====== Documentación ======
















===== Overfitting, validación , reducción de la dimensionalidad,  datos entrada =====

  * [[https://medium.com/elca-data-science/an-overview-of-time-aware-cross-validation-techniques-9c0ee51675f9|An overview of time-aware cross-validation techniques]]
    * [[https://medium.com/analytics-vidhya/deep-learning-overfitting-issu-optimization-techniques-68714400e2ca|Overfitting Issues and Optimization Techniques in Neural Network]]
  * [[https://towardsdatascience.com/overview-of-4-model-validation-approaches-to-mitigate-overfitting-problem-6d2eecdf8053|Overview Of 4 Model Validation Approaches to Mitigate Overfitting Problem]]
  * [[https://towardsdatascience.com/4-ways-to-reduce-dimensionality-of-data-8f82e6565a07|4 ways to Reduce Dimensionality of Data]]
  * [[https://www.machinecurve.com/index.php/2020/02/18/how-to-use-k-fold-cross-validation-with-keras/|How to use K-fold Cross Validation with TensorFlow 2 and Keras?]]
  * [[https://medium.com/mlearning-ai/bagging-classifier-ee6477f5193b|Bagging Classifier]]: Genera conjuntos de entrenamiento cogiendo subconjuntos de las columnas.
  * [[https://towardsdatascience.com/stop-using-all-your-features-for-modeling-82d487ca8ddd|Stop Using All Your Features for Modeling]]
  * [[https://medium.com/@shahzadabbas38/how-to-handle-imbalanced-data-4fdceb8bbe04|Handling Imbalanced Data]]
  * [[https://towardsdatascience.com/strategies-and-tactics-for-regression-on-imbalanced-data-61eeb0921fca|Strategies and Tactics for Regression on Imbalanced Data]]
  * [[https://medium.com/softplus-publication/generate-new-samples-non-linearly-using-crucio-tkrknn-5a8f6aeb2b0b|Generate new samples non-linearly using Crucio TKRKNN.]]
  * [[https://medium.com/analytics-vidhya/the-curse-of-dimensionality-and-its-cure-f9891ab72e5c|The Curse of Dimensionality and its Cure]]:  Gráfica interesante de las dimensiones vs precisión
  * [[https://ashwinhprasad.medium.com/cross-validation-what-why-and-how-machine-learning-f8a1159ce5ff|Cross Validation - What, Why and How | Machine Learning]]
  * [[https://towardsdatascience.com/examples-of-bias-variance-tradeoff-in-deep-learning-6420476a20bd|Examples of Bias Variance Tradeoff in Deep Learning]]
  * [[https://medium.com/nerd-for-tech/nominal-and-ordinal-encoding-in-data-science-c93872601f16|Nominal And Ordinal Encoding In Data Science!]]
  * [[https://medium.com/analytics-vidhya/deep-learning-overfitting-issu-optimization-techniques-68714400e2ca|Overfitting Issues and Optimization Techniques in Neural Network]]
  * [[https://medium.com/gadictos/overfitting-and-regularization-in-machine-learning-e8e60aaf37f9|Overfitting and Regularization in Machine Learning]]
  * [[https://towardsdatascience.com/batch-norm-explained-visually-how-it-works-and-why-neural-networks-need-it-b18919692739|Batch Norm Explained Visually — How it works, and why neural networks need it]]
  * [[https://medium.com/nerd-for-tech/batch-normalization-51e32053f20|Batch Normalization]]
  * [[https://towardsdatascience.com/examples-of-bias-variance-tradeoff-in-deep-learning-6420476a20bd|Examples of Bias Variance Tradeoff in Deep Learning]]: Buena explicación
  * Bias
    * [[https://afaghasanly95.medium.com/introduction-to-biasness-in-ai-part-1-6665f30a92de|Introduction to biasness in AI — part 1]]
    * [[https://afaghasanly95.medium.com/biasness-in-ai-part-2-552c6286197c|Biasness in AI— part 2]]
    * [[https://afaghasanly95.medium.com/biasness-in-ai-part-3-8f6edd386cc0|Biasness in AI- part 3]]
    * [[https://afaghasanly95.medium.com/biasness-in-ai-part-4-f5e67d0db578|Biasness in AI — part 4]]
===== Métricas =====

  * [[https://stats.stackexchange.com/questions/370861/why-is-cross-entropy-not-a-common-evaluation-metric-for-model-performance|Why is cross entropy not a common evaluation metric for model performance?]]
  * [[https://stats.stackexchange.com/questions/123401/confidence-intervals-for-the-log-loss-metric-for-model-comparison|Confidence intervals for the Log Loss metric for model comparison?]]



