In (supervised) machine learning, specifically when learning from data, there are situations when the data values cannot be modeled. This may arise if there are random fluctuations or measurement errors in the data which are not modeled, and can be appropriately called stochastic noise; or, when the phenomenon being modeled (or learned) is too complex, and so the data contains this added complexity that is not modeled. This added complexity in the data has been called deterministic noise. Though these two types of noise arise from different causes, their adverse effect on learning is similar. The overfitting occurs because the model attempts to fit the (stochastic or deterministic) noise (that part of the data that it cannot model) at the expense of fitting that part of the data which it can model. When either type of noise is present, it is usually advisable to regularize the learning algorithm to prevent overfitting the model to the data and getting inferior performance. Regularization typically results in a lower variance model at the expense of bias. One may also try to alleviate the effects of noise by detecting and removing the noisy training examples prior to training the supervised learning algorithm. There are several algorithms that identify noisy training examples, and removing the suspected noisy training examples prior to training will usually improve the performance. (Wikipedia).
The basic principles using environmental noise from city traffic as an example are explained.
From playlist HOW IT WORKS
Waves 4_2 Sources of Musical Sounds
Problems dealing with musical sounds.
From playlist Physics - Waves
I discuss causal and non-causal noise filters: the moving average filter and the exponentially weighted moving average. I show how to do this filtering in Excel and Python
From playlist Discrete
In this video i demonstrate sound waves interference and standing waves from loudspeaker used sound sensor. The frequency on loudspeaker is about 5500Hz. Enjoy!!!
From playlist WAVES
Show Me Some Science! Constructive and Destructive Interference
Waves are one way in which energy can be send down a string. When two waves meet, they interact. This interaction is called interference. If two waves add up this is known as "constructive interference" and if they cancel out it's "destructive interference". After the waves interact, they
From playlist Show Me Some Science!
What is Sound? - Quickly Discover What Sound Really Is
What is Sound? This simple demonstration visually shows how sound waves are produced from a vibrating surface. A frequency generator is hooked up to a power amplifier, and the resultant signal is used to drive a loudspeaker. The signal is also sent to an oscilloscope. After listen
From playlist Physics Demonstrations
Acoustic engineering: The art of engineering a silent world
Have you ever woken up from your sleep because of a construction taking place near your house? It is the opposite of a soothing experience, and this situation is a kind of noise pollution. The term, also known as environmental noise or sound pollution, refers to the generation of noise t
From playlist Theory to Reality
Raphaël Forien: Fluctuations in stochastic pushed fronts
CIRM HYBRID EVENT Recorded during the meeting "5th Workshop Probability and Evolution " the July 01, 2021 by the Centre International de Rencontres Mathématiques (Marseille, France) Filmmaker: Guillaume Hennenfent Find this video and other talks given by worldwide mathematicians on CIR
From playlist Probability and Statistics
Overfitting - Fitting the data too well; fitting the noise. Deterministic noise versus stochastic noise. Lecture 11 of 18 of Caltech's Machine Learning Course - CS 156 by Professor Yaser Abu-Mostafa. View course materials in iTunes U Course App - https://itunes.apple.com/us/course/machine-
From playlist Machine Learning Course - CS 156
In this video, we learn the answer to these two questions: 1. Why do complex models have higher variance? 2. Why do complex models have higher generalization error? P.S: The answer will be noise! Link to my notes on Introduction to Data Science: https://github.com/knathanieltucker/data-
From playlist Introduction to Data Science - Foundations
Reservoir computing in noisy real-world systems: network inference and dynamical. by Sarthak Chandra
DISCUSSION MEETING NEUROSCIENCE, DATA SCIENCE AND DYNAMICS (ONLINE) ORGANIZERS: Amit Apte (IISER-Pune, India), Neelima Gupte (IIT-Madras, India) and Ramakrishna Ramaswamy (IIT-Delhi, India) DATE : 07 February 2022 to 10 February 2022 VENUE: Online This discussion meeting on Neuroscien
From playlist Neuroscience, Data Science and Dynamics (ONLINE)
Michèle Thieullen: An Hodgkin-Huxley neuron receiving a random periodic signal [...]
Find this video and other talks given by worldwide mathematicians on CIRM's Audiovisual Mathematics Library: http://library.cirm-math.fr. And discover all its functionalities: - Chapter markers and keywords to watch the parts of your choice in the video - Videos enriched with abstracts, b
From playlist Probability and Statistics
Noise-induced schooling of fish by Vishwesha Guttal
DISCUSSION MEETING : 7TH INDIAN STATISTICAL PHYSICS COMMUNITY MEETING ORGANIZERS : Ranjini Bandyopadhyay, Abhishek Dhar, Kavita Jain, Rahul Pandit, Sanjib Sabhapandit, Samriddhi Sankar Ray and Prerna Sharma DATE : 19 February 2020 to 21 February 2020 VENUE : Ramanujan Lecture Hall, ICTS
From playlist 7th Indian Statistical Physics Community Meeting 2020
Stochastic modelling of geophysical flows - Mémin - Workshop 2 - CEB T3 2019
Mémin (INRIA, FR) / 13.11.2019 Stochastic modelling of geophysical flows ---------------------------------- Vous pouvez nous rejoindre sur les réseaux sociaux pour suivre nos actualités. Facebook : https://www.facebook.com/InstitutHenriPoincare/ Twitter : https://twitter.com/InHe
From playlist 2019 - T3 - The Mathematics of Climate and the Environment
Lecture 16 | MIT 6.832 Underactuated Robotics, Spring 2009
Lecture 16: Introducing stochastic optimal control Instructor: Russell Tedrake See the complete course at: http://ocw.mit.edu/6-832s09 License: Creative Commons BY-NC-SA More information at http://ocw.mit.edu/terms More courses at http://ocw.mit.edu
From playlist MIT 6.832 Underactuated Robotics, Spring 2009
GRCon19 - Multichannel phase coherent transceiver system with GNU Radio... by Michael Hennerich
Multichannel phase coherent transceiver system with GNU Radio interface by Michael Hennerich Many applications need multiple channels of phase and frequency synchronization and coherency. Applications like Direction of Arrival (DOA) accuracy are directly related to the number of channels
From playlist GRCon 2019
Jonathan defines what white noise actually is and how it's used to mask other annoying sounds. Learn more at HowStuffWorks.com: http://science.howstuffworks.com/question47.htm Share on Facebook: http://goo.gl/n7YNrZ Share on Twitter: http://goo.gl/Fq9InS Subscribe: http://goo.gl/ZYI7Gt V
From playlist Episodes hosted by Jonathan
Stanford CS234: Reinforcement Learning | Winter 2019 | Lecture 2 - Given a Model of the World
For more information about Stanford’s Artificial Intelligence professional and graduate programs, visit: https://stanford.io/ai Professor Emma Brunskill, Stanford University https://stanford.io/3eJW8yT Professor Emma Brunskill Assistant Professor, Computer Science Stanford AI for Human
From playlist Stanford CS234: Reinforcement Learning | Winter 2019