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.

Supervised Learning 446

Supervised Learning Machine Learning Machine Learning Artificial Intelligence 446

NYU Center for Data Science

NOVEMBER 3, 2023

Self-supervised learning (SSL) has emerged as a powerful technique for training deep neural networks without extensive labeled data. However, unlike supervised learning, where labels help identify relevant information, the optimal SSL representation heavily depends on assumptions made about the input data and desired downstream task.

Supervised Learning 98

Supervised Learning Deep Learning Deep Learning Algorithm 98

Dataconomy

APRIL 24, 2025

In machine learning, few ideas have managed to unify complexity the way the periodic table once did for chemistry. Now, researchers from MIT, Microsoft, and Google are attempting to do just that with I-Con, or Information Contrastive Learning. It all boils down to preserving certain relationships while simplifying others.

Machine Learning 187

Machine Learning Machine Learning Clustering Algorithm 187

Data Science Dojo

MAY 10, 2024

Stage 2: Introduction of neural networks The next step for LLM embeddings was the introduction of neural networks to capture the contextual information within the data. SOMs work to bring down the information into a 2-dimensional map where similar data points form clusters, providing a starting point for advanced embeddings.

Supervised Learning 259

Supervised Learning Clustering ML ML 259

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. By incorporating insights from human experts, HITL contributes to more accurate predictions and informed decision-making.

Machine Learning 91

Machine Learning Machine Learning Supervised Learning Natural Language Processing 91

Dataconomy

APRIL 29, 2025

A keen awareness of where a model lies on the bias-variance spectrum can lead to more informed decisions during the modeling process. Types of errors in machine learning Beyond bias and variance, specific types of errors characterize model performance issues. What is underfitting?

Cross Validation 91

Cross Validation Supervised Learning Machine Learning Machine Learning 91

NYU Center for Data Science

SEPTEMBER 13, 2024

The world of multi-view self-supervised learning (SSL) can be loosely grouped into four families of methods: contrastive learning, clustering, distillation/momentum, and redundancy reduction. This work not only brings new theoretical insights but also introduces practical tools to optimize self-supervised models.

Supervised Learning 75

Supervised Learning Clustering Machine Learning Machine Learning 75

Flipboard

MAY 11, 2025

As machine learning continues to reshape the financial services industry, most headlines are dominated by breakthroughs in supervised learning. These include fraud detection models trained on labeled transactions or credit scoring systems built from years of historical repayment data. But behind

Supervised Learning 111

Supervised Learning Machine Learning Machine Learning 111

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.

K-nearest Neighbors 338

K-nearest Neighbors Supervised Learning AI AI 338

Dataconomy

MARCH 11, 2025

Linear regression stands out as a foundational technique in statistics and machine learning, providing insights into the relationships between variables. This method enables analysts and practitioners to create predictive models that can inform decision-making across many fields. What is linear regression?

Supervised Learning 91

Supervised Learning Machine Learning Machine Learning Exploratory Data Analysis 91

Data Science Dojo

MAY 10, 2024

Stage 2: Introduction of neural networks The next step for LLM embeddings was the introduction of neural networks to capture the contextual information within the data. SOMs work to bring down the information into a 2-dimensional map where similar data points form clusters, providing a starting point for advanced embeddings.

Supervised Learning 195

Supervised Learning Clustering ML ML 195

Machine Learning Research at Apple

NOVEMBER 17, 2024

This paper was accepted at the Self-Supervised Learning - Theory and Practice (SSLTP) Workshop at NeurIPS 2024. Image-based Joint-Embedding Predictive Architecture (IJEPA) offers an attractive alternative to Masked Autoencoder (MAE) for representation learning using the Masked Image Modeling framework.

Supervised Learning 130

Supervised Learning 130

Data Science Dojo

SEPTEMBER 5, 2023

Moreover, these approaches integrate external processes to influence token generation by modifying the contextual information. A Paradigm Shift in AI Reasoning AoT marks a notable shift away from traditional supervised learning by integrating the search process itself.

Algorithm 415

Algorithm AI AI Supervised Learning 415

Data Science Dojo

JANUARY 30, 2025

Knowledge Distillation is a machine learning technique where a teacher model (a large, complex model) transfers its knowledge to a student model (a smaller, efficient model). Now, it is time to train the teacher model on the dataset using standard supervised learning.

AI 195

AI AI Natural Language Processing Python 195

Google Research AI blog

JULY 18, 2023

Alternatively, self-supervised learning (SSL) methods (e.g., SimCLR and MoCo v2 ), which leverage a large amount of unlabeled data to learn representations that capture periodic or quasi-periodic temporal dynamics, have demonstrated success in solving classification tasks. video or satellite imagery).

Supervised Learning 70

Supervised Learning Machine Learning Machine Learning 70

How to Learn Machine Learning

MARCH 25, 2025

At the core of machine learning, two primary learning techniques drive these innovations. These are known as supervised learning and unsupervised learning. Supervised learning and unsupervised learning differ in how they process data and extract insights.

Supervised Learning 59

Supervised Learning Machine Learning Machine Learning Algorithm 59

IBM Data Science in Practice

MARCH 17, 2025

From Data Cubes to Embeddings During the development phase in March, participants will pretrain their encoders using self-supervised learning methods that underpin neural compression and EO foundation models. Link to the challenge and more information : Challenge PortalEval.AI

AI 130

AI AI Supervised Learning Data Science 130

Data Science Dojo

APRIL 25, 2025

It takes in data, makes sense of it, and uses that information to plan its next move. Its about creating AI that does not just do, but thinks, learns, and acts on its own. It allows developers to easily create and manage systems where multiple AI agents can communicate, share information, and delegate tasks to each other.

