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. Rudner, among others, and “ To Compress or Not to Compress — Self-Supervised Learning and Information Theory: A Review.”

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Dataconomy

APRIL 17, 2025

Image-to-image translation is a fascinating area of generative AI that harnesses advanced algorithms to transform existing images into new forms while retaining essential characteristics. Understanding generative AI Generative AI encompasses a range of algorithms designed to create new content based on pre-existing data.

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Dataconomy

MARCH 5, 2025

is dedicated to creating systems that can learn and adapt, a fundamental step toward achieving General-Purpose Artificial Intelligence (AGI). Founded in 2010, it has made significant strides since its acquisition by Google in 2014, aiming to advance AI capabilities in diverse domains. This ambitious division of Alphabet, Inc.

Deep Learning 91

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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. These algorithms allow AI systems to recognize patterns, forecast outcomes, and adjust to new situations.

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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. This behavior appears to contradict the classical bias-variance tradeoff, which traditionally suggests a U-shaped error curve.

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How to Learn Machine Learning

MAY 7, 2025

In the world of AI, you might hear a lot of Machine Learning vs Deep Learning. Introduction to Deep Learning vs Machine Learning To a lot of people, the terms Deep Learning and Machine Learning seem like buzzwords in the AI world. What is Machine Learning?

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Towards AI

SEPTEMBER 7, 2023

Last Updated on September 8, 2023 by Editorial Team Author(s): Louis Bouchard Originally published on Towards AI. An analogy to explain how deep learning works… This member-only story is on us. link] When we talk about artificial intelligence, or AI, we tend to mean deep learning. Published via Towards AI

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Data Science Dojo

JULY 2, 2024

Adaptive AI has risen as a transformational technological concept over the years, leading Gartner to name it as a top strategic tech trend for 2023. It is a step ahead within the realm of artificial intelligence (AI). As the use of AI has expanded into various arenas of the world, the technology has also developed over time.

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IBM Journey to AI blog

JULY 6, 2023

While artificial intelligence (AI), machine learning (ML), deep learning and neural networks are related technologies, the terms are often used interchangeably, which frequently leads to confusion about their differences. Machine learning is a subset of AI. What is artificial intelligence (AI)?

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Data Science Dojo

JUNE 1, 2023

Machine learning courses Top free machine learning courses Here are 9 free machine learning courses from top universities that you can take online to upgrade your skills: 1. The course covers topics such as supervised learning, unsupervised learning, and reinforcement learning.

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Dataconomy

FEBRUARY 26, 2025

Artificial intelligence (AI): It enables machines to learn from data, improving decision-making and automation. Relation of pattern recognition to AI and machine learning Pattern recognition is a vital subset of machine learning and AI.

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Pickl AI

AUGUST 1, 2024

Summary : Deep Learning engineers specialise in designing, developing, and implementing neural networks to solve complex problems. Introduction Deep Learning engineers are specialised professionals who design, develop, and implement Deep Learning models and algorithms.

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Pickl AI

FEBRUARY 7, 2025

Summary: Machine Learning and Deep Learning are AI subsets with distinct applications. Understanding their differences helps choose the right approach for AI-driven innovations across various industries. Choose ML for structured data and interpretability; use DL for large-scale automation and deep insights.

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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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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., 2022 Deep learning notoriously needs a lot of data in training.

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Dataconomy

MAY 25, 2023

By harnessing the power of AI in IoT, we can create intelligent ecosystems where devices seamlessly communicate, collaborate, and make intelligent choices to improve our lives. Let’s explore the fascinating intersection of these two technologies and understand how AI enhances the functionalities of IoT.

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Dataconomy

MARCH 28, 2025

LLMs are advanced AI systems that leverage machine learning to understand and generate natural language. By using deep learning and large datasets, LLMs can mimic human language patterns, providing coherent and contextually relevant outputs. Llama series Meta AI’s Llama series (e.g.,

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NYU Center for Data Science

MAY 31, 2023

CDS Assistant Professor/Faculty Fellow Jacopo Cirrone works at the intersection of machine learning and healthcare, recently publishing two papers that expand deep learning research within these fields. To learn more about data science’s future in medical imaging and healthcare, CDS spoke with Jacopo.

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Becoming Human

MARCH 16, 2023

AI drug discovery is exploding. Overhyped or not, investments in AI drug discovery jumped from $450 million in 2014 to a whopping $58 billion in 2021. AI has already helped identify promising candidate therapeutics, and it didn’t take years but months or even days. We will look at success stories, AI benefits, and limitations.

