Unite.AI

APRIL 30, 2024

It includes deciphering neural network layers , feature extraction methods, and decision-making pathways. These systems rely heavily on neural networks to process vast amounts of information. During training, neural networks learn patterns from extensive datasets.

Chatbots 204

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Marktechpost

MAY 5, 2024

By leveraging a new data-dependent convolution layer, Orchid dynamically adjusts its kernel based on the input data using a conditioning neural network, allowing it to handle sequence lengths up to 131K efficiently. Compared to the BERT-base, the Orchid-BERT-base has 30% fewer parameters yet achieves a 1.0-point

Deep Learning 132

Deep Learning BERT Neural Network Natural Language Processing 132

Flipboard

NOVEMBER 24, 2023

Researchers at ETH Zurich have developed a new technique that can significantly boost the speed of neural networks. They’ve demonstrated that altering the inference process can drastically cut down the computational requirements of these networks. In experiments conducted on BERT, a transformer …

Neural Network 180

Neural Network BERT Machine Learning 180

Mlearning.ai

APRIL 21, 2023

Natural Language Processing Transformers, the neural network architecture, that has taken the world of natural language processing (NLP) by storm, is a class of models that can be used for both language and image processing. One of the earliest representation models used in NLP was the Bag of Words (BoW) model.

Neural Network 52

Neural Network Natural Language Processing ChatGPT NLP 52

Unite.AI

SEPTEMBER 24, 2023

When the models were pitted against each other, the ones based on transformer neural networks exhibited superior performance compared to the simpler recurrent neural network models and statistical models. The models were then evaluated based on whether their assessments resonated with human choices.

AI Chatbots 354

AI Chatbots Chatbots Neural Network BERT 354

Unite.AI

NOVEMBER 6, 2023

Neural Network: Moving from Machine Learning to Deep Learning & Beyond Neural network (NN) models are far more complicated than traditional Machine Learning models. Advances in neural network techniques have formed the basis for transitioning from machine learning to deep learning.

Machine Learning 204

Machine Learning Neural Network Deep Learning Convolutional Neural Networks 204

Heartbeat

MARCH 15, 2023

An open-source machine learning model called BERT was developed by Google in 2018 for NLP, but this model had some limitations, and due to this, a modified BERT model called RoBERTa (Robustly Optimized BERT Pre-Training Approach) was developed by the team at Facebook in the year 2019. What is RoBERTa?

BERT 52

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Marktechpost

APRIL 21, 2024

Transformer Models and BERT Model : In this course, participants delve into the specifics of Transformer models and the Bidirectional Encoder Representations from Transformers (BERT) model. This is crucial for ensuring AI technology is used in a way that is ethical and beneficial to society.

Artificial Intelligence 137

Artificial Intelligence Artificial Intelligence BERT Large Language Models 137

AI Weekly

JULY 27, 2023

techcrunch.com The Essential Artificial Intelligence Glossary for Marketers (90+ Terms) BERT - Bidirectional Encoder Representations from Transformers (BERT) is Google’s deep learning model designed explicitly for natural language processing tasks like answering questions, analyzing sentiment, and translation.

Neural Network 204

Neural Network Robotics Natural Language Processing Convolutional Neural Networks 204

Marktechpost

MARCH 25, 2024

Large Language Models (LLMs) like ChatGPT, Google’s Bert, Gemini, Claude Models, and others have emerged as central figures, redefining our interaction with digital interfaces. These models use deep learning techniques, particularly neural networks, to process and produce text that mimics human-like understanding and responses.

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Marktechpost

MARCH 27, 2024

These adapters allow BERT to be fine-tuned for specific downstream tasks while retaining most of its pre-trained parameters. These adapters allow BERT to be fine-tuned for specific downstream tasks while retaining most of its pre-trained parameters.

Machine Learning 107

Machine Learning BERT Neural Network Natural Language Processing 107

Analytics Vidhya

JUNE 5, 2023

These gargantuan neural networks have revolutionized how machines learn and generate human language, propelling the boundaries of what was once thought possible.

Large Language Models 283

Large Language Models Neural Network Artificial Intelligence Artificial Intelligence 283

Marktechpost

APRIL 6, 2024

Transformers have transformed the field of NLP over the last few years, with LLMs like OpenAI’s GPT series, BERT, and Claude Series, etc. in 2017, marking a departure from the previous reliance on recurrent neural networks (RNNs) and convolutional neural networks (CNNs) for processing sequential data.

