Mlearning.ai

JUNE 20, 2023

“Self-Supervised methods […] are going to be the main method to train neural nets before we train them for difficult tasks” —  Yann LeCun Well! Let’s have a look at this Self-Supervised Learning! Let’s have a look at Self-Supervised Learning. That is why it is called Self -Supervised Learning.

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Mlearning.ai

JULY 6, 2023

NOTES, DEEP LEARNING, REMOTE SENSING, ADVANCED METHODS, SELF-SUPERVISED LEARNING A note of the paper I have read Photo by Kelly Sikkema on Unsplash Hi everyone, In today’s story, I would share notes I took from 32 pages of Wang et al., Taxonomy of the self-supervised learning Wang et al. 2022’s paper.

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Dataconomy

MARCH 12, 2025

Support Vector Machines (SVM) are a type of supervised learning algorithm designed for classification and regression tasks. Definition of SVM SVMs operate on the principle of finding the hyperplane that maximizes the margin between different classes. What are Support Vector Machines (SVM)?

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Smart Data Collective

JUNE 7, 2019

At the early era of Artificial Intelligence, programmers tried to teach machines from the definition of logical rules that the machine itself could extend during the execution of the program. Although there are many types of learning, Michalski defined the two most common types of learning: Supervised Learning.

Machine Learning 101

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Dataconomy

APRIL 14, 2025

Instance-based learning (IBL) revolves around the principle of learning from specific examples, focusing on the instances that characterize the data rather than developing comprehensive theories or models. Unsupervised learning: Focuses on extracting patterns from data without pre-labeled responses, identifying inherent structures.

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Dataconomy

MARCH 10, 2025

The role of labeled datasets Labeled datasets are a cornerstone of supervised learning, where algorithms learn from input-output pairs to establish patterns. Without valid ground truth data, the training process may lead to biased or flawed models that do not perform well on new, unseen data.

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Towards AI

FEBRUARY 7, 2025

How do you tell the Machine Learning models the meaning of a particular word, especially when they are quantitatively intelligent and lexically challenged? This definitely estimates the spectrum of usage of text annotations to cater to the demands of the AI revolution across various industries. Behind the Medium paywall?

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Explosion

APRIL 1, 2017

Prodigy features many of the ideas and solutions for data collection and supervised learning outlined in this blog post. It’s a cloud-free, downloadable tool and comes with powerful active learning models. Transfer learning and better annotation tooling are both key to our current plans for spaCy and related projects.

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Dataconomy

MARCH 12, 2025

Unlike traditional machine learning practices, which often require extensive technical expertise, Machine teaching enables non-experts to curate training data and facilitate the learning process, making AI more accessible across various applications.

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Dataconomy

MARCH 13, 2025

The functionality of deep learning Deep learning relies heavily on the architecture of neural networks, which consist of interconnected layers that process information similarly to the human brain. Definition of neural networks Neural networks are designed to recognize patterns in data.

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Dataconomy

MARCH 19, 2025

Here, labeled data comes into play as it is used for supervised learning. Definition of CDBNs Convolutional Deep belief networks (CDBNs) extend the conventional DBN architecture by incorporating convolutional layers. Fine-tuning Once pre-training is complete, the fine-tuning phase begins.

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The MLOps Blog

MARCH 21, 2023

Your data scientists develop models on this component, which stores all parameters, feature definitions, artifacts, and other experiment-related information they care about for every experiment they run. Building a Machine Learning platform (Lemonade). Design Patterns in Machine Learning for MLOps (by Pier Paolo Ippolito).

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AssemblyAI

MAY 17, 2023

The below animation demonstrates this process: 0:00 / 1× Since the Language Model is, by definition, a probability distribution over word sequences, we generate text by simply recursively asking for the most likely next word given all of our previous words. Yes, it really is that simple. Can we do better?

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Dataconomy

MARCH 6, 2025

Definition and characteristics of algorithms Algorithms are characterized by their systematic procedures. Machine learning as an algorithm example Machine learning encompasses a variety of algorithms that learn from data and improve over time. It represents a logical framework for approaching tasks and finding solutions.

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Explosion

MAY 17, 2023

That’s definitely new. Once you’re past prototyping and want to deliver the best system you can, supervised learning will often give you better efficiency, accuracy and reliability than in-context learning for non-generative tasks — tasks where there is a specific right answer that you want the model to find.

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Towards AI

FEBRUARY 20, 2024

I am starting a series with this blog, which will guide a beginner to get the hang of the ‘Machine learning world’. Photo by Andrea De Santis on Unsplash So, What is Machine Learning? Definition says, machine learning is the ability of computers to learn without explicit programming.

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AWS Machine Learning Blog

MARCH 27, 2025

In this post, we explore how you can use Amazon Bedrock to generate high-quality categorical ground truth data, which is crucial for training machine learning (ML) models in a cost-sensitive environment. When you evaluate a case, evaluate the definitions in order and label the case with the first definition that fits.

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Applied Data Science

MARCH 11, 2022

Our internal agents are playing games until they learn how to cooperate and trick us into believing we are an individual. Gamification There are many definitions for what a game is. Techniques developed in NLP, such as the Transformer architecture, are useful in very diverse fields such as computer vision and reinforcement learning.

