Unite.AI

MARCH 24, 2024

Central to this advancement in NLP is the development of artificial neural networks, which draw inspiration from the biological neurons in the human brain. These networks emulate the way human neurons transmit electrical signals, processing information through interconnected nodes.

Neural Network 354

Neural Network Robotics Natural Language Processing NLP 354

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

Towards AI

JANUARY 29, 2025

Generative AI is powered by advanced machine learning techniques, particularly deep learning and neural networks, such as Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs). GPT, BERT) Image Generation (e.g., Study neural networks, including CNNs, RNNs, and LSTMs.

AI Engineer 98

AI Engineer Generative AI Neural Network BERT 98

Marktechpost

AUGUST 4, 2024

Representational similarity measures are essential tools in machine learning, used to compare internal representations of neural networks. These measures help researchers understand learning dynamics, model behaviors, and performance by providing insights into how different neural network layers and architectures process information.

Neural Network 117

Neural Network BERT Machine Learning Artificial Intelligence 117

Flipboard

DECEMBER 26, 2024

Almost thirty years later, upon Wirths passing in January 2024, lifelong technologist Bert Hubert revisited Wirths plea and despaired at how catastrophically worse the state of software bloat has become. After all, the more code you write, the more opportunities you have to introduce a mistake or a security vulnerabilities.

Software Engineer 177

Software Engineer BERT Artificial Intelligence Artificial Intelligence 177

Marktechpost

JANUARY 20, 2025

introduced the concept of Generative Adversarial Networks (GANs) , where two neural networks, i.e., the generator and the discriminator, are trained simultaneously. Notably, BERT (Bidirectional Encoder Representations from Transformers), introduced by Devlin et al. Ian Goodfellow et al.

Generative AI 114

Generative AI Neural Network AI AI 114

Towards AI

JUNE 12, 2024

Photo by david clarke on Unsplash The most recent breakthroughs in language models have been the use of neural network architectures to represent text. Both BERT and GPT are based on the Transformer architecture. 2013 Word2Vec is a neural network model that uses n-grams by training on context windows of words.

BERT 104

BERT Neural Network Natural Language Processing NLP 104

Unite.AI

JULY 23, 2024

These patterns are then decoded using deep neural networks to reconstruct the perceived images. The encoder translates visual stimuli into corresponding brain activity patterns through convolutional neural networks (CNNs) that mimic the human visual cortex's hierarchical processing stages.

Convolutional Neural Networks 317

Convolutional Neural Networks Neural Network Artificial Intelligence Artificial Intelligence 317

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 181

Neural Network BERT Machine Learning 181

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 246

Neural Network Convolutional Neural Networks Natural Language Processing Robotics 246

Unite.AI

MAY 29, 2024

This enhances speed and contributes to the extraction process's overall performance. Adapting to Varied Data Types While some models like Recurrent Neural Networks (RNNs) are limited to specific sequences, LLMs handle non-sequence-specific data, accommodating varied sentence structures effortlessly.

Data Extraction 273

Data Extraction Neural Network Large Language Models NLP 273

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

Chatbots Neural Network BERT OpenAI 204

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 290

Large Language Models Neural Network Artificial Intelligence Artificial Intelligence 290

AWS Machine Learning Blog

JANUARY 19, 2024

In this post, we demonstrate how to use neural architecture search (NAS) based structural pruning to compress a fine-tuned BERT model to improve model performance and reduce inference times. First, we use an Amazon SageMaker Studio notebook to fine-tune a pre-trained BERT model on a target task using a domain-specific dataset.

BERT 126

BERT Automation Neural Network Machine Learning 126

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

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?

Neural Network 158

Neural Network Convolutional Neural Networks BERT Deep Learning 158

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.

Large Language Models 363

Large Language Models Neural Network LLM ChatGPT 363

Towards AI

MARCH 12, 2025

Normalization Trade-off: GPT models preserve formatting & nuance (more token complexity); BERT aggressively cleans text simpler tokens, reduced nuance, ideal for structured tasks. GPT typically preserves contractions, BERT-based models may split. Tokens: Fundamental unit that neural networks process. GPT-4 and GPT-3.5

LLM 54

LLM BERT Neural Network Metadata 54

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

Pickl AI

AUGUST 14, 2024

Summary: Neural networks are a key technique in Machine Learning, inspired by the human brain. Different types of neural networks, such as feedforward, convolutional, and recurrent networks, are designed for specific tasks like image recognition, Natural Language Processing, and sequence modelling.

Neural Network 52

Neural Network Machine Learning Convolutional Neural Networks Natural Language Processing 52

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

Marktechpost

DECEMBER 12, 2024

A Deep Neural Network (DNN) is an artificial neural network that features multiple layers of interconnected nodes, also known as neurons. The deep aspect of DNNs comes from multiple hidden layers, which allow the network to learn and model complex patterns and relationships in data.