Large Language Models 120

Large Language Models NLP Convolutional Neural Networks Neural Network 120

Marktechpost

NOVEMBER 15, 2023

This model consists of two primary modules: A pre-trained BERT model is employed to extract pertinent information from the input text, and A diffusion UNet model processes the output from BERT. It is built upon a pre-trained BERT model. The BERT model takes subword input, and its output is processed by a 1D U-Net structure.

BERT 120

BERT Convolutional Neural Networks Neural Network Machine Learning 120

Marktechpost

APRIL 25, 2024

Parameter In the context of neural networks, including LLMs, a parameter is a variable part of the model’s architecture learned from the training data. Parameters (like weights in neural networks) are adjusted during training to reduce the difference between predicted and actual output.

Large Language Models 128

Large Language Models LLM Neural Network Natural Language Processing 128

The MLOps Blog

JANUARY 25, 2023

One of the ML-based approaches that have gained quite a lot of light over the past few years is transformer-based models like BERT. BERT can comprehend the context of a given text, making it a good candidate for sentiment analysis. BERT can comprehend the context of a given text, making it a good candidate for sentiment analysis.

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Marktechpost

NOVEMBER 14, 2023

The research presents a study on simplifying transformer blocks in deep neural networks, specifically focusing on the standard transformer block. The study examines the simplification of transformer blocks in deep neural networks, focusing specifically on the standard transformer block.

Neural Network 122

Neural Network Deep Learning BERT AI 122

Unite.AI

FEBRUARY 7, 2024

LLMs are deep neural networks that can generate natural language texts for various purposes, such as answering questions, summarizing documents, or writing code. LLMs, such as GPT-4 , BERT , and T5 , are very powerful and versatile in Natural Language Processing (NLP).

Machine Learning 294

Machine Learning Large Language Models LLM BERT 294

Mlearning.ai

JUNE 14, 2023

LLMs (Foundational Models) 101: Introduction to Transformer Models Transformers, explained: Understand the model behind GPT, BERT, and T5 — YouTube Illustrated Guide to Transformers Neural Network: A step by step explanation — YouTube Attention Mechanism Deep dive. Transformer Neural Networks — EXPLAINED!

Neural Network 52

Neural Network Large Language Models BERT Natural Language Processing 52

John Snow Labs

JUNE 27, 2023

At their core, LLMs are built upon deep neural networks, enabling them to process vast amounts of text and learn complex patterns. In this section, we will provide an overview of two widely recognized LLMs, BERT and GPT, and introduce other notable models like T5, Pythia, Dolly, Bloom, Falcon, StarCoder, Orca, LLAMA, and Vicuna.

Large Language Models 52

Large Language Models BERT Natural Language Processing NLP 52

Towards AI

JULY 21, 2023

Blog: Faster and smaller quantized NLP with Hugging Face and ONNX Runtime Popular Hugging Face Transformer models (BERT, GPT-2, etc) can be shrunk and accelerated with ONNX Runtime quantization… medium.com Talking about ONNX… Suraj Patil created this awesome repo that incorporates the ONNX script with the Hugging Face pipeline framework.

NLP 87

NLP Neural Network BERT Natural Language Processing 87

Unite.AI

DECEMBER 4, 2023

GPT 3 and similar Large Language Models (LLM) , such as BERT , famous for its bidirectional context understanding, T-5 with its text-to-text approach, and XLNet , which combines autoregressive and autoencoding models, have all played pivotal roles in transforming the Natural Language Processing (NLP) paradigm.

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Large Language Models BERT Neural Network Natural Language Processing 217

Marktechpost

DECEMBER 5, 2023

Small Language Models, which are compact generative AI models, are distinguished by their small neural network size, number of parameters, and volume of training data. Examples of Small Language Models DistilBERT is a quicker, more compact version of BERT that transforms NLP by preserving performance without sacrificing efficiency.

BERT 132

BERT Large Language Models Neural Network Natural Language Processing 132

Mlearning.ai

APRIL 8, 2023

Over the years, we evolved that to solving NLP use cases by adopting Neural Network-based algorithms loosely based on the structure and function of a human brain. The birth of Neural networks was initiated with an approach akin to structuring solving problems with algorithms modeled after the human brain.