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Flipboard

MARCH 13, 2023

Yes, large language models (LLMs) hallucinate , a concept popularized by Google AI researchers in 2018. That feedback is used to adjust the reward predictor neural network, and the updated reward predictor neural network is used to adjust the behavior of the AI model. Most of what we learn has nothing to do with language.” “We

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Smart Data Collective

JUNE 4, 2021

Some of the applications of data science are driverless cars, gaming AI, movie recommendations, and shopping recommendations. Learning the various categories of machine learning, associated algorithms, and their performance parameters is the first step of machine learning. In supervised learning, a variable is predicted.

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Towards AI

APRIL 7, 2024

Author(s): Stephen Chege-Tierra Insights Originally published on Towards AI. You just want to create and analyze simple maps not to learn algebra all over again. This function can be improved by AI and ML, which allow GIS to produce insights, automate procedures, and learn from data. Types of Machine Learning for GIS 1.

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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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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. Supervised, unsupervised, and reinforcement learning : Machine learning can be categorized into different types based on the learning approach.

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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. Join thousands of data leaders on the AI newsletter. Published via Towards AI

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AssemblyAI

MAY 17, 2023

Generative AI has made great strides in the language domain. GPT-4’s performance on various example compared to GPT-3.5 ( source ) These Generative AI models are progressively migrating from the ivory tower and finding themselves integrated into our everyday lives through tools like Microsoft’s Copilot.

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Pickl AI

SEPTEMBER 16, 2024

Summary : Deep Belief Networks (DBNs) are Deep Learning models that use Restricted Boltzmann Machines and feedforward networks to learn hierarchical features and model complex data distributions. What is a Deep Belief Network (DBN)? They are effective in image recognition, NLP, and speech recognition.

Deep Learning 52

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IBM Journey to AI blog

MAY 9, 2023

We stand on the frontier of an AI revolution. Over the past decade, deep learning arose from a seismic collision of data availability and sheer compute power, enabling a host of impressive AI capabilities. It sounds like a joke, but it’s not, as anyone who has tried to solve business problems with AI may know.

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Dataconomy

MARCH 6, 2025

Overview of machine learning algorithms Two primary approaches in machine learning are: Supervised learning: Algorithms learn from labeled data to make predictions on new, unseen data. Unsupervised learning: These algorithms identify patterns within unlabeled data, seeking to discover hidden structures.

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Dataconomy

MAY 4, 2023

Machine learning applications in healthcare are revolutionizing the way we approach disease prevention and treatment Machine learning is broadly classified into three categories: supervised learning, unsupervised learning, and reinforcement learning.

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Pickl AI

NOVEMBER 28, 2024

Summary: Generative Adversarial Network (GANs) in Deep Learning generate realistic synthetic data through a competitive framework between two networks: the Generator and the Discriminator. In answering the question, “What is a Generative Adversarial Network (GAN) in Deep Learning?”

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IBM Journey to AI blog

DECEMBER 20, 2023

That’s why diversifying enterprise AI and ML usage can prove invaluable to maintaining a competitive edge. What is machine learning? ML is a computer science, data science and artificial intelligence (AI) subset that enables systems to learn and improve from data without additional programming interventions.

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Mlearning.ai

JULY 11, 2023

Deep learning has transformed artificial intelligence, allowing machines to learn and make smart decisions. If you’re interested in exploring deep learning, this step-by-step guide will help you learn the basics and develop the necessary skills. Also, learn about common algorithms used in machine learning.

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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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Explosion

APRIL 1, 2017

Prodigy features many of the ideas and solutions for data collection and supervised learning outlined in this blog post. It’s a cloud-free, downloadable tool and comes with powerful active learning models. This post is based on a talk I gave at “How AI Will Shape the Future of Work” at Founders in Copenhagen in January.

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Dataconomy

MAY 22, 2023

There are various types of machine learning algorithms, including supervised learning, unsupervised learning, and reinforcement learning. In supervised learning, the model learns from labeled examples, where the input data is paired with corresponding target labels.

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Hacker News

MARCH 25, 2022

If you want to ride the next big wave in AI, grab a transformer. A transformer model is a neural network that learns context and thus meaning by tracking relationships in sequential data like the words in this sentence. They’re driving a wave of advances in machine learning some have dubbed transformer AI.

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