Towards AI

MAY 1, 2024

Last Updated on May 1, 2024 by Editorial Team Author(s): Stephen Chege-Tierra Insights Originally published on Towards AI. Created by the author with DALL E-3 R has become very ideal for GIS, especially for GIS machine learning as it has topnotch libraries that can perform geospatial computation.

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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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Towards AI

FEBRUARY 20, 2024

Last Updated on February 20, 2024 by Editorial Team Author(s): Vaishnavi Seetharama Originally published on Towards AI. Beginner’s Guide to ML-001: Introducing the Wonderful World of Machine Learning: An Introduction Everyone is using mobile or web applications which are based on one or other machine learning algorithms.

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Towards AI

APRIL 8, 2024

Last Updated on April 8, 2024 by Editorial Team Author(s): Eashan Mahajan Originally published on Towards AI. Photo by Arseny Togulev on Unsplash With machine learning’s surge of popularity in the past few years, more and more people spend hours each day trying to learn as much as they can. Let’s get right into it.

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Dataconomy

APRIL 27, 2022

Contrary to popular belief, the history of machine learning, which enables machines to learn tasks for which they are not specifically programmed, and train themselves in unfamiliar environments, goes back to 17th century. Machine learning is a powerful tool for implementing artificial intelligence technologies.

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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.

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ODSC - Open Data Science

JULY 26, 2023

As a senior data scientist, I often encounter aspiring data scientists eager to learn about machine learning (ML). In this comprehensive guide, I will demystify machine learning, breaking it down into digestible concepts for beginners. What is Machine Learning? predicting house prices).

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Mlearning.ai

OCTOBER 2, 2023

Learn how to use them to avoid the biggest scare in ML: overfitting and underfitting. Photo by Arseny Togulev on Unsplash If you’re working with a dataset and trying to build a machine learning model, you probably don’t need all the data and columns that feed into your model. Here’s the overview.

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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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Dataconomy

SEPTEMBER 13, 2023

AI annotation jobs are on the rise; naturally, people started asking what exactly is data annotation. AI annotation jobs: What is data annotation? These labels provide crucial context for machine learning models, enabling them to make informed decisions and predictions. Image Credit ) Why does data annotation matter?

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Mlearning.ai

APRIL 18, 2023

Understanding the DINOv2 Model, its Advantages, and its Applications in Computer Vision Introduction : Meta AI, has recently open-sourced DINOv2, a self-supervised learning method for training computer vision models. References: Meta AI Blog BECOME a WRITER at MLearning.ai What is DINOv2?

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Analytics Vidhya

SEPTEMBER 27, 2023

Introduction In recent years, the integration of Artificial Intelligence (AI), specifically Natural Language Processing (NLP) and Machine Learning (ML), has fundamentally transformed the landscape of text-based communication in businesses.

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Data Science Dojo

FEBRUARY 19, 2024

As part of the generative AI world, LLMs have led to innovation in machine-learning tasks. Roadmap to understanding an LLM project lifecycle Within the realm of generative AI, a project involving large language models can be a daunting task. It requires machine-learning expertise, computational resources, and time.

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Pickl AI

AUGUST 1, 2023

As technology continues to impact how machines operate, Machine Learning has emerged as a powerful tool enabling computers to learn and improve from experience without explicit programming. In this blog, we will delve into the fundamental concepts of data model for Machine Learning, exploring their types.

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Machine Learning Machine Learning Support Vector Machines Decision Trees 52

PyImageSearch

MARCH 30, 2023

If you want a gentle introduction to machine learning for computer vision, you’re in the right spot. Here at PyImageSearch we’ve been helping people just like you master deep learning for computer vision. Also, you might want to check out our computer vision for deep learning program before you go.

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Analytics Vidhya

APRIL 19, 2023

Meta AI has announced the launch of DinoV2, an open-source, self-supervised learning model. It is a vision transformer model for computer vision tasks, built upon the success of its predecessor, DINO.

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Pickl AI

AUGUST 6, 2023

Machine Learning has become a fundamental part of people’s lives and it typically holds two segments. It includes supervised and unsupervised learning. Supervised Learning deals with labels data and unsupervised learning deals with unlabelled data. What is Regression in ML? You can join Pickl.AI

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NYU Center for Data Science

FEBRUARY 29, 2024

However, the potential of such technologies is often hindered by biases in the data they learn from. As we increasingly rely on general-purpose, self-supervised learning (SSL) pre-trained foundation models across various tasks, the imperative to ensure these models are fair becomes paramount.

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ODSC - Open Data Science

FEBRUARY 10, 2023

8 Potential Technosignatures Found Beyond Our Solar System Thanks to Machine Learning With the aid of machine learning, 8 potential technosignatures have been found around five nearby stars. What worked best was an algorithm that combined two sub-fields of machine learning, supervised learning, and unsupervised learning.

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Dataconomy

MARCH 3, 2023

It’s really important to explore the potential of machine learning in asset pricing. In recent years, machine learning has emerged as a promising tool for improving asset pricing models in finance. As a result, financial analysts have turned to machine learning as a promising tool for improving asset pricing models.

