakthroughs in human-AI communication.

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ODSC - Open Data Science

APRIL 28, 2023

Be sure to check out his talk, “ Bagging to BERT — A Tour of Applied NLP ,” there! Deep learning refers to the use of neural network architectures, characterized by their multi-layer design (i.e. cats” component of Docs, for which we’ll be training a text categorization model to classify sentiment as “positive” or “negative.”

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Unite.AI

SEPTEMBER 18, 2023

Blockchain technology can be categorized primarily on the basis of the level of accessibility and control they offer, with Public, Private, and Federated being the three main types of blockchain technologies. The neural network consists of three types of layers including the hidden layer, the input payer, and the output layer.

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Heartbeat

JUNE 28, 2023

In this article, we will explore about ALBERT ( A lite weighted version of BERT machine learning model) What is ALBERT? ALBERT (A Lite BERT) is a language model developed by Google Research in 2019. BERT, GPT-2, and XLNet are some examples of models that can be used as teacher models for ALBERT.

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NVIDIA Developer

JUNE 2, 2021

Transformer-based models, such as Bidirectional Encoder Representations from Transformers (BERT), have revolutionized NLP by offering accuracy comparable to human baselines on benchmarks like SQuAD for question-answer, entity recognition, intent recognition, sentiment analysis, and more. Basic understanding of neural networks.

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Natural Language Processing Neural Network BERT Deep Learning 40

Heartbeat

FEBRUARY 8, 2024

Convolutional neural networks (CNNs) and recurrent neural networks (RNNs) are often employed to extract meaningful representations from images and text, respectively. Textual queries are transformed into embeddings using methods like word embeddings or recurrent neural networks.

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Neural Network Deep Learning Convolutional Neural Networks Computer Vision 52

Snorkel AI

MARCH 1, 2023

Model architectures that qualify as “supervised learning”—from traditional regression models to random forests to most neural networks—require labeled data for training. BERT proved useful in several ways, including quantifying sentiment and predicting the words likely to follow in unfinished sentences.

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Topbots

APRIL 11, 2023

We’ll start with a seminal BERT model from 2018 and finish with this year’s latest breakthroughs like LLaMA by Meta AI and GPT-4 by OpenAI. 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.

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NLP BERT Large Language Models Natural Language Processing 98

Viso.ai

DECEMBER 18, 2023

TensorFlow is an open-source software library for AI and machine learning with deep neural networks. TensorFlow Lite also optimizes the trained model using quantization techniques (discussed later in this article), which consequently reduces the necessary memory usage as well as the computational cost of utilizing neural networks.

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AWS Machine Learning Blog

APRIL 25, 2024

It uses BERT, a popular NLP technique, to understand the meaning and context of words in the candidate summary and reference summary. The more similar the words and meanings captured by BERT, the higher the BERTScore. It uses neural networks like BERT to measure semantic similarity beyond just exact word or phrase matching.

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Viso.ai

MARCH 26, 2024

Uniquely, this model did not rely on conventional neural network architectures like convolutional or recurrent layers. without conventional neural networks. Source ) This has led to groundbreaking models like GPT for generative tasks and BERT for understanding context in Natural Language Processing ( NLP ).

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Deep Learning Computer Vision Neural Network Natural Language Processing 52

ODSC - Open Data Science

JUNE 6, 2023

Using Embeddings to Detect Anomalies Figure 1: Using a trained deep neural network, it is possible to convert unstructured data to numeric representations, i.e., embeddings Embeddings are numerical representations generated from unstructured data like images, text, and audio, and greatly influence machine learning approaches for handling such data.

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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., Image processing : Predictive image processing models, such as convolutional neural networks (CNNs), can classify images into predefined labels (e.g.,

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Generative AI Natural Language Processing Convolutional Neural Networks Machine Learning 113

Viso.ai

DECEMBER 22, 2023

This leap forward is due to the influence of foundation models in NLP, such as GPT and BERT. The Segment Anything Model Technical Backbone: Convolutional, Generative Networks, and More Convolutional Neural Networks (CNNs) and Generative Adversarial Networks (GANs) play a foundational role in the capabilities of SAM.

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Convolutional Neural Networks Computer Vision Neural Network Auto-classification 52

Mlearning.ai

JULY 23, 2023

Text Classification : Categorizing text into predefined categories based on its content. It is used to automatically detect and categorize posts or comments into various groups such as ‘offensive’, ‘non-offensive’, ‘spam’, ‘promotional’, and others. It’s ‘trained’ on labeled data and then used to categorize new, unseen data.

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NVIDIA Developer

MAY 20, 2021

Deep learning is a powerful AI approach that uses multi-layered artificial neural networks to deliver state-of-the-art accuracy in tasks such as object detection, speech recognition, and language translation. Basic understanding of neural networks.

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Natural Language Processing Deep Learning Neural Network NLP 40

AWS Machine Learning Blog

JUNE 9, 2023

This allows GuardDuty to categorize previously unseen domains as highly likely to be malicious or benign based on their association to known malicious domains. By using Graph Neural Networks (GNNs), GuardDuty is able to enhance its capability to alert customers.

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Probably Approximately a Scientific Blog

JANUARY 12, 2021

Types of commonsense: Commonsense knowledge can be categorized according to types, including but not limited to: Social commonsense: people are capable of making inferences about other people's mental states, e.g. what motivates them, what they are likely to do next, etc. In the last 3 years, language models have been ubiquitous in NLP.

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AWS Machine Learning Blog

SEPTEMBER 11, 2024

In this example figure, features are extracted from raw historical data, which are then are fed into a neural network (NN). Parallel computing Parallel computing refers to carrying out multiple processes simultaneously, and can be categorized according to the granularity at which parallelism is supported by the hardware.

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Topbots

JULY 6, 2023

LaMDA is built on Transformer , a neural network architecture that Google Research invented and open-sourced in 2017. Like other large language models, including BERT and GPT-3, LaMDA is trained on terabytes of text data to learn how words relate to one another and then predict what words are likely to come next.

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LLM Large Language Models Natural Language Processing OpenAI 52

Explosion

MARCH 17, 2019

Clearly, we couldn’t use a model such as BERT or GPT-2 directly. Stepping back a little, the problem of so-called “one hot” representations posing representational issues for neural networks is actually quite familiar. It helps most for text categorization and parsing, but is less effective for named entity recognition.

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NLP Python Neural Network Natural Language Processing 52

Unite.AI

AUGUST 4, 2023

LangChain categorizes its chains into three types: Utility chains, Generic chains, and Combine Documents chains. Hugging Face Hugging Face is a FREE-TO-USE Transformers Python library, compatible with PyTorch, TensorFlow, and JAX, and includes implementations of models like BERT , T5 , etc.

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Prompt Engineer Prompt Engineering Python NLP 245

Unite.AI

AUGUST 8, 2023

The Technologies Behind Generative Models Generative models owe their existence to deep neural networks, sophisticated structures designed to mimic the human brain's functionality. By capturing and processing multifaceted variations in data, these networks serve as the backbone of numerous generative models.

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AWS Machine Learning Blog

AUGUST 7, 2023

LLMs are neural networks that have been trained using a massive number of parameters, typically in the order of billions, using unlabeled data. Due to the sequential nature of text, recurrent neural networks (RNNs) had been the state of the art for NLP modeling. Thus the basis of the transformer model was born.

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Bugra Akyildiz

MARCH 24, 2024

The first two can be categorized as inductive bias of humans and the last one is introducing compute over human element; which provides the following advantages: Unbiased Exploration: Evolutionary algorithms can systematically explore a vast space of potential model combinations, significantly exceeding human capabilities.

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