===== Funciones de coste =====
  * [[https://sitiobigdata.com/2018/08/27/machine-learning-metricas-regresion-mse|Aprendizaje automatico y las Metricas de regresión]]
  * [[https://sitiobigdata.com/2019/05/27/modelos-de-machine-learning-metricas-de-regresion-mse-parte-2/|Aprendizaje automático de métricas de regresión (MSE)]]
  * [[https://sitiobigdata.com/2019/01/19/machine-learning-metrica-clasificacion-parte-3/|Machine Learning: Seleccion Metricas de clasificacion]]




===== Redes neuronales =====
  * [[https://github.com/nitesh4146/Vanilla-Neural-Network|Vanilla Neural Network]]: Ejemplo básico en Python de una red neuronal
  * [[https://medium.com/@zeeshanmulla/cost-activation-loss-function-neural-network-deep-learning-what-are-these-91167825a4de|Cost, Activation, Loss Function.Neural Network.Deep Learning. What are these?]]: Explicación de funciones de coste y de activación
  * [[https://medium.com/deep-learning-sessions-lisboa/neural-netwoks-419732d6afc0|From Linear Regression to Neural Networks: Why and How]]: Interesan los dibujos 
  * [[https://urwamuaz.medium.com/neural-networks-fail-on-data-with-spurious-correlation-b5010b107c32|Case Study: Is Spurious Correlations the reason why Neural Networks fail on unseen data?]]: Falla con una vaca en el desierto y una vaca en la nieve. Piensa que es un camello y un oso polar
  * [[https://kaushikmoudgalya.medium.com/paper-walkthrough-matrix-calculus-for-deep-learning-part-1-a0991220b7ae|Paper Walkthrough — Matrix Calculus for Deep Learning (Part 1 / 2)]]: Explicación del Jacobiano
  * Batch:
    * [[https://medium.com/analytics-vidhya/optimizers-for-machine-learning-302323e4e896|Optimizers for machine learning]]
    * [[https://medium.com/deep-learning-experiments/effect-of-batch-size-on-neural-net-training-c5ae8516e57|Effect of Batch Size on Neural Net Training]]: Muy bueno
    * [[https://medium.com/analytics-vidhya/all-you-need-to-know-about-gradient-descent-f0178c19131d|All you need to know about Gradient Descent]]
    * [[https://towardsdatascience.com/batch-mini-batch-and-stochastic-gradient-descent-for-linear-regression-9fe4eefa637c|Batch, Mini-Batch and Stochastic Gradient Descent for Linear Regression]]
===== Utilidades =====
  * [[https://playground.tensorflow.org/|A Neural Network Playground]]: Simuladores de redes neuronales
  * [[http://alexlenail.me/NN-SVG/index.html|NN-SVG]]: Dibujar redes neuronales
  * [[https://neptune.ai/|Neptune AI]]
  * [[https://www.tensorflow.org/tensorboard|TensorBoard]]



===== Redes Neuronales Recurrentes, transformers, series de tiempo =====

  * [[https://medium.com/datos-y-ciencia/predicci%C3%B3n-con-series-de-tiempo-una-gu%C3%ADa-inicial-2bd62d55675a|Predicción con Series de Tiempo - Una guía inicial]]
  * [[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]]


===== 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/]]


===== Kaggle =====
  * [[https://www.kaggle.com/mlg-ulb/creditcardfraud|Kaggle. Credit Card Fraud Detection]]

===== Autoencoders =====

    * [[https://medium.com/nerd-for-tech/simplifying-the-concept-of-autoencoders-cb686f72f6db|Simplifying the concept of Autoencoders]]


===== Redes Convolutionales =====

  * [[https://kinder-chen.medium.com/basics-of-convolutional-neural-networks-stride-and-pooling-4f9c2b5a6193|Basics of Convolutional Neural Networks — Stride and Pooling]]: Explica las 2 partes de una reco convolicional: Stride y Pooling
  * [[https://ayannair2021.medium.com/variational-autoencoders-simply-explained-46e6f97947ed|Variational Autoencoders Simply Explained]]
  * [[https://towardsdatascience.com/understanding-convolutions-by-hand-vs-tensorflow-8e64053f673e|Understanding Convolutions by hand vs TensorFlow]]
  * [[https://www.nctm.org/Classroom-Resources/Illuminations/Interactives/Isometric-Drawing-Tool/|Isometric Drawing Tool]]

