Dataconomy

APRIL 3, 2023

Understanding the basic components of artificial intelligence is crucial for developing and implementing AI technologies. Artificial intelligence, commonly referred to as AI , is the field of computer science that focuses on the development of intelligent machines that can perform tasks that would typically require human intervention.

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Dataconomy

APRIL 3, 2023

Understanding the basic components of artificial intelligence is crucial for developing and implementing AI technologies. Artificial intelligence, commonly referred to as AI , is the field of computer science that focuses on the development of intelligent machines that can perform tasks that would typically require human intervention.

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Dataversity

DECEMBER 16, 2022

2022 was a big year for AI, and we’ve seen significant advancements in various areas – including natural language processing (NLP), machine learning (ML), and deep learning. Unsupervised and self-supervised learning are making ML more accessible by lowering the training data requirements.

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Dataconomy

MARCH 14, 2023

In recent years, natural language processing and conversational AI have gained significant attention as technologies that are transforming the way we interact with machines and each other. Moreover, the model training process is capable of adapting to new languages and data effectively.

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Dataconomy

MAY 25, 2023

The integration of artificial intelligence in Internet of Things introduces new dimensions of efficiency, automation, and intelligence to our daily lives. Simultaneously, artificial intelligence has revolutionized the way machines learn, reason, and make decisions.

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Dataconomy

AUGUST 21, 2023

At its core, a Large Language Model (LLM) is a sophisticated machine learning entity adept at executing a myriad of natural language processing (NLP) activities. This includes tasks like text generation, classification, engaging in dialogue, and even translating text across languages. What is LLM in AI?

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Women in Big Data

DECEMBER 13, 2023

The Bay Area Chapter of Women in Big Data (WiBD) hosted its second successful episode on the NLP (Natural Language Processing), Tools, Technologies and Career opportunities. Natural Language Processing (NLP) is a branch of Artificial Intelligence (AI) that helps computers understand, interpret and manipulate human language.

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Mlearning.ai

AUGUST 4, 2023

Fine-tuning large language models (LLMs) has become a powerful technique for achieving impressive performance in various natural language processing tasks. Additionally, few-shot learning requires maintaining a large context window, which can be resource-intensive and impractical for memory-constrained environments.

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How to Learn Machine Learning

MAY 14, 2023

Acquiring Essential Machine Learning Knowledge Once you have a strong foundation in mathematics and programming, it’s time to dive into the world of machine learning. Additionally, you should familiarize yourself with essential machine learning concepts such as feature engineering, model evaluation, and hyperparameter tuning.

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Smart Data Collective

JUNE 4, 2021

Basics of Machine Learning. Machine learning is the science of building models automatically. It is a branch of artificial intelligence. Whereas in machine learning, the algorithm understands the data and creates the logic. Machine learning is broadly classified into three types – Supervised.

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ODSC - Open Data Science

AUGUST 29, 2023

Natural Language Processing Engineer Natural Language Processing Engineers who specialize in prompt engineering are linguistic architects when it comes to AI communication. As AI models become more sophisticated and versatile, the demand for tailored, context-aware interactions grows.

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Pickl AI

AUGUST 7, 2023

However, the most complex and expensive type of Machine Learning trend in use is Labelled Data. They require human expertise to tell a machine about the different data types to help Artificial Intelligence learn it. Both labeled and unlabeled data are used in Machine Learning for different purposes.

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Dataconomy

MARCH 27, 2023

Robotic process automation vs machine learning is a common debate in the world of automation and artificial intelligence. Both have the potential to transform the way organizations operate, enabling them to streamline processes, improve efficiency, and drive business outcomes. What is machine learning (ML)?

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ODSC - Open Data Science

NOVEMBER 8, 2023

This is where the art of artificial intelligence comes into play, and has even become its own job. One common approach is to use supervised learning. The LLM learns to map the input to the output by minimizing a loss function. You can even fine-tune prompts to get exactly what you want.

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Mlearning.ai

JULY 11, 2023

Deep learning has transformed artificial intelligence, allowing machines to learn and make smart decisions. If you’re interested in exploring deep learning, this step-by-step guide will help you learn the basics and develop the necessary skills. Learn how to fine-tune model parameters effectively.

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PyImageSearch

MARCH 30, 2023

What is Machine Learning A Closer Look at Machine Learning Machine learning (ML) revolves around creating algorithms and related systems that learn behavior strategies in specific environments using instructions and training data sets. While they share many similarities, they each have unique roles and applications.

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ODSC - Open Data Science

OCTOBER 15, 2023

AI is quickly scaling through dozens of industries as companies, non-profits, and governments are discovering the power of artificial intelligence. MIT MIT is a world-renowned university that has been at the forefront of research in artificial intelligence for decades. So, what are you waiting for?

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Heartbeat

OCTOBER 11, 2023

A key component of artificial intelligence is training algorithms to make predictions or judgments based on data. This process is known as machine learning or deep learning. Two of the most well-known subfields of AI are machine learning and deep learning.