Neural Network 56

Neural Network Convolutional Neural Networks Natural Language Processing Artificial Intelligence 56

Towards AI

JUNE 3, 2024

BERT is a state-of-the-art algorithm designed by Google to process text data and convert it into vectors ([link]. What makes BERT special is, apart from its good results, the fact that it is trained over billions of records and that Hugging Face provides already a good battery of pre-trained models we can use for different ML tasks.

BERT 84

BERT Neural Network Algorithm Data Analysis 84

Unite.AI

DECEMBER 13, 2024

Large language models (LLMs) , such as GPT-4 , BERT , Llama , etc., Technologies such as Recurrent Neural Networks (RNNs) and transformers introduced the ability to process sequences of data and paved the way for more adaptive AI. Artificial intelligence (AI) fundamentally transforms how we live, work, and communicate.

LLM 246

LLM Neural Network Chatbots AI 246

Pickl AI

NOVEMBER 26, 2024

Summary: Recurrent Neural Networks (RNNs) are specialised neural networks designed for processing sequential data by maintaining memory of previous inputs. Introduction Neural networks have revolutionised data processing by mimicking the human brain’s ability to recognise patterns.

Neural Network 52

Neural Network Natural Language Processing NLP Deep Learning 52

AssemblyAI

JUNE 27, 2023

Deep Neural Networks (DNNs) have proven to be exceptionally adept at processing highly complicated modalities like these, so it is unsurprising that they have revolutionized the way we approach audio data modeling. At its core, it's an end-to-end neural network-based approach. The EnCodec architecture ( source ).

Generative AI 298

Generative AI BERT Neural Network AI 298

Unite.AI

AUGUST 1, 2023

Prompt 1 : “Tell me about Convolutional Neural Networks.” ” Response 1 : “Convolutional Neural Networks (CNNs) are multi-layer perceptron networks that consist of fully connected layers and pooling layers. They are commonly used in image recognition tasks. .”

Prompt Engineering 246

Prompt Engineering Prompt Engineer ChatGPT Convolutional Neural Networks 246

AWS Machine Learning Blog

AUGUST 2, 2024

We also released a comprehensive study of co-training language models (LM) and graph neural networks (GNN) for large graphs with rich text features using the Microsoft Academic Graph (MAG) dataset from our KDD 2024 paper. GraphStorm provides different ways to fine-tune the BERT models, depending on the task types. Dataset Num.

BERT 117

BERT Neural Network Machine Learning ML 117

ODSC - Open Data Science

MARCH 10, 2025

By 2017, deep learning began to make waves, driven by breakthroughs in neural networks and the release of frameworks like TensorFlow. Sessions on convolutional neural networks (CNNs) and recurrent neural networks (RNNs) started gaining popularity, marking the beginning of data sciences shift toward AI-driven methods.

Data Science 52

Data Science Neural Network Convolutional Neural Networks Large Language Models 52

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

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.

Large Language Models 217

Large Language Models BERT Neural Network Natural Language Processing 217

Unite.AI

MAY 24, 2024

In modern machine learning and artificial intelligence frameworks, transformers are one of the most widely used components across various domains including GPT series, and BERT in Natural Language Processing, and Vision Transformers in computer vision tasks.

Convolutional Neural Networks 130

Convolutional Neural Networks Neural Network BERT Machine Learning 130

Marktechpost

MAY 30, 2024

Google plays a crucial role in advancing AI by developing cutting-edge technologies and tools like TensorFlow, Vertex AI, and BERT. It covers how to develop NLP projects using neural networks with Vertex AI and TensorFlow.

Artificial Intelligence 130

Artificial Intelligence Artificial Intelligence BERT Computer Vision 130

Pickl AI

JANUARY 26, 2025

These models mimic the human brain’s neural networks, making them highly effective for image recognition, natural language processing, and predictive analytics. Feedforward Neural Networks (FNNs) Feedforward Neural Networks (FNNs) are the simplest and most foundational architecture in Deep Learning.

Deep Learning 52

Deep Learning Neural Network Convolutional Neural Networks Natural Language Processing 52

NVIDIA

FEBRUARY 11, 2025

They said transformer models , large language models (LLMs), vision language models (VLMs) and other neural networks still being built are part of an important new category they dubbed foundation models. Earlier neural networks were narrowly tuned for specific tasks.

Neural Network 55

Neural Network Large Language Models Robotics BERT 55

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

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. Finally, the impact of the paper and applications of BERT are evaluated from today’s perspective. 1 Architecture III.2

BERT 52

BERT NLP Deep Learning Neural Network 52