===== AutoML =====
  * [[https://medium.com/neptune-ai/a-quickstart-guide-to-auto-sklearn-automl-for-machine-learning-practitioners-b9931988bc11|A Quickstart Guide to Auto-Sklearn (AutoML) for Machine Learning Practitioners]]
  * [[https://towardsdatascience.com/bayesian-hyper-parameter-optimization-neural-networks-tensorflow-facies-prediction-example-f9c48d21f795|Bayesian Hyper-Parameter Optimization: Neural Networks, TensorFlow, Facies Prediction Example]]
  * [[https://dcpatton.medium.com/hyperparameter-optimization-with-the-keras-tuner-part-2-acc05612e05e|Hyperparameter Optimization with the Keras Tuner, Part 2]]
  * [[https://medium.com/geekculture/10-hyperparameters-to-keep-an-eye-on-for-your-lstm-model-and-other-tips-f0ff5b63fcd4|10 Hyperparameters to keep an eye on for your LSTM model — and other tips]]
  * [[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]]


===== Estadística Bayesana =====

  * [[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}} 
  * [[https://en.wikipedia.org/wiki/Pre-_and_post-test_probability|Pre- and post-test probability]]




  * {{ :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"


===== Pipelines , MLOps =====
  * [[https://medium.com/prosus-ai-tech-blog/towards-mlops-technical-capabilities-of-a-machine-learning-platform-61f504e3e281|Towards MLOps: Technical capabilities of a Machine Learning platform]]
  * [[https://sergiorubiano.medium.com/exponer-modelo-de-clasificaci%C3%B3n-mediante-tensorflow-y-fastapi-e9e27d5f1ad8|Exponer Modelo de clasificación Mediante Tensorflow y FastAPI]]
  * [[https://towardsdatascience.com/write-and-train-your-own-custom-machine-learning-models-using-pycaret-8fa76237374e|Write and train your own custom machine learning models using PyCaret]]
  * [[https://arshren.medium.com/monitoring-machine-learning-models-43c44397256c|Monitoring Machine Learning Models]]
  * [[https://laszewski.medium.com/easy-benchmarking-of-long-running-programs-82059d9c67ce|Easy Benchmarking of Long-Running Programs]]
  * [[https://medium.com/analytics-vidhya/colab-vs-code-github-jupyter-perfect-for-deep-learning-2b257ae94d01|Colab + Vs Code + GitHub + Jupyter (Perfect for Deep Learning)]]
  * [[https://www.tensorflow.org/guide/keras/save_and_serialize|Guardando y Serializando Modelos con TensorFlow Keras]]




===== Gráficos =====

  * [[https://www.geeksforgeeks.org/change-axis-labels-set-title-and-figure-size-to-plots-with-seaborn/|Change Axis Labels, Set Title and Figure Size to Plots with Seaborn]]
  * [[https://becominghuman.ai/7-open-source-libraries-for-deep-learning-graphs-7ae294f249d4|7 Open Source Libraries for Deep Learning Graphs]]
  * [[https://bokeh.org/|The Bokeh Visualization Library]]
  * [[https://www.graphext.com/|graphext]]
  * [[https://altair-viz.github.io/|Altair: Declarative Visualization in Python]]

===== Correlación =====

  * [[https://medium.com/tamed-artificial-intelligence/neural-networks-fail-on-data-with-spurious-correlation-b5010b107c32|Neural Networks fail on data with Spurious Correlation]]
  * [[https://niharjamdar.medium.com/what-is-multicollinearity-and-how-to-resolve-it-35bef9b31fc4|What is MultiCollinearity and how to resolve it?]]: Ejemplo de correlación 
  * [[https://towardsdatascience.com/pearson-and-spearman-rank-correlation-coefficient-explained-60811e61185a|Pearson and Spearman Rank Correlation Coefficient — Explained]]
  * [[https://thepythongeek.medium.com/covariance-correlation-and-r-squared-explained-with-python-df1d52bfa44b|Covariance, Correlation and R-Squared Explained with Python]] 
  * [[https://imswapnilb.medium.com/correlation-in-statistics-53c44946e3a4|Correlation in Statistics]]
  * [[https://atobek-rahimshoev.medium.com/when-to-use-spearman-correlation-e9573292463b|When To Use Spearman Correlation?]]