AI 342

AI AI Supervised Learning Algorithm 342

Dataconomy

APRIL 16, 2025

What is clustering in machine learning? Clustering is a subset of unsupervised learning where the goal is to categorize a set of objects into groups based on their similarities. Unlike supervised learning, which relies on labeled training data, clustering algorithms identify inherent structures within the data.

Clustering 91

Clustering Machine Learning Machine Learning Supervised Learning 91

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.

Supervised Learning 195

Supervised Learning Machine Learning Machine Learning Artificial Intelligence 195

Google Research AI blog

APRIL 26, 2023

These models are trained using data at scale, often by self-supervised learning. This process results in generalist models that can rapidly be adapted to new tasks and environments with less need for supervised data. The specific approach used for pre-training and learning representations is SimCLR.

Supervised Learning 130

Supervised Learning ML ML AI 130

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

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.

Predictive Analytics 208

Predictive Analytics ML ML Analytics 208

Dataconomy

APRIL 24, 2025

By allowing algorithms to learn autonomously, it opens the door to various innovative applications across different fields. From organizing vast datasets to finding similarities among complex information, unsupervised learning plays a pivotal role in enhancing decision-making processes and operational efficiencies.

Clustering 91

Clustering Machine Learning Machine Learning Algorithm 91

Data Science Dojo

AUGUST 19, 2024

Let’s dig into some of the most asked interview questions from AI Scientists with best possible answers Core AI Concepts Explain the difference between supervised, unsupervised, and reinforcement learning. The model learns to map input features to output labels.

AI 364

AI AI Machine Learning Machine Learning 364

Machine Learning Research at Apple

JULY 23, 2023

Multiview Self-Supervised Learning (MSSL) is based on learning invariances with respect to a set of input transformations. However, invariance partially or totally removes transformation-related information from the representations, which might harm performance for specific downstream tasks that require such information.

Supervised Learning 100

Supervised Learning 100

Towards AI

APRIL 8, 2024

With the use of machine learning, people find out about the 2 main types of machine learning: Supervised and Unsupervised learning. Supervised Learning First, what exactly is supervised learning? It is the most common type of machine learning that you will use. Let’s get right into it.

Supervised Learning 105

Supervised Learning Machine Learning Machine Learning Clustering 105

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.

Machine Learning 136

Machine Learning Machine Learning Supervised Learning Algorithm 136

Dataconomy

MARCH 25, 2025

Characteristics of KNN Supervised learning: KNN is a supervised learning algorithm that requires labeled training data to work effectively. Understanding these can help professionals make informed decisions on when to use this algorithm.

K-nearest Neighbors 91

K-nearest Neighbors Supervised Learning Machine Learning Machine Learning 91

Dataconomy

APRIL 14, 2025

Instance-based learning (IBL) is a fascinating approach within the realm of machine learning that emphasizes the importance of individual data points rather than abstracting information into generalized models. This method allows systems to utilize specific historical examples to inform predictions about new instances.

Machine Learning 91

Machine Learning Machine Learning Algorithm Supervised Learning 91

Machine Learning Research at Apple

MARCH 6, 2023

Recent Self-Supervised Learning (SSL) methods are able to learn feature representations that are invariant to different data augmentations, which can then be transferred to downstream tasks of interest. In this paper, we aim to learn self-supervised features that generalize well across a variety of downstream tasks (e.g.,

Supervised Learning 100

Supervised Learning 100

Dataconomy

SEPTEMBER 13, 2023

Imagine a world where computers can’t interpret the visual information around them without a little human assistance. These labels provide crucial context for machine learning models, enabling them to make informed decisions and predictions. That’s where data annotation comes into play.

Machine Learning 195

Machine Learning Machine Learning AI AI 195

Dataconomy

MARCH 10, 2025

What is ground truth in machine learning? Ground truth in machine learning refers to the precise, labeled data that provides a benchmark for various algorithms. This accurate information is essential for ensuring the performance of predictive models, which learn from existing data to make future predictions.

Machine Learning 91

Machine Learning Machine Learning Algorithm Cross Validation 91

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

Support Vector Machines Decision Trees Supervised Learning Machine Learning 91

Dataconomy

MARCH 5, 2025

Input and learning process To begin learning, DeepMind systems take in raw data, often in the form of pixel information. Deep learning techniques The division employs deep learning methodologies, particularly convolutional neural networks (CNNs), which excel at recognizing patterns.

Deep Learning 91

Deep Learning Deep Learning Artificial Intelligence Artificial Intelligence 91

Pickl AI

FEBRUARY 17, 2025

It has significantly impacted industries like finance, healthcare, and transportation by analysing data, making predictions, and automating decisions Predictive Modelling Machine Learning algorithms excel at predictive modelling, which involves using historical data to create models that forecast future events. predicting house prices).

Machine Learning 52

Machine Learning Machine Learning Supervised Learning ML 52

Towards AI

JANUARY 11, 2024

In this article, I’ll guide you through your first training session on a Machine Learning Algorithm: we’ll be training… pub.towardsai.net Classification and Regression fall under Supervised Learning, a category in Machine Learning where we have prior knowledge of the target variable.

Machine Learning 130

Machine Learning Machine Learning Decision Trees Supervised Learning 130