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

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.

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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

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

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.

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Supervised Learning Machine Learning Machine Learning Clustering 75

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

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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

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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.

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AI AI Supervised Learning Algorithm 342

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

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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

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

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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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.

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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.

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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

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

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

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

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.

Supervised Learning 52

Supervised Learning Deep Learning Deep Learning K-nearest Neighbors 52

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

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

APRIL 29, 2025

Binary classification is a supervised learning method designed to categorize data into one of two possible outcomes. Overview of classification in machine learning Classification serves as a foundational method in machine learning, where algorithms are trained on labeled datasets to make predictions.

Support Vector Machines 91

Support Vector Machines Machine Learning Machine Learning Algorithm 91

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 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

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

Dataconomy

MAY 4, 2023

By analyzing large datasets and recognizing patterns that may not be visible to the human eye, machine learning algorithms can provide unprecedented insights into patient health and enable medical professionals to make more informed decisions. From data to insights: How BI is changing healthcare delivery?

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Machine Learning Machine Learning Algorithm ML 192

Google Research AI blog

MARCH 6, 2023

First, there is a lack of scalability with conventional supervised learning approaches. In contrast, self-supervised learning can leverage audio-only data, which is available in much larger quantities across languages. For the encoder, USM uses the Conformer , or convolution-augmented transformer. CoVoST BLEU score.

Supervised Learning 140

Supervised Learning AI AI Algorithm 140

Data Science Dojo

SEPTEMBER 1, 2023

Understanding fine-tuning, even if not doing it yourself, aids in informed decision-making. This allows them to learn a wide range of tasks, such as text generation, translation, and question-answering. One common approach is to use supervised learning. This process involves retraining LLMs on new data.

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Towards AI

FEBRUARY 13, 2025

Their current capabilities are particularly impactful in routine, repetitive, and information-intensive tasks, while human strengths such as creativity, critical thinking, and emotional intelligence remain indispensable. So here are some thoughts on how different categories of human work could be impacted by LLM.

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AI AI Supervised Learning Python 62

Data Science Dojo

SEPTEMBER 11, 2023

The world we live in relies increasingly on natural language processing (NLP in short) for communication, information retrieval, and decision-making, making the evolution of language models not just a technological advancement but a necessity. If the goal is a creative and informative content generation, Llama 2 is the ideal choice.

Natural Language Processing 195

Natural Language Processing Supervised Learning Algorithm Deep Learning 195

IBM Journey to AI blog

DECEMBER 1, 2023

Foundation models are pre-trained on unlabeled datasets and leverage self-supervised learning using neural network s. The supervised learning that is used to train AI requires a lot of human effort. The introduction of ChatGPT capabilities has generated a lot of interest in generative AI foundation models.

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IBM Journey to AI blog

DECEMBER 19, 2023

In this blog we’ll go over how machine learning techniques, powered by artificial intelligence, are leveraged to detect anomalous behavior through three different anomaly detection methods: supervised anomaly detection, unsupervised anomaly detection and semi-supervised anomaly detection.

Machine Learning 82

Machine Learning Machine Learning Supervised Learning K-nearest Neighbors 82

Towards AI

JULY 22, 2023

We’re educating the computer to learn from data (the equivalent of practice), to make informed predictions (akin to riding the bicycle), and to progressively improve with each iteration. In the context of Machine Learning, data can be anything from images, text, numbers, to anything else that the computer can process and learn from.

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