===== Estadística =====

  * [[https://towardsdatascience.com/fundamentals-of-statistics-for-data-scientists-and-data-analysts-69d93a05aae7|Fundamentals Of Statistics For Data Scientists and Analysts]]
  * [[https://medium.com/mlearning-ai/important-statistical-concepts-for-data-scientists-54e09106b75e|Basic Statistics for Data Science]]
  * [[https://relopezbriega.github.io/blog/2015/06/27/probabilidad-y-estadistica-con-python/|Probabilidad y Estadística con Python]]
  * [[https://www.statisticssolutions.com/to-err-is-human-what-are-type-i-and-ii-errors/|To Err is Human: What are Type I and II Errors?]]: Foto que explica muy facilmente los errores de tipo I y tipo II
  * [[https://dhamodharans.medium.com/scatter-plots-in-data-analysis-b3881b1e8a6b|Scatter Plots In Data Analysis]]: La importancia de los Scatter Plots
  * [[https://medium.com/nerd-for-tech/anomaly-detection-techniques-5fdfbab9180a|Anomaly Detection Techniques]]
  * [[https://medium.com/@chetnakhanna/bar-chart-or-histogram-4d38f36dd135|Bar Chart or Histogram?]]
  * [[https://jllopisperez.com/2021/05/28/situacion-179-practicas-de-biomedicina/|Situación 179: Prácticas de BIOMEDICINA]]
  * [[https://magnet.xataka.com/en-diez-minutos/que-paradoja-simpson-que-va-a-ser-muy-util-para-entender-proximos-meses-pandemia|Qué es la Paradoja de Simpson y por qué va a ser muy útil para entender los próximos meses de pandemia]]
  * Regresión lineal
    * [[https://sandhyakrishnan02.medium.com/linear-regression-assumptions-eb7a760e4434|Linear Regression Assumptions]]: Que se asume para que una regresión linear se pueda realizar.
    * [[https://towardsdatascience.com/probabilistic-interpretation-of-linear-regression-clearly-explained-d3b9ba26823b|Probabilistic interpretation of linear regression clearly explained]]: Explica que el error sigue una distribución y el ½
  * Distribuciones
    * [[https://altafansari267.medium.com/the-benefits-of-central-limit-theorem-2eda88571a9b|The Benefit's Of Central Limit Theorem]]: Explicación de la utilidad de la distribución normal
    * [[https://shandou.medium.com/related-probability-distributions-6f28f8cc6fd8|Related Probability Distributions]]: Explicación de 3 distribuciones.
    * [[https://rdzudzar.github.io/scipy_distributions.html|Visualizing Continuous distributions from SciPy]]: Imagen con muchas distribuciones.
    * [[https://share.streamlit.io/rdzudzar/distributionanalyser/main/main.py|Distribution Analyser]]: Información sobre distribuciones y si se ajustan a los datos

{{:clase:iabd:pia:scipy_stats.png?200|}}

{{:clase:iabd:pia:paradoja_simpson.jpeg?200|}}

===== Matemáticas =====

  * [[https://medium.datadriveninvestor.com/a-beginners-guide-to-linear-algebra-for-deep-learning-d7631d03f430|A Beginner’s Guide to Linear Algebra for Deep Learning]]:Escalares, vectores, matrices y tensores
  * [[https://kaushikmoudgalya.medium.com/paper-walkthrough-matrix-calculus-for-deep-learning-part-2-61579011bf8c|Paper Walkthrough — Matrix Calculus for Deep Learning (Part 2 / 2)]]
  * [[https://pub.towardsai.net/some-maths-resources-to-help-you-in-your-ml-journey-6306bcc11c43|Some Maths Resources to Help You in Your ML Journey]]



