Marktechpost

AUGUST 7, 2023

Models like GPT, BERT, and PaLM are getting popular for all the good reasons. The well-known model BERT, which stands for Bidirectional Encoder Representations from Transformers, has a number of amazing applications. Recent research investigates the potential of BERT for text summarization.

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Heartbeat

SEPTEMBER 11, 2023

The following is a brief tutorial on how BERT and Transformers work in NLP-based analysis using the Masked Language Model (MLM). Introduction In this tutorial, we will provide a little background on the BERT model and how it works. The BERT model was pre-trained using text from Wikipedia. What is BERT? How Does BERT Work?

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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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AWS Machine Learning Blog

NOVEMBER 29, 2023

In today’s rapidly evolving landscape of artificial intelligence, deep learning models have found themselves at the forefront of innovation, with applications spanning computer vision (CV), natural language processing (NLP), and recommendation systems. If not, refer to Using the SageMaker Python SDK before continuing.

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Analytics Vidhya

APRIL 16, 2024

Introduction Welcome into the world of Transformers, the deep learning model that has transformed Natural Language Processing (NLP) since its debut in 2017. These linguistic marvels, armed with self-attention mechanisms, revolutionize how machines understand language, from translating texts to analyzing sentiments.

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Google Research AI blog

FEBRUARY 7, 2023

The explosion in deep learning a decade ago was catapulted in part by the convergence of new algorithms and architectures, a marked increase in data, and access to greater compute. Using this approach, for the first time, we were able to effectively train BERT using simple SGD without the need for adaptivity.

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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.

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Towards AI

JUNE 4, 2023

BERT Transformer Source: Image created by the author + Stable Diffusion (All Rights Reserved) In the context of machine learning and NLP, a transformer is a deep learning model introduced in a paper titled “Attention is All You Need” by Vaswani et al.

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The MLOps Blog

JANUARY 25, 2023

Then using Machine Learning and Deep Learning sentiment analysis techniques, these businesses analyze if a customer feels positive or negative about their product so that they can make appropriate business decisions to improve their business.

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Towards AI

JUNE 5, 2023

In Part 1 (fine-tuning a BERT model), I explained what a transformer model is and the various open source models types that are available from Hugging Face’s free transformers library. We also walked through how to fine-tune a BERT model to conduct sentiment analysis. In Part… Read the full blog for free on Medium.

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John Snow Labs

JUNE 27, 2023

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. BERT excels in understanding context and generating contextually relevant representations for a given text.

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Unite.AI

MAY 8, 2024

Graph Neural Networks (GNNs) have emerged as a powerful deep learning framework for graph machine learning tasks. E-BERT aligns KG entity vectors with BERT's wordpiece embeddings, while K-BERT constructs trees containing the original sentence and relevant KG triples.

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Analytics Vidhya

MARCH 13, 2023

Introduction Large Language Models (LLMs) are foundational machine learning models that use deep learning algorithms to process and understand natural language. These models are trained on massive amounts of text data to learn patterns and entity relationships in the language.

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AWS Machine Learning Blog

NOVEMBER 22, 2023

Amazon Elastic Compute Cloud (Amazon EC2) DL2q instances, powered by Qualcomm AI 100 Standard accelerators, can be used to cost-efficiently deploy deep learning (DL) workloads in the cloud. To learn more about tuning the performance of a model, see the Cloud AI 100 Key Performance Parameters Documentation. Roy from Qualcomm AI.

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Flipboard

JUNE 20, 2023

We present the results of recent performance and power draw experiments conducted by AWS that quantify the energy efficiency benefits you can expect when migrating your deep learning workloads from other inference- and training-optimized accelerated Amazon Elastic Compute Cloud (Amazon EC2) instances to AWS Inferentia and AWS Trainium.

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Marktechpost

DECEMBER 18, 2023

Bioformer Bioformer is a compact version of BERT that can be used for biomedical text mining. Although BERT has achieved state-of-the-art performance in NLP applications, its parameters could be reduced with a minor impact on performance to improve its computational efficiency.

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NYU Center for Data Science

OCTOBER 10, 2023

The paper is a case study of syntax acquisition in BERT (Bidirectional Encoder Representations from Transformers). An MLM, BERT gained significant attention around 2018–2019 and is now often used as a base model fine-tuned for various tasks, such as classification.

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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

DECEMBER 5, 2023

Models like OpenAI’s ChatGPT and Google Bard require enormous volumes of resources, including a lot of training data, substantial amounts of storage, intricate, deep learning frameworks, and enormous amounts of electricity. What are Small Language Models? million parameters to Medium with 41 million.

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ODSC - Open Data Science

FEBRUARY 17, 2023

Developing NLP tools isn’t so straightforward, and requires a lot of background knowledge in machine & deep learning, among others. Machine & Deep Learning Machine learning is the fundamental data science skillset, and deep learning is the foundation for NLP.