NLP 98

NLP Neural Network Natural Language Processing Convolutional Neural Networks 98

Marktechpost

MAY 5, 2024

While earlier surveys predominantly centred on encoder-based models such as BERT, the emergence of decoder-only Transformers spurred advancements in analyzing these potent generative models. They explore methods to decode information in neural network models, especially in natural language processing.

Natural Language Processing 111

Natural Language Processing Categorization Neural Network NLP 111

Viso.ai

MARCH 20, 2024

Models like GPT 4, BERT, DALL-E 3, CLIP, Sora, etc., Use Cases for Foundation Models Applications in Pre-trained Language Models like GPT, BERT, Claude, etc. Foundation models are large-scale neural network architectures that undergo pre-training on vast amounts of unlabeled data through self-supervised learning.

AI Developer 52

AI Developer AI Development Computer Vision BERT 52

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. BERT is trained on sequences where some of the words in a sentence are masked, and it has to fill in those words taking into account both the words before and after the masked words.

Large Language Models 104

Large Language Models BERT NLP Data Scientist 104

Marktechpost

DECEMBER 13, 2023

The well-known Large Language Models (LLMs) like GPT, BERT, PaLM, and LLaMA have brought in some great advancements in Natural Language Processing (NLP) and Natural Language Generation (NLG). Three types of graph-based applications, i.e., pure graphs, text-rich graphs, and text-paired graphs, have been associated with the integration of LLMs.

Large Language Models 126

Large Language Models AI Researcher AI Research Neural Network 126

Marktechpost

JULY 25, 2023

Researchers at Google and Osaka University use deep neural networks to generate music from features like fMRI scans by predicting high-level, semantically structured music. The music-generating model MusicLM consists of audio-derived embeddings named MuLan and w2v-BERT- avg.

BERT 97

BERT Neural Network AI AI 97

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.

BERT 52

BERT NLP Neural Network Categorization 52

ODSC - Open Data Science

FEBRUARY 17, 2023

TensorFlow is desired for its flexibility for ML and neural networks, PyTorch for its ease of use and innate design for NLP, and scikit-learn for classification and clustering. BERT is still very popular over the past few years and even though the last update from Google was in late 2019 it is still widely deployed.

NLP 111

NLP Deep Learning BERT Natural Language Processing 111

ODSC - Open Data Science

JUNE 9, 2023

The 1970s introduced bell bottoms, case grammars, semantic networks, and conceptual dependency theory. In the 90’s we got grunge, statistical models, recurrent neural networks and long short-term memory models (LSTM). It uses a neural network to learn the vector representations of words from a large corpus of text.

Natural Language Processing 52

Natural Language Processing Generative AI ChatGPT BERT 52

Google Research AI blog

FEBRUARY 7, 2023

Context-augmented models In the quest for higher quality and efficiency, neural models can be augmented with external context from large databases or trainable memory. The basic idea of MoEs is to construct a network from a number of expert sub-networks, where each input is processed by a suitable subset of experts.

Deep Learning 118

Deep Learning Algorithm Neural Network ML 118

Towards AI

JULY 19, 2023

Blog: Faster and smaller quantized NLP with Hugging Face and ONNX Runtime Popular Hugging Face Transformer models (BERT, GPT-2, etc) can be shrunk and accelerated with ONNX Runtime quantization… medium.com Talking about ONNX… Suraj Patil created this awesome repo that incorporates the ONNX script with the Hugging Face pipeline framework.

NLP 40

NLP Neural Network BERT Natural Language Processing 40

Mlearning.ai

MARCH 30, 2023

To train a machine learning model or a neural network that can yield the best results requires what? How can we train a neural network without having an ample amount of data, even if you have it can you afford to train a model for months? Then Neural Network in CNN is just for the prediction part.

Neural Network 52

Neural Network AI Developer AI Development Convolutional Neural Networks 52

Unite.AI

JANUARY 23, 2024

Techniques like Word2Vec and BERT create embedding models which can be reused. Word2Vec pioneered the use of shallow neural networks to learn embeddings by predicting neighboring words. BERT produces deep contextual embeddings by masking words and predicting them based on bidirectional context.

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Large Language Models Prompt Engineer Prompt Engineering LLM 130

Marktechpost

JULY 4, 2023

Unigrams, N-grams, exponential, and neural networks are valid forms for the Language Model. By optimizing the probability over all possible orders of factorization, its autoregressive formulation surpasses BERT’s restrictions, allowing for acquiring knowledge in both directions. rely on Language Models as their foundation.

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Large Language Models BERT Natural Language Processing Categorization 103