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Towards AI

FEBRUARY 15, 2024

I definitely recommend watching this one for all learners out here! Ramcharan12345 is looking to collaborate with AI devs who can leverage spaCy for NLP, utilize scikit-learn for supervised learning on historical data for symptom mapping, and implement TensorFlow/Keras for neural network-based risk prediction.

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Snorkel AI

APRIL 18, 2023

Typically, you let the experts read some articles, label them, and then use them as training data and train the supervised learning model. To address all these problems, we looked into weak supervised learning. Once we label a fraction of documents, we use that as training data to train the supervised learning model.

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Snorkel AI

APRIL 18, 2023

Typically, you let the experts read some articles, label them, and then use them as training data and train the supervised learning model. To address all these problems, we looked into weak supervised learning. Once we label a fraction of documents, we use that as training data to train the supervised learning model.

Supervised Learning 52

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Dataconomy

MARCH 27, 2023

Definition and purpose of RPA Robotic process automation refers to the use of software robots to automate rule-based business processes. Unsupervised learning:  This involves using unlabeled data to identify patterns and relationships within the data. Technology: Includes a range of technologies, including ML and deep learning.

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ML @ CMU

APRIL 23, 2025

Unlike prior benchmarks, miniCTX includes large repositories with relevant definitions, lemmas, and structures. To address this, the authors introduce miniCTX, a benchmark designed to test models’ ability to prove theorems using previously unseen, extensive context from real Lean projects and textbooks.

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Data Science 101

OCTOBER 31, 2019

A definition from the book ‘Data Mining: Practical Machine Learning Tools and Techniques’, written by, Ian Witten and Eibe Frank describes Data mining as follows: “ Data mining is the extraction of implicit, previously unknown, and potentially useful information from data. Classification. Regression.

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How to Learn Machine Learning

MAY 2, 2025

Azure ML supports various approaches to model creation: Automated ML : For beginners or those seeking quick results, Automated ML can generate optimized models based on your dataset and problem definition. Simply prepare your data, define your target variable, and let AutoML explore various algorithms and hyperparameters.

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Chatbots Life

MAY 12, 2023

Such models can also learn from a set of few examples The process of presenting a few examples is also called In-Context Learning , and it has been demonstrated that the process behaves similarly to supervised learning. Although the model acts as a highly-skilled, the profession definitely carries a lot of risks.

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Pickl AI

NOVEMBER 18, 2024

Key Takeaways Machine Learning Models are vital for modern technology applications. Types include supervised, unsupervised, and reinforcement learning. Key steps involve problem definition, data preparation, and algorithm selection. Ethical considerations are crucial in developing fair Machine Learning solutions.

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Snorkel AI

JULY 5, 2023

A large percentage of ML projects are based on supervised learning, which is very dependent on good feature selection. But then, Robert, what do you think are some of the challenges applied folks in the supervised learning space face when trying to productionize these use cases? That actually brings us to a good point.

Supervised Learning 52

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Snorkel AI

JULY 5, 2023

A large percentage of ML projects are based on supervised learning, which is very dependent on good feature selection. But then, Robert, what do you think are some of the challenges applied folks in the supervised learning space face when trying to productionize these use cases? That actually brings us to a good point.

AI 52

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Snorkel AI

JULY 5, 2023

A large percentage of ML projects are based on supervised learning, which is very dependent on good feature selection. But then, Robert, what do you think are some of the challenges applied folks in the supervised learning space face when trying to productionize these use cases? That actually brings us to a good point.

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Pickl AI

JANUARY 2, 2025

Introduced by Claude Shannon in 1948, entropy revolutionised how we measure information and remains central to modern Data Science, including Machine Learning. Lets delve into its mathematical definition and key properties. Lets explore its definition, connection to entropy, and practical applications.

Machine Learning 52

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Explosion

AUGUST 3, 2017

You’ll collect more user actions, giving you lots of smaller pieces to learn from, and a much tighter feedback loop between the human and the model. By definition, you can’t directly control what the process returns. Sometimes the meaning representation will directly address a useful question.

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Pickl AI

JULY 12, 2024

This section delves into its foundational definitions, types, and critical concepts crucial for comprehending its vast landscape. Machine Learning algorithms are trained on large amounts of data, and they can then use that data to make predictions or decisions about new data.

Artificial Intelligence 52

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DagsHub

JANUARY 6, 2025

So, here is a list of components in an image labeling guideline:  Clear Definitions for Each Label/Class: A good guideline should provide a precise definition of each label or class to be assigned to an image. In this technique, a model is trained on an initial labeled dataset.

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Pickl AI

OCTOBER 24, 2024

Summary: This article compares Artificial Intelligence (AI) vs Machine Learning (ML), clarifying their definitions, applications, and key differences. While AI aims to replicate human intelligence across various domains, ML focuses on learning from data to improve performance.

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Dataconomy

MARCH 27, 2023

Definition and purpose of RPA Robotic process automation refers to the use of software robots to automate rule-based business processes. Unsupervised learning:  This involves using unlabeled data to identify patterns and relationships within the data. Technology: Includes a range of technologies, including ML and deep learning.

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IBM Journey to AI blog

SEPTEMBER 20, 2023

They use self-supervised learning algorithms to perform a variety of natural language processing (NLP) tasks in ways that are similar to how humans use language (see Figure 1). Large language models (LLMs) have taken the field of AI by storm.

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