Dataconomy

MARCH 20, 2025

Semi-supervised learning is reshaping the landscape of machine learning by bridging the gap between supervised and unsupervised methods. With vast amounts of unlabeled data available in various domains, semi-supervised learning proves to be an invaluable tool in tackling complex classification tasks.

Supervised Learning 91

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Dataconomy

APRIL 16, 2025

Supervised learning is a powerful approach within the expansive field of machine learning that relies on labeled data to teach algorithms how to make predictions. What is supervised learning? Supervised learning refers to a subset of machine learning techniques where algorithms learn from labeled datasets.

Supervised Learning 91

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Dataconomy

JUNE 13, 2025

Supervised learning applications: Use labeled data to teach the system how to classify images accurately. Unsupervised learning insights: Allow systems to discover patterns and groupings within unlabeled data.

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Dataconomy

APRIL 24, 2025

Change the guest list or seating logic, and you get dimensionality reduction, self-supervised learning, or spectral clustering. The I-Con framework shows that algorithms differ mainly in how they define those relationships. It all boils down to preserving certain relationships while simplifying others.

Machine Learning 187

Machine Learning Machine Learning Clustering Algorithm 187

Towards AI

JANUARY 29, 2025

Have you ever felt like the world of machine learning is moving so fast that you can barely keep up? One day, its all about supervised learning and the next, people are throwing around terms like self-supervised learning as if its the holy grail of AI. So, what exactly is self-supervised learning?

Supervised Learning 94

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Dataconomy

APRIL 29, 2025

Challenges in supervised learning Supervised learning often grapples with data limitations, particularly the scarcity of labeled examples necessary for training algorithms effectively. Such flaws can lead to significant consequences in critical fields like healthcare or finance.

Machine Learning 91

Machine Learning Machine Learning Supervised Learning Natural Language Processing 91

Data Science Dojo

JANUARY 30, 2025

Now, it is time to train the teacher model on the dataset using standard supervised learning. We need to then define a function that combines soft label loss (teachers predictions) and hard label loss (ground truth) to train the student model. Finally, we can evaluate the models on the test dataset and print their accuracy.

Natural Language Processing 195

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Data Science Dojo

JULY 23, 2025

Key Characteristics of LLMs: Built on transformer architecture Trained on large corpora using self-supervised learning Capable of understanding context, semantics, grammar, and even logic Scalable and general-purpose, making them adaptable across tasks and industries Learn more about LLMs and their applications.

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Dataconomy

APRIL 29, 2025

Goals of supervised learning In supervised learning tasks, managing the bias-variance tradeoff aligns with specific objectives. Mimicking the target function (f) In supervised learning, the primary goal is to build models that genuinely mimic the target function relating inputs to outputs.

Cross Validation 91

Cross Validation Supervised Learning Machine Learning Machine Learning 91

Towards AI

FEBRUARY 3, 2025

Inspired by its reinforcement learning (RL)-based optimization, I wondered: can we apply a similar RL-driven strategy to supervised learning? Instead of manually selecting a model, why not let reinforcement learning learn the best strategy for us?

Supervised Learning 56

Supervised Learning Cross Validation Data Scientist Machine Learning 56

AWS Machine Learning Blog

JANUARY 31, 2025

Increasingly, FMs are completing tasks that were previously solved by supervised learning, which is a subset of machine learning (ML) that involves training algorithms using a labeled dataset. An FM-driven solution can also provide rationale for outputs, whereas a traditional classifier lacks this capability.

Supervised Learning 102

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Pickl AI

NOVEMBER 24, 2024

Can I Use Autoencoders for Supervised Learning Tasks? Yes, autoencoders can enhance supervised learning tasks. By pre-training the model on unlabeled data to learn useful features, you can improve classification or regression performance on labelled datasets.

Deep Learning 52

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Flipboard

JUNE 27, 2025

While deep neural networks have become an increasingly popular alternative to kernel methods for machine learning over the past decade, kernel-based systems have seen a resurgence in the past few years due to their relative simplicity and advantages when working with small datasets.

