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Próxima revisión 		Revisión previa
clase:iabd:pia:documentacion [2021/12/18 13:16]
admin [TensorFlow Avanzado] 		clase:iabd:pia:documentacion [2024/03/31 21:36] (actual)
admin [Estadística Bayesana]
Línea 4: Línea 4:
  
  
-===== Regresión logística ===== 
  
-    * [[https://medium.com/analytics-vidhya/logistic-regression-part-i-transformation-of-linear-to-logistic-395cb539038b|Logistic Regression Part I — Transformation of Linear to Logistic]] +
-    * [[https://hausetutorials.netlify.app/posts/2019-04-13-logistic-regression/|Gentle intro to logistic regression]] +
-    * [[https://medium.com/nerd-for-tech/understanding-logistic-regression-782baa868a54|Understanding Logistic Regression]] +
-    * [[https://arunaddagatla.medium.com/maximum-likelihood-estimation-in-logistic-regression-f86ff1627b67|Maximum Likelihood Estimation in Logistic Regression]] +
-    * [[https://medium.com/analytics-vidhya/logistic-regression-a3249301b75e|Logistic Regression]]: Explica cosas de L1 y L2 +
-    * [[https://towardsdatascience.com/the-intuitive-explanation-of-logistic-regression-a0375b1bee54|The Intuitive Explanation of Logistic Regression]] +
-    * [[https://medium.com/gadictos/logistic-regression-a-detailed-overview-from-scratch-ad0491f14c3b|Logistic Regression: A Detailed Overview from Scratch]]+
  
  
Línea 88: Línea 81:
   * [[https://towardsdatascience.com/transformers-explained-visually-not-just-how-but-why-they-work-so-well-d840bd61a9d3|Transformers Explained Visually — Not just how, but Why they work so well]]   * [[https://towardsdatascience.com/transformers-explained-visually-not-just-how-but-why-they-work-so-well-d840bd61a9d3|Transformers Explained Visually — Not just how, but Why they work so well]]
   * [[https://samarthagrawal86.medium.com/feature-engineering-of-datetime-variables-for-data-science-machine-learning-45e611c632ad|Feature Engineering of DateTime Variables for Data Science, Machine Learning, Python]]   * [[https://samarthagrawal86.medium.com/feature-engineering-of-datetime-variables-for-data-science-machine-learning-45e611c632ad|Feature Engineering of DateTime Variables for Data Science, Machine Learning, Python]]
 +
 +
 +===== Lenguaje Natural =====
 +  * [[https://paperswithcode.com/method/bert|BERT]]
 +  * [[https://www.codificandobits.com/blog/bert-en-el-natural-language-processing/|BERT: el inicio de una nueva era en el Natural Language Processing]]
 +
 +Word Embedding Castellano:
 +  * [[https://github.com/aitoralmeida/spanish_word2vec]]
 +  * [[https://www.kaggle.com/rtatman/pretrained-word-vectors-for-spanish]]
 +  * [[https://github.com/dccuchile/spanish-word-embeddings]]
 +  * [[https://crscardellino.ar/SBWCE/]]
  
