...predict categories - Revision history

BPeat at 02:52, 6 March 2024

2024-03-06T02:52:31Z

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2023-07-14T00:56:08Z

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

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

BPeat at 02:42, 9 February 2023

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BPeat at 03:28, 28 October 2020

2020-10-28T03:28:39Z

BPeat at 17:07, 11 October 2020

2020-10-11T17:07:57Z

@@ Line 14: / Line 14: @@
 </script>
 }}
-* [[AI Solver]] ... [[Algorithms]] ... [[Algorithm Administration|Administration]] ... [[Model Search]] ... [[Discriminative vs. Generative]] ... [[Optimizer]] ... [[Train, Validate, and Test]]
+* [[AI Solver]] ... [[Algorithms]] ... [[Algorithm Administration|Administration]] ... [[Model Search]] ... [[Discriminative vs. Generative]] ... [[Train, Validate, and Test]]
 Do you have...

@@ Line 14: / Line 14: @@
 </script>
 }}
-[[AI Solver]]
+* [[AI Solver]] ... [[Algorithms]] ... [[Algorithm Administration|Administration]] ... [[Model Search]] ... [[Discriminative vs. Generative]] ... [[Optimizer]] ... [[Train, Validate, and Test]]
 Do you have...

@@ Line 2: / Line 2: @@
 |title=PRIMO.ai
 |titlemode=append
-|keywords=artificial, intelligence, machine, learning, models, algorithms, data, singularity, moonshot, TensorFlow, Google, Nvidia, Microsoft, Azure, Amazon, AWS, Meta, Facebook
+|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
 }}
 [[AI Solver]]

@@ Line 28: / Line 28: @@
 ___________________________________________________
-* [[Capabilities]]
 * [[Data Quality#Data Augmentation, Data Labeling, and Auto-Tagging|Data Augmentation, Data Labeling, and Auto-Tagging]]
 * [https://medium.com/@srnghn/machine-learning-trying-to-predict-a-categorical-outcome-6ba542b854f5 Machine Learning: Trying to classify your data | Stacey Ronaghan - Medium]

@@ Line 24: / Line 24: @@
 **** linear, then try the [[Bayes#Naive Bayes|Naive Bayes]]
 **** accurate, can allow long training times, then try the [[Neural Network#Deep Neural Network (DNN)|Deep Neural Network (DNN)]] e.g. [[Image Classification]]
-**** which is a type of is predecessors... [[Feed Forward Neural Network (FF or FFNN)]] and [[Artificial Neural Network (ANN)]]
+**** which is a type of is predecessors... [[Feed Forward Neural Network (FF or FFNN)]] and [[Neural Network]]
 ___________________________________________________

@@ Line 14: / Line 14: @@
 **** linear, greater than 100 features, then try the [[Support Vector Machine (SVM)]]
 *** No
 **** fast training, linear, and the features are independent, then try the two-class [[Bayes#Naive Bayes|Naive Bayes]] point machine
 * ...multi-class classification; three or more categories?
@@ Line 24: / Line 23: @@
 *** No
 **** linear, then try the [[Bayes#Naive Bayes|Naive Bayes]]
-**** accurate, can allow long training times, then try the [[Deep Neural Network (DNN)]] e.g. [[Image Classification]]
+**** accurate, can allow long training times, then try the [[Neural Network#Deep Neural Network (DNN)|Deep Neural Network (DNN)]] e.g. [[Image Classification]]
 **** which is a type of is predecessors... [[Feed Forward Neural Network (FF or FFNN)]] and [[Artificial Neural Network (ANN)]]

@@ Line 31: / Line 31: @@
 * [[Capabilities]]
 * [[Data Quality#Data Augmentation, Data Labeling, and Auto-Tagging|Data Augmentation, Data Labeling, and Auto-Tagging]]
-* [http://medium.com/@srnghn/machine-learning-trying-to-predict-a-categorical-outcome-6ba542b854f5 Machine Learning: Trying to classify your data | Stacey Ronaghan - Medium]
+* [https://medium.com/@srnghn/machine-learning-trying-to-predict-a-categorical-outcome-6ba542b854f5 Machine Learning: Trying to classify your data | Stacey Ronaghan - Medium]
 * [[Evaluating Machine Learning Models]]
-** [http://bookdown.org/max/FES/encoding-categorical-predictors.html Feature Engineering and Selection: A Practical Approach for Predictive Models - 5 Encoding Categorical Predictors | Max Kuhn and Kjell Johnson]
+** [https://bookdown.org/max/FES/encoding-categorical-predictors.html Feature Engineering and Selection: A Practical Approach for Predictive Models - 5 Encoding Categorical Predictors | Max Kuhn and Kjell Johnson]
-** [http://docs.aws.amazon.com/machine-learning/latest/dg/binary-model-insights.html Binary Model Insights |] [[Amazon | Amazon Web Services]]
+** [https://docs.aws.amazon.com/machine-learning/latest/dg/binary-model-insights.html Binary Model Insights |] [[Amazon | Amazon Web Services]]
-** [http://docs.aws.amazon.com/machine-learning/latest/dg/multiclass-model-insights.html Multiclass Model Insights |] [[Amazon | Amazon Web Services]]
+** [https://docs.aws.amazon.com/machine-learning/latest/dg/multiclass-model-insights.html Multiclass Model Insights |] [[Amazon | Amazon Web Services]]
-Classification problems are sometimes divided into binary (yes or no) and multi-category problems (animal, vegetable, or mineral). Classifiers are ubiquitous in data science.  The world around is full of classifiers. Classifiers help in identifying customers who may churn. Classifiers help in predicting whether it will rain or not. Classifiers help in preventing spam e-mails. If the targets are designed to be binary (two-class classification) then a binary classifier is used, the target will only take a 0 or 1 value. [http://www.infoworld.com/article/3394399/machine-learning-algorithms-explained.html Machine learning algorithms explained | Martin Heller - InfoWorld]
+Classification problems are sometimes divided into binary (yes or no) and multi-category problems (animal, vegetable, or mineral). Classifiers are ubiquitous in data science.  The world around is full of classifiers. Classifiers help in identifying customers who may churn. Classifiers help in predicting whether it will rain or not. Classifiers help in preventing spam e-mails. If the targets are designed to be binary (two-class classification) then a binary classifier is used, the target will only take a 0 or 1 value. [https://www.infoworld.com/article/3394399/machine-learning-algorithms-explained.html Machine learning algorithms explained | Martin Heller - InfoWorld]