ODSC - Open Data Science

NOVEMBER 19, 2024

Introduction to Containers for Data Science / Data Engineering with Michael A. Fudge’s AI slides introduced participants to using containers in data science and engineering workflows. His session explained how containers can simplify the deployment of data applications by bundling dependencies and configurations into portable images.

Deep Learning 52

Deep Learning Data Science Neural Network BERT 52

deepsense.ai

NOVEMBER 9, 2024

Prompt engineering Let’s start simple. With this in mind, we strongly recommend starting with prompt engineering. Tell me what you don’t know When prompting LLMs to solve a chosen problem, we can add an instruction to return an “I don’t know” answer when the model is in doubt.

Prompt Engineer 59

Prompt Engineer Prompt Engineering LLM ChatGPT 59

AWS Machine Learning Blog

JUNE 25, 2024

Advantages of adopting generative approaches for NLP tasks For customer feedback analysis, you might wonder if traditional NLP classifiers such as BERT or fastText would suffice. Operational efficiency Uses prompt engineering, reducing the need for extensive fine-tuning when new categories are introduced.

Automation 119

Automation Prompt Engineer Prompt Engineering Categorization 119

Pickl AI

SEPTEMBER 19, 2024

How Prompt Tuning Fits into the Broader Context of AI and Machine Learning In the broader context of AI and Machine Learning , prompt tuning is part of a larger strategy known as “prompt engineering.” Prompt tuning is a more focused method compared to full model fine-tuning.

Prompt Engineer 52

Prompt Engineer Prompt Engineering Artificial Intelligence Artificial Intelligence 52

Mlearning.ai

JANUARY 17, 2024

350x: Application Areas , Companies, Startups 3,000+: Prompts , Prompt Engineering, & Prompt Lists 250+: Hardware, Frameworks , Approaches, Tools, & Data 300+: Achievements, Impacts on Society , AI Regulation, & Outlook 20x: What is Generative AI?

Generative AI 98

Generative AI Prompt Engineer Prompt Engineering Large Language Models 98

Jay Alammar

MARCH 6, 2022

The company trains massive language models (both GPT-like and BERT-like) and offers them as an API (which also supports finetuning). It establishes the difference between generative (GPT-like) and representation (BERT-like) models and examples use cases for them. A little less than a year ago, I joined the awesome Cohere team.

BERT 40

BERT Large Language Models Explainability Prompt Engineer 40

Snorkel AI

MARCH 23, 2023

Chen explains that you can improve nuance in weak supervision by allowing the system to learn separate accuracy parameters for data subgroups. They also discuss the importance of prompt engineering and the need for more principled approaches to fine-tuning and guiding these models.

BERT 52

BERT Prompt Engineer Prompt Engineering Explainability 52

Heartbeat

MARCH 19, 2024

Like machine learning operations, LLMOps involves efforts from several contributors, like prompt engineers, data scientists, DevOps engineers, business analysts, and IT operations. This is, in fact, a baseline, and the actual LLMOps workflow usually involves more stakeholders like prompt engineers, researchers, etc.

Large Language Models 52

Large Language Models Prompt Engineer Prompt Engineering Natural Language Processing 52

Heartbeat

AUGUST 2, 2023

Large language models, such as GPT-3 (Generative Pre-trained Transformer 3), BERT, XLNet, and Transformer-XL, etc., It has become the backbone of many successful language models, like GPT-3, BERT, and their variants. We’re committed to supporting and inspiring developers and engineers from all walks of life.

Large Language Models 52

Large Language Models Prompt Engineer Prompt Engineering OpenAI 52

Heartbeat

OCTOBER 5, 2023

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. Feature Engineering and Model Experimentation MLOps: Involves improving ML performance through experiments and feature engineering.

