Data Science Dojo

SEPTEMBER 5, 2023

Virginia Tech and Microsoft unveil the Algorithm of Thoughts, a breakthrough AI method supercharging idea exploration and reasoning prowess in Large Language Models (LLMs). Notably, CoT at times presents inaccuracies in intermediate steps, a shortcoming AoT aims to rectify by leveraging algorithmic examples for enhanced reliability.

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Analytics Vidhya

JANUARY 5, 2022

Types of Machine Learning Algorithms 3. Machine Learning […]. The post Machine Learning Algorithms appeared first on Analytics Vidhya. Introduction 2. Simple Linear Regression 4. Multilinear Regression 5. Logistic Regression 6. Decision Tree 7.

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Analytics Vidhya

NOVEMBER 23, 2021

Introduction to Classification Algorithms In this article, we shall analyze loan risk using 2 different supervised learning classification algorithms. These algorithms are decision trees and random forests. This article was published as a part of the Data Science Blogathon.

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KDnuggets

MARCH 21, 2022

Linear Regression and Logistic Regression are two well-used Machine Learning Algorithms that both branch off from Supervised Learning. Linear Regression is used to solve Regression problems whereas Logistic Regression is used to solve Classification problems. Read more here.

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KDnuggets

JULY 20, 2022

14 Essential Git Commands for Data Scientists • Statistics and Probability for Data Science • 20 Basic Linux Commands for Data Science Beginners • 3 Ways Understanding Bayes Theorem Will Improve Your Data Science • Learn MLOps with This Free Course • Primary Supervised Learning Algorithms Used in Machine Learning • Data Preparation with SQL Cheatsheet. (..)

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KDnuggets

JUNE 20, 2022

Also: Decision Tree Algorithm, Explained; 15 Python Coding Interview Questions You Must Know For Data Science; Naïve Bayes Algorithm: Everything You Need to Know; Primary Supervised Learning Algorithms Used in Machine Learning.

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Analytics Vidhya

SEPTEMBER 20, 2021

This article was published as a part of the Data Science Blogathon Introduction In this article, I am going to discuss the math intuition behind the Gradient boosting algorithm. The post Gradient Boosting Algorithm: A Complete Guide for Beginners appeared first on Analytics Vidhya.

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Analytics Vidhya

MAY 20, 2021

ArticleVideo Book This article was published as a part of the Data Science Blogathon Introduction This article aims to explain deep learning and some supervised. The post Introduction to Supervised Deep Learning Algorithms! appeared first on Analytics Vidhya.

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Analytics Vidhya

MAY 15, 2021

The post K-Nearest Neighbour: The Distance-Based Machine Learning Algorithm. ArticleVideo Book This article was published as a part of the Data Science Blogathon. Introduction The abbreviation KNN stands for “K-Nearest Neighbour” It is. appeared first on Analytics Vidhya.

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Analytics Vidhya

JUNE 3, 2022

Introduction Classification problems are often solved using supervised learning algorithms such as Random Forest Classifier, Support Vector Machine, Logistic Regressor (for binary class classification) etc. This article was published as a part of the Data Science Blogathon.

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Pickl AI

JANUARY 21, 2025

Summary: Machine Learning algorithms enable systems to learn from data and improve over time. These algorithms are integral to applications like recommendations and spam detection, shaping our interactions with technology daily. These intelligent predictions are powered by various Machine Learning algorithms.

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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 27, 2024

A visual representation of generative AI – Source: Analytics Vidhya Generative AI is a growing area in machine learning, involving algorithms that create new content on their own. These algorithms use existing data like text, images, and audio to generate content that looks like it comes from the real world.

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NYU Center for Data Science

JULY 5, 2024

Our study demonstrates that machine supervision significantly improves two crucial medical imaging tasks: classification and segmentation,” said Cirrone, who leads AI efforts at the Colton Center for Autoimmunity at NYU Langone. “The

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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.

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Analytics Vidhya

JUNE 13, 2021

ArticleVideo Book This article was published as a part of the Data Science Blogathon Introduction Speech Recognition is a supervised learning task. In the speech. The post MFCC Technique for Speech Recognition appeared first on Analytics Vidhya.

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Machine Learning Research at Apple

DECEMBER 17, 2023

This paper was accepted at the workshop Self-Supervised Learning - Theory and Practice at NeurIPS 2023. We propose Bootstrap Your Own Variance (BYOV), combining Bootstrap Your Own Latent (BYOL), a negative-free Self-Supervised Learning (SSL) algorithm, with Bayes by Backprop (BBB), a Bayesian method for estimating model posteriors.

