Cross-Entropy Loss - Revision history

BPeat at 00:58, 3 September 2023

2023-09-03T00:58:56Z

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2023-07-11T06:24:09Z

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2023-03-28T11:48:15Z

Text replacement - "http://" to "https://"

BPeat at 19:36, 27 September 2020

2020-09-27T19:36:19Z

BPeat at 01:08, 10 April 2020

2020-04-10T01:08:33Z

BPeat at 01:07, 10 April 2020

2020-04-10T01:07:54Z

BPeat at 23:56, 9 April 2020

2020-04-09T23:56:54Z

BPeat at 23:53, 9 April 2020

2020-04-09T23:53:49Z

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

2020-04-09T23:50:16Z

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=hyperparameters+deep+learning+tuning+optimization YouTube search...]
[http://www.google.com/search?q=hyperparameters+optimization+deep+machine+learning+ML ...Google search]

* [[hyperparameters]]

← Older revision		Revision as of 00:58, 3 September 2023
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	[https://www.google.com/search?q=Cross+Entropy+Loss+deep+learning+hyperparameter ...Google search]		[https://www.google.com/search?q=Cross+Entropy+Loss+deep+learning+hyperparameter ...Google search]

		+	* [[Loss]]
	* [[Backpropagation]] ... [[Feed Forward Neural Network (FF or FFNN)\|FFNN]] ... [[Forward-Forward]] ... [[Activation Functions]] ...[[Softmax]] ... [[Loss]] ... [[Boosting]] ... [[Gradient Descent Optimization & Challenges\|Gradient Descent]] ... [[Algorithm Administration#Hyperparameter\|Hyperparameter]] ... [[Manifold Hypothesis]] ... [[Principal Component Analysis (PCA)\|PCA]]		* [[Backpropagation]] ... [[Feed Forward Neural Network (FF or FFNN)\|FFNN]] ... [[Forward-Forward]] ... [[Activation Functions]] ...[[Softmax]] ... [[Loss]] ... [[Boosting]] ... [[Gradient Descent Optimization & Challenges\|Gradient Descent]] ... [[Algorithm Administration#Hyperparameter\|Hyperparameter]] ... [[Manifold Hypothesis]] ... [[Principal Component Analysis (PCA)\|PCA]]

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 |title=PRIMO.ai
 |titlemode=append
-|keywords=artificial, intelligence, machine, learning, models, algorithms, data, singularity, moonshot, Tensorflow, Google, Nvidia, Microsoft, Azure, Amazon, AWS
+|keywords=ChatGPT, artificial, intelligence, machine, learning, GPT-4, GPT-5, NLP, NLG, NLC, NLU, models, data, singularity, moonshot, Sentience, AGI, Emergence, Moonshot, Explainable, TensorFlow, Google, Nvidia, Microsoft, Azure, Amazon, AWS, Hugging Face, OpenAI, Tensorflow, OpenAI, Google, Nvidia, Microsoft, Azure, Amazon, AWS, Meta, LLM, metaverse, assistants, agents, digital twin, IoT, Transhumanism, Immersive Reality, Generative AI, Conversational AI, Perplexity, Bing, You, Bard, Ernie, prompt Engineering LangChain, Video/Image, Vision, End-to-End Speech, Synthesize Speech, Speech Recognition, Stanford, MIT |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
 }}
 [https://www.youtube.com/results?search_query=Cross+Entropy+Loss+deep+learning+hyperparameter YouTube search...]
 [https://www.google.com/search?q=Cross+Entropy+Loss+deep+learning+hyperparameter ...Google search]
-* [[Loss]]
+* [[Backpropagation]] ... [[Feed Forward Neural Network (FF or FFNN)|FFNN]] ... [[Forward-Forward]] ... [[Activation Functions]] ...[[Softmax]] ... [[Loss]] ... [[Boosting]] ... [[Gradient Descent Optimization & Challenges|Gradient Descent]] ... [[Algorithm Administration#Hyperparameter|Hyperparameter]] ... [[Manifold Hypothesis]] ... [[Principal Component Analysis (PCA)|PCA]]
 Cross-entropy [[loss]], or log [[loss]], measures the performance of a classification model whose output is a probability value between 0 and 1. Cross-entropy loss increases as the predicted probability diverges from the actual label. [https://ml-cheatsheet.readthedocs.io/en/latest/loss_functions.html