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Artificial Corner

JULY 30, 2023

Photo by Shubham Dhage on Unsplash Introduction Large language Models (LLMs) are a subset of Deep Learning. Some Terminologies related to Artificial Intelligence (Ai) Deep Learning is a technique used in artificial intelligence (AI) that teaches computers to interpret data in a manner modeled after the human brain.

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Unite.AI

JULY 11, 2023

Introduction To Image Generation Image Source Course difficulty: Beginner-level Completion time: ~ 1 day (Complete the quiz/lab in your own time) Prerequisites: Knowledge of ML, Deep Learning (DL), Convolutional Neural Nets (CNNs), and Python programming. What will AI enthusiasts learn? Learn how it operates and its uses.

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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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Marktechpost

NOVEMBER 14, 2023

The motivation for simplification arises from the complexity of modern neural network architectures and the gap between theory and practice in deep learning. The study conducted experiments on autoregressive decoder-only and BERT encoder-only models to assess the performance of the simplified transformers.

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Towards AI

DECEMBER 28, 2023

The advent of more powerful personal computers paved the way for the gradual acceptance of deep learning-based methods. The introduction of attention mechanisms has notably altered our approach to working with deep learning algorithms, leading to a revolution in the realms of computer vision and natural language processing (NLP).

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Unite.AI

DECEMBER 27, 2023

Another breakthrough is the rise of generative language models powered by deep learning algorithms. Facebook's RoBERTa, built on the BERT architecture, utilizes deep learning algorithms to generate text based on given prompts. trillion parameters, making it one of the largest language models ever created.

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AWS Machine Learning Blog

MARCH 20, 2023

In the following example figure, we show INT8 inference performance in C6i for a BERT-base model. The BERT-base was fine-tuned with SQuAD v1.1, In the context of deep learning, the predominant numerical format used for research and deployment has so far been 32-bit floating point, or FP32.

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Unite.AI

SEPTEMBER 25, 2023

However, as technology advanced, so did the complexity and capabilities of AI music generators, paving the way for deep learning and Natural Language Processing (NLP) to play pivotal roles in this tech. Today platforms like Spotify are leveraging AI to fine-tune their users' listening experiences.

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IBM Journey to AI blog

MARCH 27, 2024

Figure 1: Framework for estimating Scope3 emissions using large language models We conducted extensive experiments involving several cutting-edge LLMs including roberta-base, bert-base-uncased, and distilroberta-base-climate-f. Additionally, we explored non-foundation classical models based on TF-IDF and Word2Vec vectorization approaches.

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Lexalytics

JULY 12, 2023

Given the significant progress made in deep learning and the emergence of powerful language models such as BERT and GPT in recent years, our aim is to continue helping businesses in leveraging these advancements across diverse industries and languages worldwide.

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Mlearning.ai

JUNE 14, 2023

— YouTube Introduction to Sequence Learning and Attention Mechanisms Stanford CS224N: NLP with Deep Learning | Winter 2019 | Lecture 8 — Translation, Seq2Seq, Attention — YouTube Stanford CS224N NLP with Deep Learning | Winter 2021 | Lecture 7 — Translation, Seq2Seq, Attention — YouTube 2.

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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. Examples include GPT (Generative Pre-trained Transformer), BERT (Bidirectional Encoder Representations from Transformers), Claude, etc. with labeled data.

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Marktechpost

JULY 4, 2023

Top Open Source Large Language Models GPT-Neo, GPT-J, and GPT-NeoX Extremely potent artificial intelligence models, such as GPT-Neo, GPT-J, and GPT-NeoX, can be used to Few-shot learning issues. Few-shot learning is similar to training and fine-tuning any deep learning model but requires fewer samples.

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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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Unite.AI

DECEMBER 1, 2023

Exploring the Techniques of LIME and SHAP Interpretability in machine learning (ML) and deep learning (DL) models helps us see into opaque inner workings of these advanced models. Flawed Decision Making The opaqueness in the decision-making process of LLMs like GPT-3 or BERT can lead to undetected biases and errors.

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AWS Machine Learning Blog

FEBRUARY 1, 2023

Recent developments in deep learning have led to increasingly large models such as GPT-3, BLOOM, and OPT, some of which are already in excess of 100 billion parameters. Many enterprise customers choose to deploy their deep learning workloads using Kubernetes—the de facto standard for container orchestration in the cloud.

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Unite.AI

JANUARY 23, 2024

LLMs are a class of deep learning models that are pretrained on massive text corpora, allowing them to generate human-like text and understand natural language at an unprecedented level. Techniques like Word2Vec and BERT create embedding models which can be reused. What are Large Language Models and Why are They Important?

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