===== TensorFlow Avanzado =====

  * [[https://medium.com/geekculture/lets-create-tensors-like-numpy-arrays-90a4cf32144|Let’s Create Tensors like NumPy Arrays]]
  * [[https://medium.com/geekculture/linear-algebra-with-tensorflow-755598b6eb46|Linear Algebra with TensorFlow]]: Ejemplos de cálculos con TensorFlow en vez de usar numpy 
  * [[https://towardsdatascience.com/creating-and-training-custom-layers-in-tensorflow-2-6382292f48c2|Creating and Training Custom Layers in TensorFlow 2]]
  * [[https://towardsdatascience.com/creating-custom-loss-functions-using-tensorflow-2-96c123d5ce6c|Creating custom Loss functions using TensorFlow 2]]
  * [[https://towardsdatascience.com/creating-custom-activation-functions-with-lambda-layers-in-tensorflow-691398b8a52d|Creating Custom Activation Functions with Lambda Layers in TensorFlow 2]]
  * [[https://towardsdatascience.com/3-keras-design-patterns-every-ml-engineer-should-know-cae87618c7e3|3 Keras Design Patterns Every ML Engineer Should Know]]


<sxh python>
import tensorflow as tf
tf.config.list_physical_devices()
</sxh>

<sxh python>
from tensorflow.python.client import device_lib
device_lib.list_local_devices()
</sxh>

<sxh python>
device=device_lib.list_local_devices()[0]
with tf.device(device.name):
    history=model.fit(x, y,epochs=epochs,verbose=False)
</sxh> 


===== Natural Language Processing =====

  * [[https://www.pinecone.io/learn/nlp/|Natural Language Processing (NLP) for Semantic Search]]

===== EDA =====

  * [[https://dzone.com/articles/imputing-missing-data-using-sklearn-simpleimputer|Imputing Missing Data Using Sklearn SimpleImputer]]
  * [[https://medium.com/geekculture/everything-youve-ever-wanted-to-know-about-exploratory-data-analysis-eda-part-1-7b48782db774|Everything You’ve Ever Wanted to Know About Exploratory Data Analysis (EDA) Part — 1]]
  * [[https://medium.com/@arunm8489/exploratory-data-analysis-on-habermans-cancer-survival-data-set-3923cda7e3d2|Exploratory Data Analysis On Haberman’s Cancer Survival Data set]]
  * [[https://towardsdatascience.com/making-your-first-kaggle-submission-36fa07739272|Making Your First Kaggle Submission]]
  * [[https://www.analyticsvidhya.com/blog/2021/04/rapid-fire-eda-process-using-python-for-ml-implementation/|Rapid-Fire EDA process using Python for ML Implementation]]
  * [[https://ai.plainenglish.io/a-detailed-pre-processing-in-machine-learning-with-notebook-dfd86dc1cacf|Data Pre-Processing in Machine Learning with 🐍+Notebook]]
  * [[https://sachin-date.medium.com/how-to-estimate-vaccine-efficacy-using-a-logistic-regression-model-121f9ca5a9d8|How to Estimate Vaccine Efficacy Using a Logistic Regression Model]]
  * [[https://medium.com/analytics-vidhya/how-to-find-the-best-performing-machine-learning-algorithm-dc4eb4ff34b6|How to find the best performing Machine Learning algorithm]]
  * [[https://medium.com/mlearning-ai/how-i-solved-kaggles-april-2021-tabular-competition-28b3ed8f465a|How I solved Kaggle’s April 2021 tabular competition]]
  * [[https://jinchuika.medium.com/preprocessing-data-for-data-science-part-1-f78a546ad459|Preprocessing data for data science (Part 1)]]
  * [[https://python.plainenglish.io/data-pre-processing-in-machine-learning-with-python-and-jupyter-55a5676fced6|Data Pre-Processing in Machine Learning with Python and Jupyter]]
  * [[https://medium.com/@harimittapalli/exploratory-data-analysis-iris-dataset-9920ea439a3e|Exploratory Data Analysis : Iris DataSet]]
  * [[https://medium.com/geekculture/step-by-step-guide-to-build-a-logistic-regression-model-in-python-ca42577733fb|Step by Step Guide to Build a Logistic Regression Model in Python]]
  * [[https://medium.com/analytics-vidhya/beginners-guide-to-stock-prediction-using-lstm-7010bf8b8c21|Beginners guide to stock prediction using LSTM.]]
  * [[https://medium.com/geekculture/exploratory-data-analysis-on-meteorological-data-using-python-e56dbeb67e23|Exploratory Data Analysis on Meteorological Data using Python]]
  * [[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://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
    * [[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-hypothesis-testing-part-2-25c07755613c|Solve Machine Learning Problems: Hypothesis Testing (Part-2)]]
    * [[https://medium.com/analytics-vidhya/solve-machine-learning-problems-data-preprocessing-part-3-4988d17ae235|Solve Machine Learning Problems: Data Preprocessing(part-3)]]
    * [[https://medium.com/analytics-vidhya/solve-machine-learning-problem-feature-selection-part-4-cadd00e7da56|Solve Machine Learning problems: Feature Selection (part 4)]]
    * [[https://medium.com/analytics-vidhya/solve-machine-learning-problem-dimensionality-reduction-part-5-cf7676b20164|Solve Machine Learning Problems: Dimensionality Reduction (part-5)]]
    * [[https://medium.com/analytics-vidhya/solve-machine-learning-problem-outlier-detection-part-6-387eb02befaa|Solve Machine Learning Problems: Outlier Detection (part-6)]]
    * [[https://medium.com/analytics-vidhya/solve-machine-learning-problem-regression-model-selection-part-7-4b58bd65c6d8|Solve Machine Learning Problems: Regression & Model Selection(Part -7)]]




