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

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.

Supervised Learning 98

Supervised Learning Deep Learning Deep Learning Algorithm 98

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.”

Supervised Learning 98

Supervised Learning Deep Learning Deep Learning Algorithm 98

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

Deep Learning 357

Deep Learning Deep Learning Data Science Analytics 357

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.

Supervised Learning 147

Supervised Learning Deep Learning Deep Learning 147

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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Deep Learning Deep Learning Supervised Learning Algorithm 91

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.

Natural Language Processing 271

Natural Language Processing Deep Learning Deep Learning Analytics 271

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.

Supervised Learning 75

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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.

Supervised Learning 75

Supervised Learning Deep Learning Deep Learning Algorithm 75

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.

Machine Learning 195

Machine Learning Machine Learning Natural Language Processing Deep Learning 195

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?

Deep Learning 71

Deep Learning Deep Learning Machine Learning Machine Learning 71

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.

Supervised Learning 52

Supervised Learning Deep Learning Deep Learning K-nearest Neighbors 52

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.

Deep Learning 52

Deep Learning Deep Learning Machine Learning Machine Learning 52

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.

Deep Learning 52

Deep Learning Deep Learning Machine Learning Machine Learning 52

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.

Deep Learning 52

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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.

Deep Learning 52

Deep Learning Deep Learning Machine Learning Machine Learning 52

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.

Deep Learning 52

Deep Learning Deep Learning Cross Validation ML 52

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.

Data Science 133

Data Science Exploratory Data Analysis Machine Learning Machine Learning 133

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 Machine Learning Deep Learning Deep Learning 96

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.

Supervised Learning 130

Supervised Learning Algorithm Deep Learning Deep Learning 130

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.

Deep Learning 128

Deep Learning Deep Learning AI AI 128

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

Deep Learning Deep Learning Natural Language Processing Supervised Learning 52

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.

Machine Learning 105

Machine Learning Machine Learning K-nearest Neighbors Supervised Learning 105

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

Deep Learning 172

Deep Learning Deep Learning Supervised Learning AI 172

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?”

Deep Learning 52

Deep Learning Deep Learning Machine Learning Machine Learning 52

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.

Predictive Analytics 208

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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.

Deep Learning 40

Deep Learning Deep Learning Machine Learning Machine Learning 40

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.

Deep Learning 52

Deep Learning Deep Learning Supervised Learning Algorithm 52

Towards AI

AUGUST 29, 2023

Introducing the backbone of Reinforcement Learning — The Markov Decision Process This member-only story is on us. Image by Ricardo Gomez Angel on Unsplash In most of my previous articles, I have mostly discussed Supervised Learning, with some sprinkling of elements of Unsupervised Learning.

Supervised Learning 105

Supervised Learning Machine Learning Machine Learning AI 105

BAIR

JULY 10, 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.

Supervised Learning 40

Supervised Learning Deep Learning Deep Learning Algorithm 40

Dataconomy

APRIL 18, 2023

Undetectable backdoors can be implemented in any ML algorithm Machine learning Machine learning is a subfield of artificial intelligence that focuses on the development of algorithms and models that can learn from data and make predictions or decisions.

Artificial Intelligence 113

Artificial Intelligence Artificial Intelligence Machine Learning Machine Learning 113

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.

Machine Learning 192

Machine Learning Machine Learning Algorithm ML 192

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. Transfer learning and better annotation tooling are both key to our current plans for spaCy and related projects.

Supervised Learning 40

Supervised Learning Machine Learning Machine Learning Algorithm 40

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.

ML 233

ML ML Machine Learning Machine Learning 233

IBM Journey to AI blog

DECEMBER 20, 2023

Machine learning types Machine learning algorithms fall into five broad categories: supervised learning, unsupervised learning, semi-supervised learning, self-supervised and reinforcement learning. the target or outcome variable is known). temperature, salary).

Machine Learning 92

Machine Learning Machine Learning Supervised Learning Clustering 92

Data Science Dojo

JULY 2, 2024

Machine Learning Algorithms : These algorithms allow AI systems to learn from data and make predictions or decisions based on their learning. Machine learning is categorized into three main types: Supervised Learning : This is where the system receives labeled data and learns to map input data to known outputs.

AI 195

AI AI Algorithm Machine Learning 195

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