  
Línea 112: Línea 116:
   * [[https://rohit10patel20.medium.com/hyperparameter-search-using-bayesian-optimization-and-an-evolutionary-algorithm-bdca6331de1c|Hyperparameter search using Bayesian Optimization and an Evolutionary Algorithm]]   * [[https://rohit10patel20.medium.com/hyperparameter-search-using-bayesian-optimization-and-an-evolutionary-algorithm-bdca6331de1c|Hyperparameter search using Bayesian Optimization and an Evolutionary Algorithm]]
   * [[https://towardsdatascience.com/how-to-use-horovods-large-batch-simulation-to-optimize-hyperparameter-tuning-for-highly-a815c4ab1d34|How to Use Horovod’s Large Batch Simulation to Optimize Hyperparameter Tuning for (Highly) Distributed Training]]   * [[https://towardsdatascience.com/how-to-use-horovods-large-batch-simulation-to-optimize-hyperparameter-tuning-for-highly-a815c4ab1d34|How to Use Horovod’s Large Batch Simulation to Optimize Hyperparameter Tuning for (Highly) Distributed Training]]
-  * Teorema de Bayes + 
-    * [[https://seeing-theory.brown.edu/bayesian-inference/es.html|Viendo la Teoría - Inferencia Bayesiana]] + 
-    * [[https://datascience.com.co/c%C3%B3mo-funciona-la-inferencia-bayesiana-dc4ad29d4697|¿Cómo funciona la inferencia bayesiana?]] +===== Estadística Bayesana ===== 
-    * [[https://distill.pub/2020/bayesian-optimization/|Exploring Bayesian Optimization]]: Muy buena explicación de la optimización Bayesiana + 
-    * [[https://picanumeros.wordpress.com/2021/04/18/la-estadistica-detras-del-rescate-de-la-bomba-de-palomares/|La estadística detrás del rescate de la bomba de Palomares]]+  * [[https://seeing-theory.brown.edu/bayesian-inference/es.html|Viendo la Teoría - Inferencia Bayesiana]] 
 +  * [[https://datascience.com.co/c%C3%B3mo-funciona-la-inferencia-bayesiana-dc4ad29d4697|¿Cómo funciona la inferencia bayesiana?]] 
 +  * [[https://distill.pub/2020/bayesian-optimization/|Exploring Bayesian Optimization]]: Muy buena explicación de la optimización Bayesiana 
 +  * [[https://picanumeros.wordpress.com/2021/04/18/la-estadistica-detras-del-rescate-de-la-bomba-de-palomares/|La estadística detrás del rescate de la bomba de Palomares]] 
 +  * [[https://towardsdatascience.com/bayes-theorem-the-holy-grail-of-data-science-55d93315defb|Bayes’ Theorem: The Holy Grail of Data Science]] 
 +  * [[https://towardsdatascience.com/probability-learning-ii-how-bayes-theorem-is-applied-in-machine-learning-bd747a960962|Probability Learning II: How Bayes’ Theorem is applied in Machine Learning]] 
 +  * {{ :clase:iabd:pia:a_gentle_introduction_to_bayesian_analysis.applications_to_development_research.pdf|A Gentle Introduction to Bayesian Analysis.Applications to Development Research}}  
 + 
 + 
 + 
 + 
 + 
 +  * {{ :clase:iabd:pia:doing_bayesian_data_analysis.pdf |Doing Bayesian Data Analysis:A Tutorial with R, JAGS,and Stan}} 
 +    * [[http://doingbayesiandataanalysis.blogspot.com/2013/08/how-much-of-bayesian-posterior.html|How much of a Bayesian posterior distribution falls inside a region of practical equivalence (ROPE)]] 
 +    * {{ :clase:iabd:pia:bayesian_estimation_supersedes_the_t_test.pdf|Bayesian Estimation Supersedes the t Test (PDF)}} 
 +      * [[https://jkkweb.sitehost.iu.edu/BEST/|Bayesian estimation supersedes the t test]] 
 +      * [[https://www.sumsar.net/best_online/|Bayesian Estimation Supersedes the t-test (BEST)]]: Online tool 
 +      * [[https://xianblog.wordpress.com/2016/12/05/bayesian-parameter-estimation-versus-model-comparison/|Bayesian parameter estimation versus model comparison]]: Critica de "Bayesian estimation supersedes the t test"
  
  
Línea 208: Línea 229:
     history=model.fit(x, y,epochs=epochs,verbose=False)     history=model.fit(x, y,epochs=epochs,verbose=False)
 </sxh>  </sxh> 
 +
 +
 +===== Natural Language Processing =====
 +
 +  * [[https://www.pinecone.io/learn/nlp/|Natural Language Processing (NLP) for Semantic Search]]
 +
 ===== EDA ===== ===== EDA =====
  
