Analytics Vidhya

DECEMBER 25, 2022

Introduction There have been many recent advances in natural language processing (NLP), including improvements in language models, better representation of the linguistic structure, advancements in machine translation, increased use of deep learning, and greater use of transfer learning.

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Data Science Dojo

MAY 27, 2024

A visual representation of discriminative AI – Source: Analytics Vidhya Discriminative modeling, often linked with supervised learning, works on categorizing existing data. Generative AI often operates in unsupervised or semi-supervised learning settings, generating new data points based on patterns learned from existing data.

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Dataconomy

MAY 12, 2025

A validation set is a critical element in the machine learning process, particularly for those working within the realms of supervised learning. Its applications range from image recognition to natural language processing, highlighting the significance of building robust and adaptable models.

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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 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

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. The two main approaches of interest for embeddings include unsupervised and supervised learning.

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Data Science Dojo

JUNE 1, 2023

Machine Learning for Absolute Beginners by Kirill Eremenko and Hadelin de Ponteves This is another beginner-level course that teaches you the basics of machine learning using Python. The course covers topics such as supervised learning, unsupervised learning, and reinforcement learning.

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Data Science Dojo

AUGUST 19, 2024

These professionals venture into new frontiers like machine learning, natural language processing, and computer vision, continually pushing the limits of AI’s potential. Supervised learning: This involves training a model on a labeled dataset, where each data point has a corresponding output or target variable.

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Dataconomy

MARCH 13, 2025

These sophisticated algorithms facilitate a deeper understanding of data, enabling applications from image recognition to natural language processing. What is deep learning? Deep learning is a subset of artificial intelligence that utilizes neural networks to process complex data and generate predictions.

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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. If the goal is a creative and informative content generation, Llama 2 is the ideal choice.

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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

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. The two main approaches of interest for embeddings include unsupervised and supervised learning.

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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. The CNN is typically trained on a large-scale dataset, such as ImageNet, using techniques like supervised learning.

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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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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. They excel in processing sequential data for tasks such as speech recognition, natural language processing, and time series prediction.

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Dataconomy

MARCH 20, 2025

It acts as a learning mechanism, continuously refining model predictions through a process that adjusts weights based on errors. This iterative enhancement is vital for applications in predictive analytics, from face and speech recognition systems to complex natural language processing tasks.

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Dataconomy

MAY 29, 2023

The applications of AI span diverse domains, including natural language processing, computer vision, robotics, expert systems, and machine learning. Some of the methods used in ML include supervised learning, unsupervised learning, reinforcement learning, and deep learning.

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Dataconomy

AUGUST 31, 2023

Here are some examples of where classification can be used in machine learning: Image recognition : Classification can be used to identify objects within images. This type of problem is more challenging because the model needs to learn more complex relationships between the input features and the multiple classes.

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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. Training demands: It often requires substantial time and quality input data for optimal performance.

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Pickl AI

NOVEMBER 24, 2024

Fraud Detection In finance, autoencoders help detect fraudulent transactions by learning patterns from legitimate transactions and identifying anomalies that may indicate fraud. Can I Use Autoencoders for Supervised Learning Tasks? Yes, autoencoders can enhance supervised learning tasks.

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Smart Data Collective

JUNE 4, 2021

Learning the various categories of machine learning, associated algorithms, and their performance parameters is the first step of machine learning. Machine learning is broadly classified into three types – Supervised. In supervised learning, a variable is predicted. Semi-Supervised Learning.

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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 26, 2025

AI data labeling refers to the process of identifying and tagging data to train supervised learning models effectively. This critical step ensures that machine learning algorithms can recognize patterns and make predictions with greater accuracy. 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. The process generally begins with unsupervised learning, progresses through self-supervised learning, and culminates in deep learning architecture.

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Dataconomy

MARCH 6, 2025

Machine learning as an algorithm example Machine learning encompasses a variety of algorithms that learn from data and improve over time. Overview of machine learning algorithms Two primary approaches in machine learning are: Supervised learning: Algorithms learn from labeled data to make predictions on new, unseen data.

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IBM Journey to AI blog

DECEMBER 20, 2023

And retailers frequently leverage data from chatbots and virtual assistants, in concert with ML and natural language processing (NLP) technology, to automate users’ shopping experiences. Semi-supervised learning The fifth type of machine learning technique offers a combination between supervised and unsupervised 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

MARCH 4, 2023

Fully-Supervised Learning (Non-Neural Network) — powered by — Feature Engineering Supervised learning required input-output examples to train the model. To appreciate what is prompting and to get started, Part 1 discusses 4 major paradigms that have occurred over the past years. Let’s get started !!

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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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AWS Machine Learning Blog

JULY 18, 2023

Word2vec is useful for various natural language processing (NLP) tasks, such as sentiment analysis, named entity recognition, and machine translation. Set the learning mode hyperparameter to supervised. BlazingText has both unsupervised and supervised learning modes. Start training the model.

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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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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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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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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.

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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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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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