Difference between revisions of "Online Learning"
(Created page with "{{#seo: |title=PRIMO.ai |titlemode=append |keywords=artificial, intelligence, machine, learning, models, algorithms, data, singularity, moonshot, Tensorflow, Google, Nvidia, M...") |
m |
||
| (3 intermediate revisions by the same user not shown) | |||
| Line 5: | Line 5: | ||
|description=Helpful resources for your journey with artificial intelligence; videos, articles, techniques, courses, profiles, and tools | |description=Helpful resources for your journey with artificial intelligence; videos, articles, techniques, courses, profiles, and tools | ||
}} | }} | ||
| − | [http://www.youtube.com/results?search_query= | + | [http://www.youtube.com/results?search_query=Online+Learning+machine YouTube search...] |
| − | [http://www.google.com/search?q= | + | [http://www.google.com/search?q=Online+Learning+machine ...Google search] |
| − | |||
| − | |||
| − | |||
* [[Learning Techniques]] | * [[Learning Techniques]] | ||
| + | ** [[PRIMO.ai#Supervised|Supervised Learning]] | ||
| + | ** [[PRIMO.ai#Unsupervised|Unsupervised Learning]] | ||
| + | * [http://en.wikipedia.org/wiki/Online_machine_learning Online Machine Learning | Wikipedia] | ||
| + | Online learning involves using the data available and updating the model directly before a prediction is required or after the last observation was made. Online learning is appropriate for those problems where observations are provided over time and where the probability distribution of observations is expected to also change over time. Therefore, the model is expected to change just as frequently in order to capture and harness those changes. This approach is also used by algorithms where there may be more observations than can reasonably fit into [[memory]], therefore, learning is performed incrementally over observations, such as a stream of data. Generally, online learning seeks to minimize “regret,” which is how well the model performed compared to how well it might have performed if all the available information was available as a batch. One example of online learning is so-called stochastic or online gradient descent used to fit an artificial neural network. [http://machinelearningmastery.com/types-of-learning-in-machine-learning/ 14 Different Types of Learning in Machine Learning | Jason Brownlee - Machine Learning Mastery] | ||
| − | |||
| − | <youtube> | + | <youtube>dnCzy_XKGbA</youtube> |
| − | <youtube> | + | <youtube>O3gd6elZOlA</youtube> |
| + | <youtube>F17exCgFC6M</youtube> | ||
| + | <youtube>YLedud-YuWg</youtube> | ||
Latest revision as of 22:42, 2 March 2024
YouTube search... ...Google search
Online learning involves using the data available and updating the model directly before a prediction is required or after the last observation was made. Online learning is appropriate for those problems where observations are provided over time and where the probability distribution of observations is expected to also change over time. Therefore, the model is expected to change just as frequently in order to capture and harness those changes. This approach is also used by algorithms where there may be more observations than can reasonably fit into memory, therefore, learning is performed incrementally over observations, such as a stream of data. Generally, online learning seeks to minimize “regret,” which is how well the model performed compared to how well it might have performed if all the available information was available as a batch. One example of online learning is so-called stochastic or online gradient descent used to fit an artificial neural network. 14 Different Types of Learning in Machine Learning | Jason Brownlee - Machine Learning Mastery
