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.

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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.

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Unite.AI

DECEMBER 8, 2023

These systems, typically deep learning models, are pre-trained on extensive labeled data, incorporating neural networks for self-attention. This article introduces UltraFastBERT, a BERT-based framework matching the efficacy of leading BERT models but using just 0.3%

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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.

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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.

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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.

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Marktechpost

JUNE 27, 2023

Activation functions for neural networks are an essential part of deep learning since they decide the accuracy and efficiency of the training model used to create or split a large-scale neural network and the output of deep learning models. An artificial neural network contains a large number of linked individual neurons.

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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

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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.

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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 …

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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.

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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.

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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?

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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.

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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

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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

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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.

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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.

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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.

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Convolutional Neural Networks Neural Network Robotics Natural Language Processing 211

John Snow Labs

JUNE 6, 2023

Transformers are defined as a specific type of neural network architecture that have proven to be particularly effective for sequence classification tasks, thanks to their ability to capture long-term dependencies and contextual relationships in the data. The transformer architecture was introduced by Vaswani et al.

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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.

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Convolutional Neural Networks Neural Network BERT Natural Language Processing 130

Unite.AI

OCTOBER 22, 2024

AI models like neural networks , used in applications like Natural Language Processing (NLP) and computer vision , are notorious for their high computational demands. Models like GPT and BERT involve millions to billions of parameters, leading to significant processing time and energy consumption during training and inference.

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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.

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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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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.

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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

SEPTEMBER 30, 2024

While the Adam optimizer has become the standard for training Transformers, stochastic gradient descent with momentum (SGD), which is highly effective for convolutional neural networks (CNNs), performs worse on Transformer models. This performance gap poses a challenge for researchers. Check out the Paper.

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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.

Convolutional Neural Networks 124

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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.

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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 111

Machine Learning BERT Neural Network Continuous Learning 111

Mlearning.ai

FEBRUARY 15, 2023

transformer.ipynb” uses the BERT architecture to classify the behaviour type for a conversation uttered by therapist and client, i.e, The fourth model which is also used for multi-class classification is built using the famous BERT architecture. The architecture of BERT is represented in Figure 14.

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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.

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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.

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Marktechpost

NOVEMBER 23, 2024

MCCE is multi-capable in determining the individual sample’s effects and generalizing the thought process of neural networks while making a rule. They performed multiclass semantic classification by fine-tuning data on three open Large models – base BERT, base RoBERTa, and Llama-3.

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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.

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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.

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