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Dataconomy

MAY 29, 2023

In the field of AI and ML, QR codes are incredibly helpful for improving predictive analytics and gaining insightful knowledge from massive data sets. So let’s start with the understanding of QR Codes, Artificial intelligence, and Machine Learning.

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Towards AI

AUGUST 29, 2023

Last Updated on August 30, 2023 by Editorial Team Author(s): Tan Pengshi Alvin Originally published on Towards AI. Introducing the backbone of Reinforcement Learning — The Markov Decision Process This member-only story is on us. Let’s first start with a broad overview of Machine Learning. Published via Towards AI

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IBM Journey to AI blog

JULY 6, 2023

While data science and machine learning are related, they are very different fields. In a nutshell, data science brings structure to big data while machine learning focuses on learning from the data itself. What is machine learning? This post will dive deeper into the nuances of each field.

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Dataconomy

APRIL 18, 2023

With the rise of AI-generated art and AI-powered chatbots like ChatGPT, it’s clear that artificial intelligence has become a ubiquitous part of our daily lives. These cutting-edge technologies have captured the public imagination, fueling speculation about the future of AI and its impact on society.

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Heartbeat

OCTOBER 11, 2023

This process is known as machine learning or deep learning. Two of the most well-known subfields of AI are machine learning and deep learning. What is Machine Learning? Machine learning algorithms can make predictions or classifications based on input data.

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Mlearning.ai

FEBRUARY 17, 2023

A complete explanation of the most widely practical and efficient field, that nowadays has an impact on every industry Photo by Thomas T on Unsplash Machine learning has become one of the most rapidly evolving and popular fields of technology in recent years. In this article, I will cover all of them. BECOME a WRITER at MLearning.ai

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Machine Learning Machine Learning K-nearest Neighbors Clustering 52

Pickl AI

JUNE 2, 2023

One of the most popular algorithms in Machine Learning are the Decision Trees that are useful in regression and classification tasks. Decision trees are easy to understand, and implement therefore, making them ideal for beginners who want to explore the field of Machine Learning. What is Decision Tree in Machine Learning?

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Dataconomy

OCTOBER 5, 2023

If you are interested in technology at all, it is hard not to be fascinated by AI technologies. Whether it’s pushing the limits of creativity with its generative abilities or knowing our needs better than us with its advanced analysis capabilities, many sectors have already taken a slice of the huge AI pie.

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Flipboard

FEBRUARY 22, 2023

One rarely gets to engage in a conversation with an individual like Andrew Ng, who has left an indelible impact as an educator, researcher, innovator and leader in the artificial intelligence and technology realms. Fortunately, I recently had the privilege of doing so. Our article detailing the …

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Towards AI

JULY 22, 2023

Last Updated on July 24, 2023 by Editorial Team Author(s): Cristian Originally published on Towards AI. This is similar to how machine learning (ML) can seem at first. In today’s post, we’re going to decode ten of the most common machine learning terms. Do you remember the first time you tried to follow a recipe?

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Snorkel AI

FEBRUARY 27, 2024

The three main phases are: self-supervised learning supervised learning reinforcement learning. I recently gave a talk at Snorkel AI’s second Enterprise LLM Summit about the problems that can surface when the data for these three labels is not properly aligned. I’ve summarized the main points below.

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Supervised Learning Data Scientist Machine Learning Machine Learning 59

Towards AI

FEBRUARY 15, 2024

Author(s): Towards AI Editorial Team Originally published on Towards AI. Good morning, fellow AI enthusiasts! This week’s podcast episode is extremely useful if you are a student or want to switch to the AI space. I hope you enjoy this week’s iteration of the newsletter and that you learn at least one new thing!

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Dataversity

DECEMBER 16, 2022

2022 was a big year for AI, and we’ve seen significant advancements in various areas – including natural language processing (NLP), machine learning (ML), and deep learning. Unsupervised and self-supervised learning are making ML more accessible by lowering the training data requirements.

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Towards AI

FEBRUARY 13, 2024

Author(s): Louis-François Bouchard Originally published on Towards AI. Louis-François Bouchard in What is Artificial Intelligence Introduction to self-supervised learning·4 min read·May 27, 2020 80 … Read the full blog for free on Medium. Join thousands of data leaders on the AI newsletter.

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Snorkel AI

FEBRUARY 27, 2024

The three main phases are: self-supervised learning supervised learning reinforcement learning. I recently gave a talk at Snorkel AI’s second Enterprise LLM Summit about the problems that can surface when the data for these three labels is not properly aligned. I’ve summarized the main points below.

Supervised Learning 52

Supervised Learning Data Scientist Machine Learning Machine Learning 52

Dataconomy

MAY 25, 2023

Simultaneously, artificial intelligence has revolutionized the way machines learn, reason, and make decisions. On the other hand, artificial intelligence is the simulation of human intelligence in machines that are programmed to think and learn like humans.

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Pickl AI

JULY 31, 2023

Machine Learning has revolutionized various industries, from healthcare to finance, with its ability to uncover valuable insights from data. Among the different learning paradigms in Machine Learnin g, “Eager Learning” and “Lazy Learning” are two prominent approaches.

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