Large Language Models 52

Large Language Models DevOps Prompt Engineer Prompt Engineering 52

Snorkel AI

MAY 25, 2023

BERT, the first breakout large language model In 2019, a team of researchers at Goole introduced BERT (which stands for bidirectional encoder representations from transformers). By making BERT bidirectional, it allowed the inputs and outputs to take each others’ context into account. BERT), or consist of both (e.g.,

Large Language Models 52

Large Language Models BERT Neural Network LLM 52

Snorkel AI

MAY 25, 2023

BERT, the first breakout large language model In 2019, a team of researchers at Goole introduced BERT (which stands for bidirectional encoder representations from transformers). By making BERT bidirectional, it allowed the inputs and outputs to take each others’ context into account. BERT), or consist of both (e.g.,

Large Language Models 52

Large Language Models BERT Neural Network LLM 52

Chatbots Life

MAY 12, 2023

As everything is explained from scratch but extensively I hope you will find it interesting whether you are NLP Expert or just want to know what all the fuss is about. We will discuss how models such as ChatGPT will affect the work of software engineers and ML engineers. and we will also explain how GPT can create jobs.

ChatGPT 105

ChatGPT Software Engineer NLP Prompt Engineer 105

Heartbeat

JANUARY 9, 2024

BERT and GPT are examples. You can adapt foundation models to downstream tasks in the following ways: Prompt Engineering: Prompt engineering is a powerful technique that enables LLMs to be more controllable and interpretable in their outputs, making them more suitable for real-world applications with specific requirements and constraints.

Large Language Models 52

Large Language Models Natural Language Processing LLM Machine Learning 52

AWS Machine Learning Blog

DECEMBER 22, 2023

These functions can be implemented in several ways, including BERT-style models, appropriately prompted LLMs, and more. Although new components have worked their way into the compute layer (fine-tuning, prompt engineering, model APIs) and storage layer (vector databases), the need for observability remains.

LLM 113

LLM Large Language Models BERT Prompt Engineer 113

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. We’re committed to supporting and inspiring developers and engineers from all walks of life. We pay our contributors, and we don’t sell ads.

Data Scientist 52

Data Scientist Deep Learning Algorithm BERT 52

Explosion

MAY 17, 2023

In 2014 I started working on spaCy , and here’s an excerpt of how I explained the motivation for the library: Computers don’t understand text. This lets you perform prompt engineering systematically, based on decisions you can record and later review. This is unfortunate, because that’s what the web almost entirely consists of.

LLM 135

LLM NLP Large Language Models OpenAI 135

Unite.AI

JUNE 27, 2024

Two key techniques driving these advancements are prompt engineering and few-shot learning. Prompt engineering involves carefully crafting inputs to guide AI models in producing desired outputs, ensuring more relevant and accurate responses.

Prompt Engineer 147

Prompt Engineer Prompt Engineering AI Modeling Large Language Models 147

Flipboard

DECEMBER 6, 2023

In this post, we focus on the BERT extractive summarizer. BERT extractive summarizer The BERT extractive summarizer is a type of extractive summarization model that uses the BERT language model to extract the most important sentences from a text. It works by first embedding the sentences in the text using BERT.

BERT 167

BERT Large Language Models Artificial Intelligence Artificial Intelligence 167

Marktechpost

SEPTEMBER 15, 2024

The pre-train and fine-tune paradigm, exemplified by models like ELMo and BERT, has evolved into prompt-based reasoning used by the GPT family. Techniques like Uprise and DaSLaM use lightweight retrievers or small models to optimize prompts, break down complex problems, or generate pseudo labels.

BERT 122

BERT LLM Large Language Models Categorization 122

AWS Machine Learning Blog

NOVEMBER 1, 2023

Please explain the main clinical purpose of such image?Can accuracy on the development set, while its counterpart bert-base-uncased boasts an accuracy of 92.7%. His research interests are in the area of natural language processing, explainable deep learning on tabular data, and robust analysis of non-parametric space-time clustering.

Auto-classification 112

Auto-classification LLM Auto-complete Generative AI 112

Topbots

JUNE 29, 2023

While you will absolutely need to go for this approach if you want to use Text2SQL on many different databases, keep in mind that it requires considerable prompt engineering effort. 4] In the open-source camp, initial attempts at solving the Text2SQL puzzle were focussed on auto-encoding models such as BERT, which excel at NLU tasks.[5,

Auto-complete 52

Auto-complete Algorithm Data Scientist Auto-classification 52