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Dataconomy

MARCH 11, 2025

Applications of linear regression in machine learning Linear regression plays a significant role in supervised learning, where it models relationships based on a labeled dataset. Understanding supervised learning In supervised learning, algorithms learn from training data that includes input-output pairs.

Supervised Learning 91

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Towards AI

DECEMBER 18, 2024

In Part 2, we compared Contextual Bandits to Supervised Learning, highlighting the advantages of adaptive optimization over static learning. In Part 1, we introduced the concept of Contextual Bandits, a powerful technique for solving real-time decision-making problems.

Machine Learning 105

Machine Learning Machine Learning Supervised Learning AI 105

How to Learn Machine Learning

JULY 6, 2025

Machine learning forms a core subset of artificial intelligence and has a heavy influence in modern technology ranging from recommendation engines to self-driving cars. Supervised Learning Algorithms One of the most common applications of machine learning occurs in supervised learning.

Machine Learning 52

Machine Learning Machine Learning Algorithm Clustering 52

Towards AI

JULY 10, 2025

InfoNCE Loss (Information Noise Contrastive Estimation): A popular variant of contrastive loss, InfoNCE measures similarity by treating the problem as a binary classification task: given a positive pair and a set of negative pairs, the model learns to discriminate them. It’s widely used in self-supervised learning for its effectiveness.

Database 83

Database Supervised Learning Clustering AI 83

ODSC - Open Data Science

JULY 4, 2025

Learn how to build resilient, production-grade AI systems end-to-end. Deep Learning & Multi‑Modal Models Explore foundational and advanced deep learning — from CNNs and GANs to transformers, self-supervised learning, and reinforcement methods — plus integration with multi-modal systems.

Deep Learning 52

Deep Learning Deep Learning ML ML 52

Towards AI

DECEMBER 18, 2024

In Part 2, we compared Contextual Bandits to Supervised Learning, highlighting the advantages of adaptive optimization over static learning. In Part 1, we introduced the concept of Contextual Bandits, a powerful technique for solving real-time decision-making problems.

Machine Learning 111

Machine Learning Machine Learning Supervised Learning AI 111

Towards AI

FEBRUARY 26, 2025

Have you ever looked at AI models and thought, How the heck does this thing actually learn? Supervised learning, a cornerstone of machine learning, often seems like magic like feeding a computer some data and watching it miraculously predict things. This member-only story is on us. Upgrade to access all of Medium.

Supervised Learning 78

Supervised Learning Machine Learning Machine Learning AI 78

Towards AI

MAY 15, 2025

Regression vs Classification in Machine Learning Why Most Beginners Get This Wrong | M004 If youre learning Machine Learning and think supervised learning is straightforward, think again. When I first started with supervised learning, I picked models like they were tools in a toolbox.

Machine Learning 95

Machine Learning Machine Learning Supervised Learning ML 95

Dataconomy

FEBRUARY 21, 2025

Using deep learning and transformer-based models, SparkAI processes extensive audio datasets to analyze tonal characteristics and generate realistic guitar sounds. The system applies self-supervised learning techniques, allowing it to adapt to different playing styles without requiring manually labeled training data.

Supervised Learning 113

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Dataconomy

JUNE 11, 2025

Supervised learning means training an AI model using examples with labels. If labels are wrong or messy, the model learns the wrong thing. It’s where model accuracy begins. What defines high-quality data annotation? Not all labels are equal. These annotations tell the model what it’s looking at or working with.

AI 103

AI AI Data Quality Supervised Learning 103

Pickl AI

JANUARY 21, 2025

Types of Machine Learning Algorithms Machine Learning has become an integral part of modern technology, enabling systems to learn from data and improve over time without explicit programming. The goal is to learn a mapping from inputs to outputs, allowing the model to make predictions on unseen data.

