Dataconomy

MARCH 19, 2025

This deep learning model is designed to extract hierarchical representations from unlabeled data, setting a strong foundation for tasks across various domains, including image recognition and natural language processing. Here, labeled data comes into play as it is used for supervised learning.

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Data Science Dojo

MAY 10, 2024

Hence, acting as a translator it converts human language into a machine-readable form. These embeddings when particularly used for natural language processing (NLP) tasks are also referred to as LLM embeddings. They function by remembering past inputs to learn more contextual information.

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Dataconomy

MAY 29, 2023

In their quest for effectiveness and well-informed decision-making, businesses continually search for new ways to collect information. QR codes can contain a huge amount of information, such as text, URLs, contact details, and more. In the realm of AI and ML, QR codes find diverse applications across various domains.

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Dataconomy

MARCH 13, 2025

It allows machines to analyze vast amounts of information, which can lead to incredible innovations across various industries. These sophisticated algorithms facilitate a deeper understanding of data, enabling applications from image recognition to natural language processing. What is deep learning?

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Data Science Dojo

SEPTEMBER 11, 2023

From virtual assistants like Siri and Alexa to personalized recommendations on streaming platforms, chatbots, and language translation services, language models surely are the engines that power it all.

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Data Science Dojo

DECEMBER 8, 2023

Zero-shot, one-shot, and few-shot learning are redefining how machines adapt and learn, promising a future where adaptability and generalization reach unprecedented levels. 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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Data Science Dojo

JUNE 11, 2024

These include image recognition, natural language processing, autonomous vehicles, financial services, healthcare, recommender systems, gaming and entertainment, and speech recognition. Inspired by human brain structure, they are designed to perform as powerful tools for pattern recognition, classification, and prediction tasks.

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Heartbeat

SEPTEMBER 20, 2023

Pixabay: by Activedia Image captioning combines natural language processing and computer vision to generate image textual descriptions automatically. Image captioning integrates computer vision, which interprets visual information, and NLP, which produces human language.

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Dataconomy

MARCH 28, 2025

By systematically updating the weights of connections between neurons, this algorithm forms the basis for training models that can tackle a variety of tasks, from image recognition to natural language processing. Structure of neural networks Neural networks consist of several layers that work together to process information.

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Dataconomy

MARCH 28, 2025

Masked language models (MLMs) are at the forefront of advancements in natural language processing (NLP). These innovative models have revolutionized how machines comprehend and generate human language. What are masked language models (MLMs)?

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Dataconomy

AUGUST 31, 2023

The classification model learns from the training data, identifying the distinguishing characteristics between each class, enabling it to make informed predictions. Classification in machine learning can be a versatile tool with numerous applications across various industries.

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Pickl AI

NOVEMBER 24, 2024

Each layer captures essential features while discarding irrelevant information. It contains the most crucial information from the input data in a significantly reduced form. Can I Use Autoencoders for Supervised Learning Tasks? Yes, autoencoders can enhance supervised learning tasks.

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Dataconomy

MARCH 26, 2025

AI data labeling is a fundamental process that underpins the success of machine learning (ML) applications. By accurately tagging and categorizing data, it transforms raw information into valuable insights, driving advancements across various sectors. What is AI data labeling?

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Dataconomy

APRIL 18, 2023

Understanding the basics of artificial intelligence Artificial intelligence is an interdisciplinary field of study that involves creating intelligent machines that can perform tasks that typically require human-like cognitive abilities such as learning, reasoning, and problem-solving.

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Dataconomy

MARCH 28, 2025

Historical context of LLM development The development of AI language models began in the 1960s with early programs like Eliza, which simulated conversation by using pre-defined scripts. This innovation improved the efficiency of processing information, allowing models to handle longer dependencies in text data.

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Dataconomy

MARCH 6, 2025

They are essential for securing communications and safeguarding sensitive information against unauthorized access. Machine learning as an algorithm example Machine learning encompasses a variety of algorithms that learn from data and improve over time.

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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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AWS Machine Learning Blog

JULY 18, 2023

It’s important to take extra precautions to protect your device and sensitive information. As technology is improving, the detection of spam emails becomes a challenging task due to its changing nature. Text classification is essential for applications like web searches, information retrieval, ranking, and document classification.

