Boosting - Revision history

BPeat at 14:31, 6 August 2023

2023-08-06T14:31:36Z

BPeat at 00:36, 14 July 2023

2023-07-14T00:36:18Z

BPeat at 06:04, 11 July 2023

2023-07-11T06:04:11Z

BPeat at 05:27, 11 July 2023

2023-07-11T05:27:40Z

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

2023-03-28T08:29:29Z

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

BPeat at 17:12, 17 August 2020

2020-08-17T17:12:28Z

BPeat at 16:14, 28 July 2019

2019-07-28T16:14:41Z

BPeat at 16:14, 28 July 2019

2019-07-28T16:14:06Z

BPeat at 16:10, 28 July 2019

2019-07-28T16:10:15Z

BPeat at 04:53, 13 July 2019

2019-07-13T04:53:14Z

@@ Line 35: / Line 35: @@
 * [https://xgboost.readthedocs.io/en/latest/ XGBoost] — uses liner and tree algorithms
 * [[LightGBM]]   ...Microsoft's gradient boosting framework that uses tree based learning algorithms
 <youtube>5CWwwtEM2TA</youtube>

@@ Line 23: / Line 23: @@
 # Boosting
 # [[Multiclassifiers; Ensembles and Hybrids; Bagging, Boosting, and Stacking]]
 * [https://en.wikipedia.org/wiki/Boosting_(machine_learning) Boosting | Wikipedia]

@@ Line 17: / Line 17: @@
 [https://www.google.com/search?q=Gradient+Boosting+Algorithms+machine+learning+ML+artificial+intelligence ...Google search]
-* [[Backpropagation]] ... [[Feed Forward Neural Network (FF or FFNN)|FFNN]] ... [[Forward-Forward]] ... [[Activation Functions]] ... [[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]]
 * [[Objective vs. Cost vs. Loss vs. Error Function]]
 * [[Overfitting Challenge]]

@@ 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
+|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=Gradient+Boosting+Algorithms Youtube search...]
 [https://www.google.com/search?q=Gradient+Boosting+Algorithms+machine+learning+ML+artificial+intelligence ...Google search]
 * [[Overfitting Challenge]]
 # [[Regularization]]
@@ Line 14: / Line 25: @@
 * [https://en.wikipedia.org/wiki/Boosting_(machine_learning) Boosting | Wikipedia]
 * [https://machinelearningmastery.com/gentle-introduction-gradient-boosting-algorithm-machine-learning/ A Gentle Introduction to the Gradient Boosting Algorithm for Machine Learning | Jason Brownlee]

@@ Line 5: / Line 5: @@
 |description=Helpful resources for your journey with artificial intelligence; videos, articles, techniques, courses, profiles, and tools
 }}
-[http://www.youtube.com/results?search_query=Gradient+Boosting+Algorithms Youtube search...]
+[https://www.youtube.com/results?search_query=Gradient+Boosting+Algorithms Youtube search...]
-[http://www.google.com/search?q=Gradient+Boosting+Algorithms+machine+learning+ML+artificial+intelligence ...Google search]
+[https://www.google.com/search?q=Gradient+Boosting+Algorithms+machine+learning+ML+artificial+intelligence ...Google search]
 * [[Overfitting Challenge]]
@@ Line 16: / Line 16: @@
 * [[Gradient Descent Optimization & Challenges]]
 * [[Objective vs. Cost vs. Loss vs. Error Function]]
-* [http://en.wikipedia.org/wiki/Boosting_(machine_learning) Boosting | Wikipedia]
+* [https://en.wikipedia.org/wiki/Boosting_(machine_learning) Boosting | Wikipedia]
-* [http://machinelearningmastery.com/gentle-introduction-gradient-boosting-algorithm-machine-learning/ A Gentle Introduction to the Gradient Boosting Algorithm for Machine Learning | Jason Brownlee]
+* [https://machinelearningmastery.com/gentle-introduction-gradient-boosting-algorithm-machine-learning/ A Gentle Introduction to the Gradient Boosting Algorithm for Machine Learning | Jason Brownlee]
 * [[Ensemble Learning]]
-Gradient Boosting Algorithm uses multiple weak algorithms to create a more powerful accurate algorithm. Instead of using a single estimator, having multiple will create a more stable and robust algorithm. The specialty of Gradient Boosting Algorithms is their higher accuracy. There are several Gradient Boosting Algorithms. [http://towardsdatascience.com/10-machine-learning-algorithms-you-need-to-know-77fb0055fe0 10 Machine Learning Algorithms You need to Know | Sidath Asir @ Medium]
+Gradient Boosting Algorithm uses multiple weak algorithms to create a more powerful accurate algorithm. Instead of using a single estimator, having multiple will create a more stable and robust algorithm. The specialty of Gradient Boosting Algorithms is their higher accuracy. There are several Gradient Boosting Algorithms. [https://towardsdatascience.com/10-machine-learning-algorithms-you-need-to-know-77fb0055fe0 10 Machine Learning Algorithms You need to Know | Sidath Asir @ Medium]
 * [[Gradient Boosting Machine (GBM)]]
 * Gradient [[(Boosted) Decision Tree]] (GBDT)
-* [http://en.wikipedia.org/wiki/AdaBoost Adaptive Boosting (AdaBoost)]
+* [https://en.wikipedia.org/wiki/AdaBoost Adaptive Boosting (AdaBoost)]
-* [http://xgboost.readthedocs.io/en/latest/ XGBoost] — uses liner and tree algorithms
+* [https://xgboost.readthedocs.io/en/latest/ XGBoost] — uses liner and tree algorithms
 * [[LightGBM]]   ...Microsoft's gradient boosting framework that uses tree based learning algorithms

@@ Line 28: / Line 28: @@
 * [http://github.com/microsoft/LightGBM LightGBM, Light Gradient Boosting Machine] - A fast, distributed, high performance gradient boosting (GBT, GBDT, GBRT, GBM or MART) framework based on decision tree algorithms, used for ranking, classification and many other machine learning tasks. LightGBM is under the umbrella of the [http://github.com/microsoft/dmtk DMTK project of Microsoft | GitHub]
 <youtube>sRktKszFmSk</youtube>
 <youtube>ErDgauqnTHk</youtube>

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