Algorithms - Revision history

BPeat at 20:31, 28 April 2024

2024-04-28T20:31:15Z

BPeat: Text replacement - "* Conversational AI ... ChatGPT | OpenAI ... Bing | Microsoft ... Bard | Google ... Claude | Anthropic ... Perplexity ... You ... Ernie | Baidu" to "* Conversational AI ... [[C...

2024-03-16T15:50:20Z

BPeat at 02:41, 6 March 2024

2024-03-06T02:41:02Z

BPeat at 18:40, 18 September 2023

2023-09-18T18:40:13Z

BPeat at 18:52, 2 September 2023

2023-09-02T18:52:23Z

BPeat at 02:02, 14 July 2023

2023-07-14T02:02:16Z

BPeat at 02:18, 12 July 2023

2023-07-12T02:18:18Z

BPeat at 17:04, 10 July 2023

2023-07-10T17:04:53Z

BPeat at 17:01, 10 July 2023

2023-07-10T17:01:25Z

BPeat at 16:50, 10 July 2023

2023-07-10T16:50:52Z

@@ Line 28: / Line 28: @@
 * [[Monte Carlo]] (MC) Method
 * [[Architectures]] for AI ... [[Generative AI Stack]] ... [[Enterprise Architecture (EA)]] ... [[Enterprise Portfolio Management (EPM)]] ... [[Architecture and Interior Design]]
-* [[In-Context Learning (ICL)]] ... [[Context]] ... [[Causation vs. Correlation]] ... [[Autocorrelation]] ... [[Out-of-Distribution (OOD) Generalization]] ... [[Transfer Learning]]
+* [[Perspective]] ... [[Context]] ... [[In-Context Learning (ICL)]] ... [[Transfer Learning]] ... [[Out-of-Distribution (OOD) Generalization]]
 * [[What is Artificial Intelligence (AI)? | Artificial Intelligence (AI)]] ... [[Generative AI]] ... [[Machine Learning (ML)]] ... [[Deep Learning]] ... [[Neural Network]] ... [[Reinforcement Learning (RL)|Reinforcement]] ... [[Learning Techniques]]
 * [[Conversational AI]] ... [[ChatGPT]] | [[OpenAI]] ... [[Bing/Copilot]] | [[Microsoft]] ... [[Gemini]] | [[Google]] ... [[Claude]] | [[Anthropic]] ... [[Perplexity]] ... [[You]] ... [[phind]] ... [[Ernie]] | [[Baidu]]

@@ Line 30: / Line 30: @@
 * [[In-Context Learning (ICL)]] ... [[Context]] ... [[Causation vs. Correlation]] ... [[Autocorrelation]] ... [[Out-of-Distribution (OOD) Generalization]] ... [[Transfer Learning]]
 * [[What is Artificial Intelligence (AI)? | Artificial Intelligence (AI)]] ... [[Generative AI]] ... [[Machine Learning (ML)]] ... [[Deep Learning]] ... [[Neural Network]] ... [[Reinforcement Learning (RL)|Reinforcement]] ... [[Learning Techniques]]
-* [[Conversational AI]] ... [[ChatGPT]] | [[OpenAI]] ... [[Bing]] | [[Microsoft]] ... [[Bard]] | [[Google]] ... [[Claude]] | [[Anthropic]] ... [[Perplexity]] ... [[You]] ... [[Ernie]] | [[Baidu]]
+* [[Conversational AI]] ... [[ChatGPT]] | [[OpenAI]] ... [[Bing/Copilot]] | [[Microsoft]] ... [[Gemini]] | [[Google]] ... [[Claude]] | [[Anthropic]] ... [[Perplexity]] ... [[You]] ... [[phind]] ... [[Ernie]] | [[Baidu]]
 * [https://github.com/JonTupitza/Data-Science-On-Ramp/blob/master/07-Develop-Machine-Learning-Models.ipynb Training (Fitting) Machine Learning Models - Jupyter Notebooks |] [https://github.com/jontupitza Jon Tupitza]
 ** [https://github.com/JonTupitza/Data-Science-Process/blob/master/07-Develop-Machine-Learning-Models-RFC.ipynb Model: Random Forests Classifier] Reference: [[Random Forest (or) Random Decision Forest]]

@@ Line 17: / Line 17: @@
 [https://www.google.com/search?q=models+~algorithms+deep+machine+learning+ML ...Google search]
-* [[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]]
 * [[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]]
 * [[Strategy & Tactics]] ... [[Project Management]] ... [[Best Practices]] ... [[Checklists]] ... [[Project Check-in]] ... [[Evaluation]] ... [[Evaluation - Measures|Measures]]