===== Guías =====
  * [[https://hilanth7.medium.com/nine-things-i-learned-about-machine-learning-during-umojahack-africa-2021-8f6a4a2db16a|Nine Things I Learned About Machine Learning During UmojaHack Africa 2021]]
  * [[https://abhimarichi.medium.com/basics-of-data-cleansing-daffb5cad310|Basics of Data Cleansing]]: Las 6 cosas que hay que hacer para limpiar los datos
  * [[https://www.analyticsvidhya.com/blog/2021/05/data-cleaning-libraries-in-python-a-gentle-introduction/|Data Cleaning Libraries In Python: A Gentle Introduction]]
  * [[https://betterprogramming.pub/10-deadly-sins-of-ml-model-training-a5046c1f5094|10 Deadly Sins of ML Model Training]]
  * [[https://saket-shubham16.medium.com/data-analytics-introduction-201d6a734c41|Data Analytics- Introduction]]: Tipos de análisis de datos
  * [[https://medium.com/analytics-vidhya/getting-started-with-data-science-with-python-part-1-456019eebf55|Getting Started with Data Science with Python (Part-1)]]: Que es la ciencia de datos
  * [[https://towardsdatascience.com/top-7-feature-selection-techniques-in-machine-learning-94e08730cd09|Top 7 Feature Selection Techniques in Machine Learning]]
  * [[https://medium.com/geekculture/the-top-25-python-libraries-for-data-science-71c0eb58723d|The Top 25 Python libraries for Data Science]]
  * [[https://medium.com/mlearning-ai/life-cycle-of-data-science-projects-a4739f6e2419|Life Cycle Of Data Science Projects!]]


===== Libros online =====

  * {{ :clase:iabd:pia:dive_into_deep_learning.2021.pdf |Dive into Deep Learning. (2021)}}
  * {{ :clase:iabd:pia:mathematics_for_machine_learning.pdf |Mathematics for Machine Learning-Ed. Cambridge University Press (2020)}}
  * {{ :clase:iabd:pia:the_elements_of_statistical_learning.data_mining_inference_and_prediction.2017.pdf |The Elements of Statistical Learning Data Mining, Inference, and Prediction-Ed Springer (2017)}}
  * [[https://www.deeplearningbook.org/|Deep Learning-Ed. MIT Press (2016)]]
  * [[http://neuralnetworksanddeeplearning.com/|Neural Networks and Deep Learning-Ed Determination Press (2015)]]
  * {{ :clase:iabd:pia:pattern-recognition-and-machine-learning-2006.pdf |Pattern Recognition and Machine Learning-Ed. Microsfot Press (2006) }}



 


===== Compendios =====

  * [[https://becominghuman.ai/58-resources-to-help-get-started-with-deep-learning-in-tf-9f29ba585e0f|58 Resources To Help Get Started With Deep Learning ( In TF )]]
  * [[https://www.iartificial.net/guia-rapida-iartificial-net/|Guía rápida de IArtificial.net]]
  * [[https://distill.pub/|Distill — Latest articles about machine learning]]: Papers muy buenos sobre IA. Tienen mucho nivel.
  * [[https://medium.com/geekculture/the-complete-reference-to-data-science-ml-ai-and-related-concepts-f21fcf75af2c|The Complete Reference to Data Science (ML/AI) and Related Concepts]]
  * [[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://www.cienciadedatos.net/|Ciencia de datos, teoría y ejemplos prácticos en R y Python]]