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Dataconomy

NOVEMBER 1, 2018

A clever algorithm that has digested seven decades’ worth of articles in China’s state-run media is now ready to predict its future policies. Supervised learning — the most developed form of Machine. The research design of this “crystal ball” can also be applied to tackling a variety of other problems.

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Data Science Dojo

MAY 10, 2024

Unsupervised vs. supervised learning for embeddings While vector representation and contextual inference remain important factors in the evolution of LLM embeddings, the lens of comparative analysis also highlights another aspect for discussion. It involves the different approaches to train embeddings.

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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.

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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. I don’t think it will replace existing algorithms,” Shwartz-Ziv noted.

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Google Research AI blog

FEBRUARY 10, 2023

Robust algorithm design is the backbone of systems across Google, particularly for our ML and AI models. Hence, developing algorithms with improved efficiency, performance and speed remains a high priority as it empowers services ranging from Search and Ads to Maps and YouTube. You can find other posts in the series here.)

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BAIR

JULY 9, 2023

Figure 1: stepwise behavior in self-supervised learning. When training common SSL algorithms, we find that the loss descends in a stepwise fashion (top left) and the learned embeddings iteratively increase in dimensionality (bottom left). Our work finds the analogous results for SSL.

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Towards AI

JULY 3, 2024

When it comes to the three best algorithms to use for spatial analysis, the debate is never-ending. The competition for best algorithms can be just as intense in machine learning and spatial analysis, but it is based more objectively on data, performance, and particular use cases. Also, what project are you working on?

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Machine Learning Research at Apple

FEBRUARY 27, 2023

Self-training (ST) and self-supervised learning (SSL) methods have demonstrated strong improvements in automatic speech recognition (ASR). In this work we aim to analyse the effect of number of speakers in the training data on a recent SSL algorithm (wav2vec 2.0), and a recent ST algorithm (slimIPL).

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Machine Learning Mastery

DECEMBER 31, 2022

Last Updated on January 1, 2023 While a logistic regression classifier is used for binary class classification, softmax classifier is a supervised learning algorithm which is mostly used when multiple classes are involved. Softmax classifier works by assigning a probability distribution to each class.

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Data Science Dojo

MAY 10, 2024

Unsupervised vs. supervised learning for embeddings While vector representation and contextual inference remain important factors in the evolution of LLM embeddings, the lens of comparative analysis also highlights another aspect for discussion. It involves the different approaches to train embeddings.

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Data Science Dojo

FEBRUARY 15, 2023

Ultimately, we can use two or three vital tools: 1) [either] a simple checklist, 2) [or,] the interdisciplinary field of project-management, and 3) algorithms and data structures. In addition to the mindful use of the above twelve elements, our Google-search might reveal that various authors suggest some vital algorithms for data science.

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Towards AI

APRIL 8, 2024

Supervised Learning First, what exactly is supervised learning? It is the most common type of machine learning that you will use. In supervised machine learning, the machine learning algorithm is trained on a labeled dataset. This is where supervised learning would come in handy.

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Data Science Dojo

DECEMBER 28, 2023

Formatting the data in a way that ML algorithms can understand. Model selection and training: Teaching machines to learn With your data ready, it’s time to select an appropriate ML algorithm. Popular choices include: Supervised learning algorithms like linear regression or decision trees for problems with labeled data.

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Data Science Dojo

AUGUST 19, 2024

They dive deep into artificial neural networks, algorithms, and data structures, creating groundbreaking solutions for complex issues. These professionals venture into new frontiers like machine learning, natural language processing, and computer vision, continually pushing the limits of AI’s potential.

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Dataconomy

AUGUST 31, 2023

Multi-class classification in machine learning Multi-class classification in machine learning is a type of supervised learning problem where the goal is to predict one of multiple classes or categories based on input features.

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Hacker News

FEBRUARY 8, 2024

Unlike traditional chess engines that rely on complex heuristics, explicit search, or a combination of both, we train a 270M parameter transformer model with supervised learning on a dataset of 10 million chess games. This paper investigates the impact of training at scale for chess. turbo-instruct.

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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.

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Pickl AI

FEBRUARY 17, 2025

These figures underscore the pressing need for awareness and solutions regarding the challenges faced by Machine Learning professionals. Key Takeaways Data quality is crucial; poor data leads to unreliable Machine Learning models. Algorithmic bias can result in unfair outcomes, necessitating careful management.

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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.

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IBM Data Science in Practice

MARCH 17, 2025

Currently, hand-crafted compression algorithms, often designed for general image data like JPEG2000, are applied. 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.

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