NYU Center for Data Science

OCTOBER 10, 2023

In a recent interview, Chen explained the importance of studying interpretability artifacts not just at the end of a model’s training but throughout its entire learning process. “A The paper is a case study of syntax acquisition in BERT (Bidirectional Encoder Representations from Transformers).

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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!

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Viso.ai

MARCH 19, 2024

Powered by DistilBERT, Grounding DINO is a distilled version of the BERT model optimized for speed and efficiency. The post Grounded-SAM Explained: A New Image Segmentation Paradigm? For example, replacing Grounding DINO with GLIP or Stable-Diffusion with ControlNet or GLIGEN with ChatGPT). appeared first on viso.ai.

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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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Flipboard

JULY 10, 2023

These are advanced machine learning models that are trained to comprehend massive volumes of text data and generate natural language. Examples of LLMs include GPT-3 (Generative Pre-trained Transformer 3) and BERT (Bidirectional Encoder Representations from Transformers).

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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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Marktechpost

JULY 4, 2023

” Even for seasoned programmers, the syntax of shell commands might need to be explained. 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.

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AWS Machine Learning Blog

JULY 24, 2023

When deploying Deep Learning models at scale, it is crucial to effectively utilize the underlying hardware to maximize performance and cost benefits. In this post, we share best practices to deploy deep learning models with FastAPI on AWS Inferentia NeuronCores. xlarge 1 4 1 2 Inf1.2xlarge 1 4 2 4 Inf1.6xlarge 4 16 1.5

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Heartbeat

FEBRUARY 7, 2023

image by rawpixel.com Understanding the concept of language models in natural language processing (NLP) is very important to anyone working in the Deep learning and machine learning space. Learn more from Uber’s Olcay Cirit. One of the areas that has seen significant growth is language modeling.

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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 Impact V.2

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Heartbeat

JULY 19, 2023

Hugging Face transformer models BERT, GPT-2, RoBERTa, and T5 are included in the library. BERT is one of the most popular Hugging Face transformer models (Bidirectional Encoder Representations from Transformers). To train the transformer model BERT, a massive corpus of text was used. Next, we move on to the model inference step.

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AWS Machine Learning Blog

MAY 3, 2023

times the speed for BERT, making Graviton-based instances the fastest compute optimized instances on AWS for these models. How to take advantage of the optimizations The simplest way to get started is by using the AWS Deep Learning Containers (DLCs) on Amazon Elastic Compute Cloud (Amazon EC2) C7g instances or Amazon SageMaker.

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Viso.ai

DECEMBER 27, 2023

CLIP Architecture explained Contrastive Language-Image Pre-training (CLIP) uses a dual-encoder architecture to map images and text into a shared latent space. It often uses a transformer-based architecture, like a Transformer or BERT, to process text sequences. It works by jointly training two encoders.

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Viso.ai

MARCH 26, 2024

The introduction of the Transformer model was a significant leap forward for the concept of attention in deep learning. Furthermore, attention mechanisms work to enhance the explainability or interpretability of AI models. Vaswani et al. without conventional neural networks.

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Heartbeat

JUNE 6, 2023

Prerequisites To follow along with this tutorial, you will need the following: Basic knowledge of Python and deep learning. Editorially independent, Heartbeat is sponsored and published by Comet, an MLOps platform that enables data scientists & ML teams to track, compare, explain, & optimize their experiments.

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Heartbeat

OCTOBER 5, 2023

Unsurprisingly, Machine Learning (ML) has seen remarkable progress, revolutionizing industries and how we interact with technology. The emergence of Large Language Models (LLMs) like OpenAI's GPT , Meta's Llama , and Google's BERT has ushered in a new era in this field. We pay our contributors, and we don't sell ads.

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Bugra Akyildiz

DECEMBER 18, 2022

Standard researchers wrote 3 posts that explain in detail what CL is and how it is being used: [link] [link] [link] Vespa wrote about how they are using pre-trained bidirectional encoders for search functionality. Dragon can be used as a drop-in replacement for BERT. SELECT date.fromtimestamp(.purchase_ts)

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Heartbeat

AUGUST 2, 2023

Introduction to LLMs LLM in the sphere of AI Large language models (often abbreviated as LLMs) refer to a type of artificial intelligence (AI) model typically based on deep learning architectures known as transformers. Large language models, such as GPT-3 (Generative Pre-trained Transformer 3), BERT, XLNet, and Transformer-XL, etc.,

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AWS Machine Learning Blog

JANUARY 17, 2023

The following table summarizes the evaluation results for our multimodal model with a Hugging Face sentence transformer and Scikit-learn random forest classifier. BERT + Random Forest. BERT + Random Forest. BERT + Random Forest with HPO. BERT + Random Forest. BERT + Random Forest with HPO.

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Heartbeat

SEPTEMBER 12, 2023

Implementing end-to-end deep learning projects has never been easier with these awesome tools Image by Freepik LLMs such as GPT, BERT, and Llama 2 are a game changer in AI. But you need to fine-tune these language models when performing your deep learning projects. This is where AI platforms come in. Let’s do this.