===== Humor =====
  * [[https://es.xkcd.com/strips/extrapolar/|extrapolar]]

===== Python =====
  * Charla: "Python packaging: lo estás haciendo mal"
    * [[https://www.youtube.com/watch?v=OeOtIEDFr4Y|Video de la charla]]: 
    * [[https://nbviewer.jupyter.org/format/slides/github/astrojuanlu/charla-python-packaging/blob/main/Charla%20Python%20packaging.ipynb#/|Diapositivas]]
  * [[https://mypy.readthedocs.io/en/latest/installed_packages.html#creating-pep-561-compatible-packages|Creating PEP 561 compatible packages]]
  * [[https://preettheman.medium.com/how-to-make-a-python-package-2708867e09ef|How to make a Python package]]
  * [[https://gandhimanan1.medium.com/installing-anaconda-for-windows-10-8-7-46014696c103|Installing Anaconda for Windows 10/8/7]]
  * [[https://marketplace.visualstudio.com/items?itemName=VisualStudioExptTeam.vscodeintellicode|Visual Studio IntelliCode]]
  * Google Colaboratory: [[https://jainil-parikh.medium.com/make-google-colab-wait-for-you-1de95b5f96c9|Make Google Colaboratory GPU session wait for you.]]
  * [[https://colab.research.google.com/|Google Colab]]
  * [[https://towardsdatascience.com/an-informative-colab-guide-to-load-image-datasets-from-github-kaggle-and-local-machine-75cae89ffa1e|How to Efficiently Load Image Datasets into Colab from Github, Kaggle and Local Machine]]
  * [[https://towardsdatascience.com/10-tips-tricks-for-working-with-large-datasets-in-machine-learning-7065f1d6a802|8 Tips and Tricks for Working with Large Datasets in Machine Learning]]


<note important>
  * Estructura de ficheros: [[https://github.com/drivendata/cookiecutter-data-science]]
</note>


  * 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>
poetry config virtualenvs.in-project true
poetry config --list

#se debe instalar siempre esta dependencia
poetry add ipykernel

#instalar
poetry install

#ejecutar código python
poetry run python my_script.py
</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}}


===== Intervalos de confianza =====

  * Teoria
    * [[https://sebastianraschka.com/blog/2022/confidence-intervals-for-ml.html|Creating Confidence Intervals for Machine Learning Classifiers]]
    * [[https://hmong.es/wiki/Wilson_score_interval|Intervalo de confianza de la proporción binomial]]
    * {{ :clase:iabd:pia:2eval:intervalos_de_confianza_para_las_estimaciones_de_proporciones_y_las_diferencias_entre_ellas.pdf |Intervalos de confianza para las estimaciones de proporciones y las diferencias entre ellas}}
    * {{ :clase:iabd:pia:2eval:capitulo_5.inferencia_estadistica_ii_estimacion_por_intervalo_de_confianza.pdf |Capitulo 5.Inferencia Estadística II: Estimación por Intervalo de Confianza}}
    * {{ :clase:iabd:pia:2eval:intervalos_de_confianza_e_intervalos_de_credibilidad_para_una_proporcion.pdf |Intervalos de confianza e intervalos de credibilidad para una proporción}}
    * {{ :clase:iabd:pia:2eval:bioestadistica_para_no_estadisticos.08_intervalos_de_confianza.pdf |Bioestadistica para no estadisticos. Intervalos de confianza}}
    * {{ :clase:iabd:pia:2eval:understanding_and_interpreting_confidence_and_credible_intervals_around_effect_estimates.pdf |Understanding and interpreting confidence and credible intervals around effect estimates}}
  * Herramientas:
    * [[https://www.causascientia.org/math_stat/ProportionCI.html|Exact Confidence Interval for a Proportion]]:Herramienta online para calcular intervalos de confianza con el teorema de bayes
    * [[https://epitools.ausvet.com.au/ciproportion|Límites de confianza para una proporción]]: Herramienta online para calcular intervalos de confianza.
  * Código Python
    * [[https://www.statsmodels.org/v0.12.2/generated/statsmodels.stats.proportion.proportion_confint.html|statsmodels.stats.proportion.proportion_confint]]: Calculo en Pyhton de intervalos de confianza.
    * [[https://akshay-a.medium.com/confidence-interval-for-population-proportion-basic-understanding-in-python-56b8cc5f8320|Confidence Interval for Population Proportion basic understanding in python]]

