Dataconomy

FEBRUARY 21, 2025

Using deep learning and transformer-based models, SparkAI processes extensive audio datasets to analyze tonal characteristics and generate realistic guitar sounds. The system applies self-supervised learning techniques, allowing it to adapt to different playing styles without requiring manually labeled training data.

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Data Science Blog

FEBRUARY 23, 2023

1, Data is the new oil, but labeled data might be closer to it Even though we have been in the 3rd AI boom and machine learning is showing concrete effectiveness at a commercial level, after the first two AI booms we are facing a problem: lack of labeled data or data themselves. That is, is giving supervision to adjust via.

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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.

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

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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.

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Analytics Vidhya

JULY 13, 2022

Where our task will be to take brain MR images as input and utilize them with deep learning for automatic brain segmentation matured to a level […]. Introduction In this blog, we will try to solve a famously discussed task of Brain MRI segmentation. The post Brain MRI Segmentation with 0.95

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Machine Learning Research at Apple

APRIL 28, 2024

Deep learning models can perform the task but at the expense of large labeled datasets, which are unfeasible to procure at scale. Sleep staging is a clinically important task for diagnosing various sleep disorders but remains challenging to deploy at scale because it requires clinical expertise, among other reasons.

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Analytics Vidhya

DECEMBER 25, 2022

Introduction There have been many recent advances in natural language processing (NLP), including improvements in language models, better representation of the linguistic structure, advancements in machine translation, increased use of deep learning, and greater use of transfer learning.

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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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Dataconomy

APRIL 17, 2025

Frequently leveraging deep learning techniques, this method allows for creative and practical applications across diverse fields, from artistic endeavors to medical imaging. What is image-to-image translation?

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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 Deep Learning?

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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. This blogpost is cross-posted here.

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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). Technology and methodology DeepMind’s approach revolves around sophisticated machine learning methods that enable AI to interact with its environment and learn from experience.

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Data Science Dojo

JUNE 1, 2023

Machine Learning for Absolute Beginners by Kirill Eremenko and Hadelin de Ponteves This is another beginner-level course that teaches you the basics of machine learning using Python. The course covers topics such as supervised learning, unsupervised learning, and reinforcement learning.

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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. How do artificial intelligence, machine learning, deep learning and neural networks relate to each other?

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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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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. Introduction In todays world of AI, both Machine Learning (ML) and Deep Learning (DL) are transforming industries, yet many confuse the two. The model learns from the input-output pairs and predicts outcomes for new data.

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Dataconomy

MARCH 20, 2025

This technological progress has made it feasible to employ deep learning techniques across various fields. Characteristics of backpropagation Backpropagation primarily operates under the framework of supervised learning, where the model is trained on labeled data.

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Heartbeat

AUGUST 29, 2023

Deep learning is a branch of machine learning that makes use of neural networks with numerous layers to discover intricate data patterns. Deep learning models use artificial neural networks to learn from data. Semi-Supervised Learning : Training is done using both labeled and unlabeled data.

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Dataconomy

FEBRUARY 26, 2025

Whether it’s supervised learning, unsupervised learning, or reinforcement learning, pattern recognition plays a role in understanding data structures and relationships. Algorithms can be designed specifically for identifying patterns in data, thus enabling greater functionality and accuracy.

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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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Smart Data Collective

JUNE 4, 2021

Learning the various categories of machine learning, associated algorithms, and their performance parameters is the first step of machine learning. Machine learning is broadly classified into three types – Supervised. In supervised learning, a variable is predicted. Semi-Supervised Learning.

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Mlearning.ai

MARCH 7, 2023

Dive Into Deep Learning — Part 3 In this part, I will summarize section 3.6 Dive Into Deep Learning — Part 2 Dive Into Deep Learning — Part1 Generalization The authors give an example of students who prepare for an exam, student 1 memorizes the past exams questions and student 2 discovers patterns in the questions, if the exam is 1.

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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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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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Towards AI

SEPTEMBER 7, 2023

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. Last Updated on September 8, 2023 by Editorial Team Author(s): Louis Bouchard Originally published on Towards AI. Upgrade to access all of Medium.

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Dataconomy

APRIL 29, 2025

Binary classification is a supervised learning method designed to categorize data into one of two possible outcomes. Additionally, overfitting, where a model learns noise instead of underlying patterns, can lead to poor generalization to unseen data. What is binary classification?

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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.

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Flipboard

MARCH 13, 2023

“I’m quite hopeful that by simply improving this subsequent reinforcement learning from human feedback step, we can teach it to not hallucinate,” said Sutskever, suggesting that the ChatGPT limitations we see today will dwindle as the model improves. Most of what we learn has nothing to do with language.” “We

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Towards AI

APRIL 7, 2024

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. Supervised learning– In supervised learning, the input data and associated output labels are paired, letting the system be trained on labelled data.

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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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Dataconomy

MAY 29, 2023

Machine Learning (ML) serves as a subset of AI, specifically focusing on the development of algorithms and statistical models that enable computers to learn and improve from data without explicit programming. Some of the methods used in ML include supervised learning, unsupervised learning, reinforcement learning, and deep learning.

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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.

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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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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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Heartbeat

FEBRUARY 28, 2023

The past few years have witnessed exponential growth in medical image analysis using deep learning. In this article we will look into medical image segmentation and see how deep learning can be helpful in these cases. This can be further classified as supervised and unsupervised learning. Image by author.

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