U-Time: A Fully Convolutional Network for Time Series Segmentation and Event Detection U-Time Implementation of the U-Time model for time-series segmentation as described in: Mathias Perslev, Michael Hejselbak Jensen, Sune Darkner, Poul Jørgen Jennum, and Christian Igel. U-Time: A Fully Convolutional Network for Time Series Segmentation Applied to Sleep Staging. …

Data augmentation for deep learning The breakthrough of artificial neural networks in the past few years is the most exciting achievement of machine learning in recent decades. The advance of various types of deep learning models provides us with powerful tools to tackle various challenging problems that previously a traditional machine learning model could hardly…

A pythonic framework for experimenting with deep learning models From Youtube: Learn PyTorch for deep learning in a day. Literally. (25 hrs) by Daniel Bourke 128K subscribers PyTorch — Python Deep Learning Neural Network API by deeplizard 124K subscribers From Pytorch's official site https://pytorch.org/resources/ https://pytorch.org/tutorials/

Dataset distillation is a technique used to reduce the size of a dataset while preserving its information content. …

In today’s digital age, the internet has become an indispensable part of our lives. It has revolutionized the way we communicate, access information, and connect with people from around the world. However, with the ease of access to the internet comes the challenge of authenticity and misinformation. Authenticity refers to…

The process of selecting a career path can be overwhelming and intimidating for many people. There are numerous factors to consider, such as interests, skills, personality traits, and job market demand. …

The food industry has been experiencing a rapid transformation in recent years, with technological advancements leading to new ways of producing, preparing, and delivering food. One of the most significant developments in this industry is the integration of Artificial Intelligence (AI) in the kitchen. AI has provided solutions to modern…

Recommender systems are widely used in today's world to recommend products, services, or content to users based on their past behavior or preferences. The traditional approach to building recommender systems involves collaborative filtering, which is a type of unsupervised learning that uses past user-item interactions to predict future preferences. …

Machine learning has revolutionized the way we approach data analysis and decision-making. However, one of the biggest challenges in machine learning is the availability of labeled data. Labeled data is essential for training machine learning models and improving their accuracy. However, in many real-world applications, labeled data is scarce, and…