===== Estadística bayesiana y precision =====
  * {{{{ :clase:iabd:pia:2eval:las_pruebas_pcr_con_elevada_sensibilidad_y_especificidad_en_condiciones_de_alta_prevalencia_o_bajo_prescripcion_medica_son_fiables.pdf |Las pruebas PCR con elevada sensibilidad y especificidad, en condiciones de alta prevalencia o bajo prescripción médica, son fiables}}
  * {{ :clase:iabd:pia:2eval:6-ayuda_pruebas_diagnsticas.pdf |Pruebas diagnosticas}}: Explicación desde el punto de vista médico de Bayes con la especificidad y sensibilidad
  * {{ :clase:iabd:pia:2eval:falsos_negativos_en_tests_de_covid-19.pdf |Falsos negativos en tests de COVID-19}}
  * {{ :clase:iabd:pia:2eval:commentary_sensitivity_specificity_and_predictive_values_foundations_pliabilities_and_pitfalls_in_research_and_practice.pdf |Commentary: Sensitivity, Specificity,and Predictive Values: Foundations,Pliabilities, and Pitfalls in Research and Practice}}
  * {{ :clase:iabd:pia:2eval:comentario_sensibilidad_especificidad_y_valores_predictivos_fundamentos_flexibilidades_y_dificultades_en_la_investigacion_y_la_practica.pdf |Comentario: Sensibilidad, especificidad y valores predictivos: fundamentos,flexibilidades y dificultades en la investigación y la práctica}}: El documento anterior pero traducido al castellano.
  * Prevalencia
    * {{ :clase:iabd:pia:2eval:riesgo_de_contagio_por_covid-19_en_funcion_del_tipo_de_contacto_y_de_la_renta_familiar.pdf |Riesgo de contagio por COVID-19 en función del tipo de contacto y de la renta familiar}}
    * {{ :clase:iabd:pia:2eval:prevalencia_de_infeccion_por_coronavirus_sars-cov-2_en_pacientes_y_profesionales_de_un_hospital_de_media_y_larga_estancia_en_espana.pdf |Prevalencia de infección por coronavirus SARS-CoV-2 en pacientes y profesionales de un hospital de media y larga estancia en España}}

===== Probabilidad del resultado de la red =====
  * {{ :clase:iabd:pia:2eval:introduction_to_uncertainty_in_deep_learning.pdf |Introduction to Uncertainty in Deep Learning}}
  * {{ :clase:iabd:pia:2eval:calibrar_modelos_de_machine_learning.pdf |Calibrar modelos de machine learning}}
  * [[https://wttech.blog/blog/2021/a-guide-to-model-calibration/|A guide to model calibration]]

  * {{ :clase:iabd:pia:2eval:predicting_good_probabilities_with_supervised_learning.pdf |Predicting Good Probabilities With Supervised Learning}}
  * {{ :clase:iabd:pia:2eval:neural_network_prediction_scores_are_not_probabilities.pdf |Neural Network Prediction Scores are not Probabilities}}
  * [[https://machinelearningmastery.com/calibrated-classification-model-in-scikit-learn/|How and When to Use a Calibrated Classification Model with scikit-learn]]
  * scikit-learn
    * [[https://scikit-learn.org/stable/auto_examples/calibration/plot_calibration_curve.html|Probability Calibration curves]]
    * [[https://scikit-learn.org/stable/modules/generated/sklearn.calibration.calibration_curve.html|sklearn.calibration.calibration_curve]]
    * [[https://scikit-learn.org/stable/modules/generated/sklearn.calibration.CalibratedClassifierCV.html|sklearn.calibration.CalibratedClassifierCV]]
  * Regresión logística isotónica
  * Calibración de Platt

===== PyMC3 =====
  * {{ :clase:iabd:pia:2eval:bayesian_linear_regression_in_python_using_machine_learning_to_predict_student_grades_part_2.pdf |Bayesian Linear Regression in Python: Using Machine Learning to Predict Student Grades Part 2}}



===== Hardware =====



{{:clase:iabd:pia:guide-become-data-scientist.jpeg?200|}}