Online Learning - Revision history

BPeat at 02:42, 3 March 2024

2024-03-03T02:42:00Z

BPeat at 20:27, 8 December 2019

2019-12-08T20:27:15Z

BPeat at 19:42, 8 December 2019

2019-12-08T19:42:48Z

BPeat at 19:39, 8 December 2019

2019-12-08T19:39:20Z

BPeat: Created page with "{{#seo: |title=PRIMO.ai |titlemode=append |keywords=artificial, intelligence, machine, learning, models, algorithms, data, singularity, moonshot, Tensorflow, Google, Nvidia, M..."

2019-12-08T18:54:57Z

Created page with "{{#seo: |title=PRIMO.ai |titlemode=append |keywords=artificial, intelligence, machine, learning, models, algorithms, data, singularity, moonshot, Tensorflow, Google, Nvidia, M..."

New page

{{#seo:
|title=PRIMO.ai
|titlemode=append
|keywords=artificial, intelligence, machine, learning, models, algorithms, data, singularity, moonshot, Tensorflow, Google, Nvidia, Microsoft, Azure, Amazon, AWS
|description=Helpful resources for your journey with artificial intelligence; videos, articles, techniques, courses, profiles, and tools
}}
[http://www.youtube.com/results?search_query=Few+Shot+Learning+Model+agnostic+Meta+learning+MAML YouTube search...]
[http://www.google.com/search?q=Few+Shot+Learning+Model+agnostic+Meta+learning+MAML+deep+machine+learning+ML ...Google search]

* [http://medium.com/quick-code/understanding-few-shot-learning-in-machine-learning-bede251a0f67 Understanding few-shot learning in machine learning | Michael J. Garbade]
* [http://arxiv.org/pdf/1803.02999.pdf On First-Order Meta-Learning Algorithms | A. Nichol, J. Achiam, and J. Schulman - OpenAI]
* [http://bair.berkeley.edu/blog/2017/07/18/learning-to-learn/ Learning to Learn | Chelsea Finn]
* [[Learning Techniques]]

Most of the time, computer vision systems need to see hundreds or thousands (or even millions) of examples to figure out how to do something. One-shot and few-shot learning try to create a system that can be taught to do something with far less training. It’s similar to how toddlers might learn a new concept or task.

<youtube>Q-agrHm-ztU</youtube>
<youtube>LMfLRF9VKrc</youtube>

← Older revision		Revision as of 02:42, 3 March 2024
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	* [http://en.wikipedia.org/wiki/Online_machine_learning Online Machine Learning \| Wikipedia]		* [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]	+	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]

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 ** [[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]

← Older revision		Revision as of 19:42, 8 December 2019
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−	<youtube>~~Q-agrHm-ztU~~</youtube>	+	<youtube>dnCzy_XKGbA</youtube>
−	<youtube>~~LMfLRF9VKrc~~</youtube>	+	<youtube>O3gd6elZOlA</youtube>
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 |description=Helpful resources for your journey with artificial intelligence; videos, articles, techniques, courses, profiles, and tools
 }}
-[http://www.youtube.com/results?search_query=Few+Shot+Learning+Model+agnostic+Meta+learning+MAML YouTube search...]
+[http://www.youtube.com/results?search_query=Online+Learning+machine YouTube search...]
-[http://www.google.com/search?q=Few+Shot+Learning+Model+agnostic+Meta+learning+MAML+deep+machine+learning+ML ...Google search]
+[http://www.google.com/search?q=Online+Learning+machine ...Google search]
 * [[Learning Techniques]]
-Most of the time, computer vision systems need to see hundreds or thousands (or even millions) of examples to figure out how to do something. One-shot and few-shot learning try to create a system that can be taught to do something with far less training. It’s similar to how toddlers might learn a new concept or task.
+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>Q-agrHm-ztU</youtube>
 <youtube>LMfLRF9VKrc</youtube>