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Hacker News

MAY 3, 2023

The core process is a general technique known as self-supervised learning , a learning paradigm that leverages the inherent structure of the data itself to generate labels for training. Fine-tuning may involve further training the pre-trained model on a smaller, task-specific labeled dataset, using supervised learning.

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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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Towards AI

JULY 23, 2023

In the first part of the series, we talked about how Transformer ended the sequence-to-sequence modeling era of Natural Language Processing and understanding. Semi-Supervised Sequence Learning As we all know, supervised learning has a drawback, as it requires a huge labeled dataset to train.

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Flipboard

DECEMBER 6, 2023

Summarization is the technique of condensing sizable information into a compact and meaningful form, and stands as a cornerstone of efficient communication in our information-rich age. In a world full of data, summarizing long texts into brief summaries saves time and helps make informed decisions.

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Pickl AI

NOVEMBER 13, 2024

The answer lies in the various types of Machine Learning, each with its unique approach and application. In this blog, we will explore the four primary types of Machine Learning: Supervised Learning, UnSupervised Learning, semi-Supervised Learning, and Reinforcement Learning.

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Dataconomy

MAY 25, 2023

On the other hand, artificial intelligence is the simulation of human intelligence in machines that are programmed to think and learn like humans. By leveraging advanced algorithms and machine learning techniques, IoT devices can analyze and interpret data in real-time, enabling them to make informed decisions and take autonomous actions.

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Towards AI

FEBRUARY 20, 2024

Types of Machine Learning There are three main categories of Machine Learning, Supervised learning, Unsupervised learning, and Reinforcement learning. Supervised learning: This involves learning from labeled data, where each data point has a known outcome. Models […]

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Becoming Human

MAY 11, 2023

Empowering Startups and Entrepreneurs | InvestBegin.com | investbegin The success of ChatGPT can be attributed to several key factors, including advancements in machine learning, natural language processing, and big data. NLP is a field of AI that focuses on enabling computers to understand and process human language.

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How to Learn Machine Learning

MAY 14, 2025

Copy the YouTube video URL from your browser Head over to Y2Mate and paste the URL in the search box Wait for Y2Mate to fetch the video information (like waiting for your model to initialize!)

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Dataconomy

APRIL 3, 2023

ML models are designed to learn from data and make predictions or decisions based on that data. Types of ML There are three main types of machine learning: Supervised learning: In supervised learning, the algorithm is trained on labeled data.

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Dataconomy

APRIL 3, 2023

ML models are designed to learn from data and make predictions or decisions based on that data. Types of ML There are three main types of machine learning: Supervised learning: In supervised learning, the algorithm is trained on labeled data.

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Data Science Dojo

JULY 4, 2024

User Satisfaction : For example, in natural language processing applications like chatbots, RLHF helps generate responses that are more engaging and satisfying to users by sounding more natural and providing appropriate contextual information.

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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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Explosion

MAY 17, 2023

A lot of people are building truly new things with Large Language Models (LLMs), like wild interactive fiction experiences that weren’t possible before. But if you’re working on the same sort of Natural Language Processing (NLP) problems that businesses have been trying to solve for a long time, what’s the best way to use them?

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Snorkel AI

MARCH 1, 2023

Foundation models are large AI models trained on enormous quantities of unlabeled data—usually through self-supervised learning. This process results in generalized models capable of a wide variety of tasks, such as image classification, natural language processing, and question-answering, with remarkable accuracy.

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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. LLMs don’t claim to reproduce accurate information.

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Pickl AI

OCTOBER 21, 2024

Summary: Zero-Shot Learning (ZSL) empowers AI systems to recognize and classify new categories without needing labelled examples. By leveraging auxiliary information such as semantic attributes, ZSL enhances scalability, reduces data dependency, and improves generalisation. Poorly defined attributes may lead to inaccurate predictions.

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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. Achieving higher performance is often challenging, as the context window restricts the model’s ability to process long sequences of information effectively.

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Towards AI

JANUARY 7, 2025

Well do so in three levels: first, by manually adding a classification head in PyTorch* and training the model so you can see the full process; second, by using the Hugging Face* Transformers library to streamline the process; and third, by leveraging PyTorch Lightning* and accelerators to optimize training performance.

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