@@ Line 27: / Line 27: @@
 * [https://aiyprojects.withgoogle.com/models/ Machine learning models for AIY kits | AIY]
 * [[Monte Carlo]] (MC) Method
-* [[Architectures]] for AI ... [[Enterprise Architecture (EA)]] ... [[Enterprise Portfolio Management (EPM)]] ... [[Architecture and Interior Design]]
+* [[Architectures]] for AI ... [[Generative AI Stack]] ... [[Enterprise Architecture (EA)]] ... [[Enterprise Portfolio Management (EPM)]] ... [[Architecture and Interior Design]]
 * [[In-Context Learning (ICL)]] ... [[Context]] ... [[Causation vs. Correlation]] ... [[Autocorrelation]] ... [[Out-of-Distribution (OOD) Generalization]] ... [[Transfer Learning]]
 * [[What is Artificial Intelligence (AI)? | Artificial Intelligence (AI)]] ... [[Generative AI]] ... [[Machine Learning (ML)]] ... [[Deep Learning]] ... [[Neural Network]] ... [[Reinforcement Learning (RL)|Reinforcement]] ... [[Learning Techniques]]

@@ Line 29: / Line 29: @@
 * [[Architectures]] for AI ... [[Enterprise Architecture (EA)]] ... [[Enterprise Portfolio Management (EPM)]] ... [[Architecture and Interior Design]]
 * [[In-Context Learning (ICL)]] ... [[Context]] ... [[Causation vs. Correlation]] ... [[Autocorrelation]] ... [[Out-of-Distribution (OOD) Generalization]] ... [[Transfer Learning]]
-* [[Generative AI]] ... [[Conversational AI]] ... [[ChatGPT]] | [[OpenAI]] ... [[Bing]] | [[Microsoft]] ... [[Bard]] | [[Google]] ... [[Claude]] | [[Anthropic]] ... [[Perplexity]] ... [[You]] ... [[Ernie]] | [[Baidu]]
+* [[What is Artificial Intelligence (AI)? | Artificial Intelligence (AI)]] ... [[Generative AI]] ... [[Machine Learning (ML)]] ... [[Deep Learning]] ... [[Neural Network]] ... [[Reinforcement Learning (RL)|Reinforcement]] ... [[Learning Techniques]]
 * [https://github.com/JonTupitza/Data-Science-On-Ramp/blob/master/07-Develop-Machine-Learning-Models.ipynb Training (Fitting) Machine Learning Models - Jupyter Notebooks |] [https://github.com/jontupitza Jon Tupitza]
 ** [https://github.com/JonTupitza/Data-Science-Process/blob/master/07-Develop-Machine-Learning-Models-RFC.ipynb Model: Random Forests Classifier] Reference: [[Random Forest (or) Random Decision Forest]]

@@ Line 17: / Line 17: @@
 [https://www.google.com/search?q=models+~algorithms+deep+machine+learning+ML ...Google search]
-* [[Algorithm Administration]]
+* [[AI Solver]] ... [[Algorithms]] ... [[Algorithm Administration|Administration]] ... [[Model Search]] ... [[Discriminative vs. Generative]] ... [[Train, Validate, and Test]]
 * [[Algorithm Administration#Hyperparameter|Hyperparameter]]s
 * [[Strategy & Tactics]] ... [[Project Management]] ... [[Best Practices]] ... [[Checklists]] ... [[Project Check-in]] ... [[Evaluation]] ... [[Evaluation - Measures|Measures]]
 * [[Google DeepMind AlphaGo Zero]]
 * [[Google AIY Projects Program]]
 * [https://www.sas.com/en_gb/insights/articles/analytics/machine-learning-algorithms.html A guide to machine learning algorithms and their applications]
 * [https://en.wikipedia.org/wiki/Outline_of_machine_learning Outline of machine learning | Wikipedia]

@@ Line 18: / Line 18: @@
 * [[Algorithm Administration]]
 * [[Discriminative vs. Generative]]
 * [[Algorithm Administration#Hyperparameter|Hyperparameter]]s
 * [[Strategy & Tactics]] ... [[Project Management]] ... [[Best Practices]] ... [[Checklists]] ... [[Project Check-in]] ... [[Evaluation]] ... [[Evaluation - Measures|Measures]]
 * [[Google DeepMind AlphaGo Zero]]
 * [[Google AIY Projects Program]]
@@ Line 39: / Line 36: @@
 ** [https://github.com/JonTupitza/Data-Science-Process/blob/master/08-Develop-Machine-Learning-Models-GBC.ipynb Model:] [[Gradient Boosting Machine (GBM)]]
 ** [https://github.com/JonTupitza/Data-Science-Process/blob/master/09-Develop-Machine-Learning-Models-PCA.ipynb Model:] [[Principal Component Analysis (PCA)]]
+* [https://machinelearningmastery.com/types-of-learning-in-machine-learning/ 14 Different Types of Learning in Machine Learning | Jason Brownlee - Machine Learning Mastery]
 ...machine learning is a class of methods for automatically creating models from data. Machine learning algorithms are the engines of machine learning, meaning it is the algorithms that turn a data set into a model. Which kind of algorithm works best (supervised, unsupervised, classification, [[Regression]], etc.) depends on the kind of problem you’re solving, the computing resources available, and the nature of the data. Ordinary programming algorithms tell the computer what to do in a straightforward way. For example, sorting algorithms turn unordered data into data ordered by some criteria, often the numeric or alphabetical order of one or more fields in the data. [https://www.infoworld.com/article/3394399/machine-learning-algorithms-explained.html Machine learning algorithms explained | Martin Heller - InfoWorld]