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Mlearning.ai

FEBRUARY 8, 2023

for example, the attention-based models (Transformer, Bert, GPT3 ….) transformer, bert) as an encoder to map the word into different spaces, which could describe the word semantics better. Thus, in Stable Diffusion 2, the author used OpenCLIP-ViT/H as the text encoder (τ_θ in the above image) to extract the word representation (repr.).

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Heartbeat

OCTOBER 23, 2023

By analyzing historical data and utilizing predictive machine learning algorithms like BERT, ARIMA, Markov Chain Analysis, Principal Component Analysis, and Support Vector Machine, they can assess the likelihood of adverse events, such as hospital readmissions, and stratify patients based on risk profiles.

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John Snow Labs

JUNE 6, 2023

Text classification with transformers refers to the application of deep learning models based on the transformer architecture to classify sequences of text into predefined categories or labels. BERT (Bidirectional Encoder Representations from Transformers) is a language model that was introduced by Google in 2018.

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Dlabs.ai

MAY 22, 2022

The instructors are very good at explaining complex topics in an easy-to-understand way. Machine Learning Author: Andrew Ng Everyone interested in machine learning has heard of Andrew Ng : one of the most respected people in the AI world. These skills are a must-have if you plan to work with Google Cloud.

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Snorkel AI

AUGUST 8, 2023

Deep learning to learn complex patterns from data that are difficult to identify using traditional methods. Model explainability. Then, they can distill that model’s expertise into a deployable form by having it “teach” a smaller model like BERT.

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Snorkel AI

AUGUST 8, 2023

Deep learning to learn complex patterns from data that are difficult to identify using traditional methods. Model explainability. Then, they can distill that model’s expertise into a deployable form by having it “teach” a smaller model like BERT.

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Viso.ai

DECEMBER 18, 2023

ONNX (Open Neural Network Exchange) is an open-source format that facilitates interoperability between different deep learning algorithms for simple model sharing and deployment. It promotes interoperability between different deep learning frameworks for simple model sharing and deployment. Framework Interoperability.

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ODSC - Open Data Science

JANUARY 29, 2024

Prompt Engineering Platforms LLM Platforms: ChatGpt, GPT-4, LLama 2, Stable Diffusion, and BERT ChatGPT OpenAI’s ChatGPT was one of the most popular apps in history, so it’s no surprise that the suite of API models including GPT-3.5 BERT While technically not an LLM (pre-dates LLMs), due to its 360 million parameters vs the (supposed) 1.76

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Heartbeat

NOVEMBER 17, 2023

Considerations for Choosing a Distance Metric for Text Embeddings: Scale or Magnitude : Embeddings from models like Word2Vec, FastText, BERT, and GPT are often normalized to unit length. In the context of this code, it seems to be applied to vectors by determining the proportion of differing vector elements.

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Heartbeat

JANUARY 9, 2024

BERT and GPT are examples. Editor's Note: Heartbeat is a contributor-driven online publication and community dedicated to providing premier educational resources for data science, machine learning, and deep learning practitioners. Researchers, developers, and practitioners can all access, utilize, and modify them.

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Marktechpost

JULY 11, 2023

Even OpenAI’s DALL-E and Google’s BERT have contributed to making significant advances in recent times. GPT 4, which is the latest add-on to OpenAI’s deep learning models, is multimodal in nature. What is AutoGPT? Recently, a new AI tool has been released, which has even more potential than ChatGPT.

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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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Heartbeat

SEPTEMBER 20, 2023

Deep learning-based models, especially CNNs, have revolutionized feature extraction in image captioning. CNNs are particularly well-suited for this task due to their ability to learn hierarchical representations of visual data. We're committed to supporting and inspiring developers and engineers from all walks of life.

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Mlearning.ai

MARCH 30, 2023

According to Wikipedia, “Transfer learning is a research problem in machine learning that focuses on storing knowledge gained while solving one problem and applying it to a different but related problem.” Let me explain this in simple words.

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Heartbeat

OCTOBER 10, 2023

RoBERTa RoBERTa (Robustly Optimized BERT Approach) is a natural language processing (NLP) model based on the BERT (Bidirectional Encoder Representations from Transformers) architecture. This refers to the fact that BERT was pre-trained on one set of tasks but fine-tuned on a different set of tasks for downstream NLP applications.

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Viso.ai

DECEMBER 18, 2023

As an Edge AI implementation, TensorFlow Lite greatly reduces the barriers to introducing large-scale computer vision with on-device machine learning, making it possible to run machine learning everywhere. TensorFlow Lite is an open-source deep learning framework designed for on-device inference ( Edge Computing ).

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AssemblyAI

JUNE 27, 2023

In this article, we take an overview of some exciting new advances in the space of Generative AI for audio that have all happened in the past few months , explaining where the key ideas come from and how they come together to bring audio generation to a new level. This blog post is part of a series on generative AI.

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