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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=Cross+Entropy+Loss+deep+learning+hyperparameter YouTube search...]
+[https://www.youtube.com/results?search_query=Cross+Entropy+Loss+deep+learning+hyperparameter YouTube search...]
-[http://www.google.com/search?q=Cross+Entropy+Loss+deep+learning+hyperparameter ...Google search]
+[https://www.google.com/search?q=Cross+Entropy+Loss+deep+learning+hyperparameter ...Google search]
 * [[Loss]]
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-Cross-entropy [[loss]], or log [[loss]], measures the performance of a classification model whose output is a probability value between 0 and 1. Cross-entropy loss increases as the predicted probability diverges from the actual label. [http://ml-cheatsheet.readthedocs.io/en/latest/loss_functions.html
+Cross-entropy [[loss]], or log [[loss]], measures the performance of a classification model whose output is a probability value between 0 and 1. Cross-entropy loss increases as the predicted probability diverges from the actual label. [https://ml-cheatsheet.readthedocs.io/en/latest/loss_functions.html
-Cross-entropy [[loss]] is one of the most widely used loss functions in classification scenarios. In face recognition tasks, the cross-entropy loss is an effective method to eliminate outliers. [http://arxiv.org/pdf/1904.09523.pdf Neural Architecture Search for Deep Face Recognition | Ning Zhu]
+Cross-entropy [[loss]] is one of the most widely used loss functions in classification scenarios. In face recognition tasks, the cross-entropy loss is an effective method to eliminate outliers. [https://arxiv.org/pdf/1904.09523.pdf Neural Architecture Search for Deep Face Recognition | Ning Zhu]
-http://ml-cheatsheet.readthedocs.io/en/latest/_images/cross_entropy.png
+https://ml-cheatsheet.readthedocs.io/en/latest/_images/cross_entropy.png

@@ Line 9: / Line 9: @@
 * [[Loss]]
-* [[hyperparameter]]
+* [[Algorithm Administration#Hyperparameter|Hyperparameter]]
 Cross-entropy [[loss]], or log [[loss]], measures the performance of a classification model whose output is a probability value between 0 and 1. Cross-entropy loss increases as the predicted probability diverges from the actual label. [http://ml-cheatsheet.readthedocs.io/en/latest/loss_functions.html

@@ Line 8: / Line 8: @@
 [http://www.google.com/search?q=Cross+Entropy+Loss+deep+learning+hyperparameter ...Google search]
 * [[hyperparameter]]
 Cross-entropy [[loss]], or log [[loss]], measures the performance of a classification model whose output is a probability value between 0 and 1. Cross-entropy loss increases as the predicted probability diverges from the actual label. [http://ml-cheatsheet.readthedocs.io/en/latest/loss_functions.html
-Cross-entropy loss is one of the most widely used loss functions in classification scenarios. In face recognition tasks, the cross-entropy loss is an effective method to eliminate outliers. [http://arxiv.org/pdf/1904.09523.pdf Neural Architecture Search for Deep Face Recognition | Ning Zhu]
+Cross-entropy [[loss]] is one of the most widely used loss functions in classification scenarios. In face recognition tasks, the cross-entropy loss is an effective method to eliminate outliers. [http://arxiv.org/pdf/1904.09523.pdf Neural Architecture Search for Deep Face Recognition | Ning Zhu]
 http://ml-cheatsheet.readthedocs.io/en/latest/_images/cross_entropy.png

← Older revision		Revision as of 23:56, 9 April 2020
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	* [[hyperparameter]]		* [[hyperparameter]]
		+
		+	Cross-entropy loss, or log loss, measures the performance of a classification model whose output is a probability value between 0 and 1. Cross-entropy loss increases as the predicted probability diverges from the actual label. [http://ml-cheatsheet.readthedocs.io/en/latest/loss_functions.html

	Cross-entropy loss is one of the most widely used loss functions in classification scenarios. In face recognition tasks, the cross-entropy loss is an effective method to eliminate outliers. [http://arxiv.org/pdf/1904.09523.pdf Neural Architecture Search for Deep Face Recognition \| Ning Zhu]		Cross-entropy loss is one of the most widely used loss functions in classification scenarios. In face recognition tasks, the cross-entropy loss is an effective method to eliminate outliers. [http://arxiv.org/pdf/1904.09523.pdf Neural Architecture Search for Deep Face Recognition \| Ning Zhu]
		+
		+	http://ml-cheatsheet.readthedocs.io/en/latest/_images/cross_entropy.png