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Towards AI

JANUARY 30, 2024

GPT4, Stable Diffusion, Llama, BERT, Gemini Large Language Models (LLMs) Foundation models, trained on the “Transformer Architecture”, that can perform a wide array of Natural Language Processing (NLP) tasks like text generation, classification, summarisation etc. Examples: GPT 3.5,

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Mlearning.ai

APRIL 21, 2023

Text annotation Text annotation is the process of adding structured information to unstructured text data in order to make it more understandable and useful for downstream applications. The resulting annotated text data can then be used to train and improve the accuracy of LLM models for specific natural language processing tasks.

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IBM Journey to AI blog

JUNE 14, 2023

Sometimes the problem with artificial intelligence (AI) and automation is that they are too labor intensive. Traditional AI tools, especially deep learning-based ones, require huge amounts of effort to use. That sounds like a joke, but we’re quite serious.

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Dataconomy

MARCH 27, 2023

Robotic process automation vs machine learning is a common debate in the world of automation and artificial intelligence. Both have the potential to transform the way organizations operate, enabling them to streamline processes, improve efficiency, and drive business outcomes. What is machine learning (ML)?

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ODSC - Open Data Science

SEPTEMBER 19, 2023

Depending on the position, and company, it can require a strong understanding of natural language processing, computer science, linguistics, and software engineering. Conclusion So, it’s clear that though this field is quite new, and still in its infancy, prompt engineering is a challenging and rewarding field.

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Dataversity

DECEMBER 23, 2021

This has created a need for humans and artificial intelligence (AI) to work side by side to create a true natural language-enabled enterprise, which allows the organization to deliver business outcomes with an effectiveness that far surpasses […].

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ODSC - Open Data Science

APRIL 26, 2023

ACTIS by Everana uses artificial intelligence and machine learning algorithms to provide predictive analytics, real-time insights, and integrated data all via the cloud. The goal of which is to lower the cost of care through predictive medicine powered by AI.

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Data Science Blog

FEBRUARY 23, 2023

1, Data is the new oil, but labeled data might be closer to it Even though we have been in the 3rd AI boom and machine learning is showing concrete effectiveness at a commercial level, after the first two AI booms we are facing a problem: lack of labeled data or data themselves. That is, is giving supervision to adjust via.

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Data Science Dojo

DECEMBER 8, 2023

In the dynamic field of artificial intelligence, traditional machine learning, reliant on extensive labeled datasets, has given way to transformative learning paradigms. Source: Photo by Hal Gatewood on Unsplash In this exploration, we navigate from the basics of supervised learning to the forefront of adaptive models.

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Hacker News

MARCH 25, 2022

Transformers made self-supervised learning possible, and AI jumped to warp speed,” said NVIDIA founder and CEO Jensen Huang in his keynote address this week at GTC. Transformers are in many cases replacing convolutional and recurrent neural networks (CNNs and RNNs), the most popular types of deep learning models just five years ago.

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IBM Journey to AI blog

AUGUST 11, 2023

Machine learning (ML), a subset of artificial intelligence (AI), is an important piece of data-driven innovation. Machine learning engineers take massive datasets and use statistical methods to create algorithms that are trained to find patterns and uncover key insights in data mining projects.

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AWS Machine Learning Blog

NOVEMBER 27, 2023

Generative artificial intelligence ( generative AI ) models have demonstrated impressive capabilities in generating high-quality text, images, and other content. As AI adoption continues to accelerate, developing efficient mechanisms for digesting and learning from unstructured data becomes even more critical in the future.

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IBM Journey to AI blog

JULY 6, 2023

What is machine learning? Machine learning (ML) is a subset of artificial intelligence (AI) that focuses on learning from what the data science comes up with. Machine learning can then “learn” from the data to create insights that improve performance or inform predictions.

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AWS Machine Learning Blog

AUGUST 16, 2023

Training machine learning (ML) models to interpret this data, however, is bottlenecked by costly and time-consuming human annotation efforts. One way to overcome this challenge is through self-supervised learning (SSL). His specialty is Natural Language Processing (NLP) and is passionate about deep learning.

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Heartbeat

JULY 6, 2023

At a high level, the Swin Transformer is based on the transformer architecture, which was originally developed for natural language processing but has since been adapted for computer vision tasks. The Swin Transformer is part of a larger trend in deep learning towards attention-based models and self-supervised learning.

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Data Science Dojo

DECEMBER 8, 2023

Counting Shots, Making Strides: Zero, One and Few-Shot Learning Unleashed In the dynamic field of artificial intelligence, traditional machine learning, reliant on extensive labeled datasets, has given way to transformative learning paradigms. Welcome to the frontier of machine learning innovation!

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Defined.ai blog

MARCH 20, 2023

We wanted to learn from the best, and what better way to learn more about this topic than to ask the source itself? We will be asking ChatGPT to share its unique perspective on the important issue in artificial intelligence as it pertains to the “Human in the loop” (HITL) annotations.

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IBM Journey to AI blog

DECEMBER 20, 2023

What is machine learning? ML is a computer science, data science and artificial intelligence (AI) subset that enables systems to learn and improve from data without additional programming interventions. Each type and sub-type of ML algorithm has unique benefits and capabilities that teams can leverage for different tasks.

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Pickl AI

MAY 31, 2023

Accordingly, there are many Python libraries which are open-source including Data Manipulation, Data Visualisation, Machine Learning, Natural Language Processing , Statistics and Mathematics. After that, move towards unsupervised learning methods like clustering and dimensionality reduction.

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