Machine Learning 52

Machine Learning Machine Learning Algorithm Decision Trees 52

Flipboard

JULY 4, 2025

Theres another reason we are doing this, let me clarify it a bit later. from sklearn.model_selection import train_test_split X_train, X_test, y_train, y_test = train_test_split( X, y, test_size = 0.2, random_state=42) Preprocessing the data and making it suitable for the PCA algorithm is as important as applying the algorithm itself.

Python 131

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Pickl AI

JULY 22, 2025

It enables systems to automatically learn and improve from experience without being explicitly programmed. Machine learning can be categorized as: Supervised learning : Uses labeled data to train models for prediction or classification tasks. What are some artificial intelligence techniques examples?

Artificial Intelligence 52

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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. In the context of SVMs, it serves as the decision boundary that separates different classes of data, allowing for distinct classifications in supervised learning.

Support Vector Machines 91

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Hacker News

JUNE 14, 2025

We know that model learning is scalable, because its just supervised learning. So why dont we combine the two, where we first learn a model and run on-policy RL within the model? Would model-based RL indeed scale better than TD-based Q-learning? We also know that on-policy RL is scalable.

Algorithm 177

Algorithm Supervised Learning Machine Learning Machine Learning 177

Pickl AI

APRIL 13, 2025

Classification is a subset of supervised learning, where labelled data guides the algorithm to make predictions. Classifiers are algorithms designed to perform this task efficiently, helping industries solve problems like spam detection, fraud prevention, and medical diagnosis.

Machine Learning 52

Machine Learning Machine Learning Decision Trees K-nearest Neighbors 52

Pickl AI

FEBRUARY 17, 2025

The primary types of learning approaches include: Supervised Learning In this approach, the model is trained using labelled data, where the input-output pairs are provided. The goal is to learn a mapping from inputs to outputs, allowing the model to make predictions on unseen data. predicting house prices).

Machine Learning 52

Machine Learning Machine Learning Supervised Learning ML 52

Dataconomy

JUNE 16, 2025

Machine learning Structured data is crucial in machine learning applications. It provides clear and organized datasets essential for training supervised learning models. Data management SQL databases and tools like Excel frequently utilize structured data for efficient business intelligence and data tracking.

Database 91

Database Data Lakes ETL Natural Language Processing 91

Pickl AI

JULY 22, 2025

Training on Massive Datasets LLMs are trained using unsupervised or semi-supervised learning on huge text corpora, including books, websites, code, news, and forums. Training on diverse and large-scale data enables the model to learn grammar, facts, styles, reasoning, and even world knowledge.

Data Science 52

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Flipboard

JUNE 13, 2025

To excel in ML, you must understand its key methodologies: Supervised Learning: Involves training models on labeled datasets for tasks like classification (e.g., spam detection) and regression (e.g., predicting housing prices).

Artificial Intelligence 70

Artificial Intelligence Artificial Intelligence Exploratory Data Analysis Machine Learning 70

Towards AI

JULY 9, 2025

This is in contrast to the previous step of supervised learning, where the model is shown explicit pairs of inputs and ideal outputs and learns to mimic them directly. It doesn’t learn which answers are better than others — just how to replicate the provided ones.

Supervised Learning 60

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AWS Machine Learning Blog

JANUARY 31, 2025

These models are trained using self-supervised learning algorithms on expansive datasets, enabling them to capture a comprehensive repertoire of visual representations and patterns inherent within pathology images.

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NYU Center for Data Science

JUNE 11, 2025

CDS Assistant Professor Mengye Ren and collaborators have developed a new self-supervised learning method that tackles this challenge. The research, published as “ PooDLe🐩: Pooled and Dense Self-Supervised Learning from Naturalistic Videos ” at ICLR 2025 , addresses a fundamental problem in computer vision.

Supervised Learning 95

Supervised Learning AI AI Algorithm 95

Towards AI

FEBRUARY 13, 2025

Meme shared by bin4ry_d3struct0r TAI Curated section Article of the week Reinforcement Learning-Driven Adaptive Model Selection and Blending for Supervised Learning By Shenggang Li This article discusses a novel framework for adaptive model selection and blending in supervised learning inspired by reinforcement learning (RL) techniques.

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