Línea 227: Línea 254:
   * [[https://medium.com/geekculture/exploratory-data-analysis-eda-44912e492879#7195-1280d36b4873|Exploratory Data Analysis(EDA)]] y [[https://medium.com/geekculture/exploratory-data-analysis-part-2-2e68180be41e|Exploratory Data Analysis(Part 2)]]   * [[https://medium.com/geekculture/exploratory-data-analysis-eda-44912e492879#7195-1280d36b4873|Exploratory Data Analysis(EDA)]] y [[https://medium.com/geekculture/exploratory-data-analysis-part-2-2e68180be41e|Exploratory Data Analysis(Part 2)]]
   * [[https://levelup.gitconnected.com/concrete-strength-modeling-with-5-machine-learning-algorithms-447ea3d558d1|Concrete Strength Modeling with 5 Machine Learning Algorithms]]   * [[https://levelup.gitconnected.com/concrete-strength-modeling-with-5-machine-learning-algorithms-447ea3d558d1|Concrete Strength Modeling with 5 Machine Learning Algorithms]]
 +  * [[https://github.com/fbdesignpro/sweetviz|sweetviz]]:EDA basada en Pandas que genera el resultado en HTML con solo 2 líneas de código
   * Solve Machine Learning Problems   * Solve Machine Learning Problems
     * [[https://medium.com/analytics-vidhya/solve-machine-learning-problems-eda-part-1-761f1828a465|Solve Machine Learning Problems: EDA (part-1)]]     * [[https://medium.com/analytics-vidhya/solve-machine-learning-problems-eda-part-1-761f1828a465|Solve Machine Learning Problems: EDA (part-1)]]
Línea 273: Línea 301:
   * [[https://amitprius.medium.com/important-links-of-articles-in-data-science-and-deep-learning-7b577559d4d1|Important Links of Articles in Data Science and Deep Learning]]   * [[https://amitprius.medium.com/important-links-of-articles-in-data-science-and-deep-learning-7b577559d4d1|Important Links of Articles in Data Science and Deep Learning]]
   * [[https://people.eecs.berkeley.edu/~jrs/189/|CS 189/289A Introduction to Machine Learning]]: Curso de IA de la universidad de Berkeley   * [[https://people.eecs.berkeley.edu/~jrs/189/|CS 189/289A Introduction to Machine Learning]]: Curso de IA de la universidad de Berkeley
 +  * [[https://www.cienciadedatos.net/|Ciencia de datos, teoría y ejemplos prácticos en R y Python]]
  
 ===== Humor ===== ===== Humor =====
Línea 297: Línea 326:
  
   * Poetry   * Poetry
 +    * [[https://towardsdatascience.com/poetry-to-complement-virtualenv-44088cc78fd1|Are You Still Using Virtualenv for Managing Dependencies in Python Projects?]]
 +    * [[https://www.pythoncheatsheet.org/blog/python-projects-with-poetry-and-vscode-part-1/|Python projects with Poetry and VSCode. Part 1]]
 +    * [[https://www.pythoncheatsheet.org/blog/python-projects-with-poetry-and-vscode-part-2/|Python projects with Poetry and VSCode. Part 2]]
  
 <sxh python> <sxh python>
Línea 311: Línea 343:
 poetry run python my_script.py poetry run python my_script.py
 </sxh> </sxh>
 +
 +=== Crear un colormap personalizado ===
 +<sxh python>
 +from matplotlib.colors import LinearSegmentedColormap, to_rgb
 +
 +def crear_colomap_continuo(hex_colors):
 +    all_red = []
 +    all_green = []
 +    all_blue = []
 +    
 +    for hex_color in hex_colors:
 +        all_red.append(to_rgb(hex_color)[0])
 +        all_green.append(to_rgb(hex_color)[1])
 +        all_blue.append(to_rgb(hex_color)[2])
 +
 +        
 +    num_colors = len(hex_colors) - 1
 +    red = tuple([(1/num_colors*i,v,v) for i,v in enumerate(all_red)])
 +    green = tuple([(1/num_colors*i,v,v) for i,v in enumerate(all_green)])
 +    blue = tuple([(1/num_colors*i,v,v) for i,v in enumerate(all_blue)])
 +    
 +    color_dictionary = {'red':red,'green':green,'blue':blue}
 +    new_cmap = LinearSegmentedColormap('new_cmap',segmentdata=color_dictionary)
 +    return new_cmap
 +
 +
 +
 +new_cmap = build_custom_continuous_cmap(["#FFFFFF","#DBE6FA","#C3D6F4","#ACC8EF","#96BBE9","#81AFE4","#6DA4DE","#599BD9","#4793D3","#358CCE","#2486C8","#1481C3","#057DBD"])
 +
 +</sxh>
 +
 +===== Calculo de errores =====
 +  * {{ :clase:iabd:pia:2eval:propagacion_de_errores.pdf |Propagación de errores}}
 +  * {{ :clase:iabd:pia:2eval:guia_practica_para_la_realizacion_de_la_medida_y_el_calculo_de_errores.pdf |Guía práctica para la realización de la medida y el cálculo de errores}}
  
 ===== Hardware ===== ===== Hardware =====
-  * [[https://www.nvidia.com/es-es/data-center/dgx-a100/|DGX A100 : Universal System for AI Infrastructure | NVIDIA]] 
-  * {{ :clase:iabd:pia:nvidia-dgx-station-a100-datasheet.pdf |NVIDIA DGX Station A100 datasheet}} 
  
  
  
 {{:clase:iabd:pia:guide-become-data-scientist.jpeg?200|}} {{:clase:iabd:pia:guide-become-data-scientist.jpeg?200|}}
clase/iabd/pia/documentacion.1639829795.txt.gz · Última modificación: 2021/12/18 13:16 por admin

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