Documentación

Overfitting, validación , reducción de la dimensionalidad, datos entrada

An overview of time-aware cross-validation techniques

Overfitting Issues and Optimization Techniques in Neural Network

Overview Of 4 Model Validation Approaches to Mitigate Overfitting Problem

4 ways to Reduce Dimensionality of Data

How to use K-fold Cross Validation with TensorFlow 2 and Keras?

Bagging Classifier: Genera conjuntos de entrenamiento cogiendo subconjuntos de las columnas.

Stop Using All Your Features for Modeling

Handling Imbalanced Data

Strategies and Tactics for Regression on Imbalanced Data

Generate new samples non-linearly using Crucio TKRKNN.

The Curse of Dimensionality and its Cure: Gráfica interesante de las dimensiones vs precisión

Cross Validation - What, Why and How | Machine Learning

Examples of Bias Variance Tradeoff in Deep Learning

Nominal And Ordinal Encoding In Data Science!

Overfitting Issues and Optimization Techniques in Neural Network

Overfitting and Regularization in Machine Learning

Batch Norm Explained Visually — How it works, and why neural networks need it

Batch Normalization

Examples of Bias Variance Tradeoff in Deep Learning: Buena explicación

Bias

Introduction to biasness in AI — part 1

Biasness in AI— part 2

Biasness in AI- part 3

Biasness in AI — part 4

Métricas

Why is cross entropy not a common evaluation metric for model performance?

Confidence intervals for the Log Loss metric for model comparison?

Funciones de coste

Aprendizaje automatico y las Metricas de regresión

Aprendizaje automático de métricas de regresión (MSE)

Machine Learning: Seleccion Metricas de clasificacion

Redes neuronales

Vanilla Neural Network: Ejemplo básico en Python de una red neuronal

Cost, Activation, Loss Function.Neural Network.Deep Learning. What are these?: Explicación de funciones de coste y de activación

From Linear Regression to Neural Networks: Why and How: Interesan los dibujos

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

Paper Walkthrough — Matrix Calculus for Deep Learning (Part 1 / 2): Explicación del Jacobiano

Batch:

Optimizers for machine learning

Effect of Batch Size on Neural Net Training: Muy bueno

All you need to know about Gradient Descent

Batch, Mini-Batch and Stochastic Gradient Descent for Linear Regression

Utilidades

A Neural Network Playground: Simuladores de redes neuronales

NN-SVG: Dibujar redes neuronales

Neptune AI

TensorBoard

Redes Neuronales Recurrentes, transformers, series de tiempo

Predicción con Series de Tiempo - Una guía inicial

Transformers Explained Visually — Not just how, but Why they work so well

Feature Engineering of DateTime Variables for Data Science, Machine Learning, Python

Lenguaje Natural

BERT

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

Kaggle. Credit Card Fraud Detection

Autoencoders

Simplifying the concept of Autoencoders

Redes Convolutionales

Basics of Convolutional Neural Networks — Stride and Pooling: Explica las 2 partes de una reco convolicional: Stride y Pooling

Variational Autoencoders Simply Explained

Understanding Convolutions by hand vs TensorFlow

Isometric Drawing Tool

AutoML

A Quickstart Guide to Auto-Sklearn (AutoML) for Machine Learning Practitioners

Bayesian Hyper-Parameter Optimization: Neural Networks, TensorFlow, Facies Prediction Example

Hyperparameter Optimization with the Keras Tuner, Part 2

10 Hyperparameters to keep an eye on for your LSTM model — and other tips

Hyperparameter search using Bayesian Optimization and an Evolutionary Algorithm

How to Use Horovod’s Large Batch Simulation to Optimize Hyperparameter Tuning for (Highly) Distributed Training

Estadística Bayesana

Viendo la Teoría - Inferencia Bayesiana

¿Cómo funciona la inferencia bayesiana?

Exploring Bayesian Optimization: Muy buena explicación de la optimización Bayesiana

La estadística detrás del rescate de la bomba de Palomares

Bayes’ Theorem: The Holy Grail of Data Science

Probability Learning II: How Bayes’ Theorem is applied in Machine Learning

A Gentle Introduction to Bayesian Analysis.Applications to Development Research

Doing Bayesian Data Analysis:A Tutorial with R, JAGS,and Stan

How much of a Bayesian posterior distribution falls inside a region of practical equivalence (ROPE)

Bayesian Estimation Supersedes the t Test (PDF)

Bayesian estimation supersedes the t test

Bayesian Estimation Supersedes the t-test (BEST): Online tool

Bayesian parameter estimation versus model comparison: Critica de "Bayesian estimation supersedes the t test"

Pipelines , MLOps

Towards MLOps: Technical capabilities of a Machine Learning platform

Exponer Modelo de clasificación Mediante Tensorflow y FastAPI

Write and train your own custom machine learning models using PyCaret

Monitoring Machine Learning Models

Easy Benchmarking of Long-Running Programs

Colab + Vs Code + GitHub + Jupyter (Perfect for Deep Learning)

Guardando y Serializando Modelos con TensorFlow Keras

Gráficos

Change Axis Labels, Set Title and Figure Size to Plots with Seaborn

7 Open Source Libraries for Deep Learning Graphs

The Bokeh Visualization Library

graphext

Altair: Declarative Visualization in Python

Correlación

Neural Networks fail on data with Spurious Correlation

What is MultiCollinearity and how to resolve it?: Ejemplo de correlación

Pearson and Spearman Rank Correlation Coefficient — Explained

Covariance, Correlation and R-Squared Explained with Python

Correlation in Statistics

When To Use Spearman Correlation?

Estadística

Fundamentals Of Statistics For Data Scientists and Analysts

Basic Statistics for Data Science

Probabilidad y Estadística con Python

To Err is Human: What are Type I and II Errors?: Foto que explica muy facilmente los errores de tipo I y tipo II

Scatter Plots In Data Analysis: La importancia de los Scatter Plots

Anomaly Detection Techniques

Bar Chart or Histogram?

Situación 179: Prácticas de BIOMEDICINA

Qué es la Paradoja de Simpson y por qué va a ser muy útil para entender los próximos meses de pandemia

Regresión lineal

Linear Regression Assumptions: Que se asume para que una regresión linear se pueda realizar.

Probabilistic interpretation of linear regression clearly explained: Explica que el error sigue una distribución y el ½

Distribuciones

The Benefit's Of Central Limit Theorem: Explicación de la utilidad de la distribución normal

Related Probability Distributions: Explicación de 3 distribuciones.

Visualizing Continuous distributions from SciPy: Imagen con muchas distribuciones.

Distribution Analyser: Información sobre distribuciones y si se ajustan a los datos

Matemáticas

A Beginner’s Guide to Linear Algebra for Deep Learning:Escalares, vectores, matrices y tensores

Paper Walkthrough — Matrix Calculus for Deep Learning (Part 2 / 2)

Some Maths Resources to Help You in Your ML Journey

TensorFlow Avanzado

Let’s Create Tensors like NumPy Arrays

Linear Algebra with TensorFlow: Ejemplos de cálculos con TensorFlow en vez de usar numpy

Creating and Training Custom Layers in TensorFlow 2

Creating custom Loss functions using TensorFlow 2

Creating Custom Activation Functions with Lambda Layers in TensorFlow 2

3 Keras Design Patterns Every ML Engineer Should Know

Natural Language Processing

Natural Language Processing (NLP) for Semantic Search

EDA

Imputing Missing Data Using Sklearn SimpleImputer

Everything You’ve Ever Wanted to Know About Exploratory Data Analysis (EDA) Part — 1

Exploratory Data Analysis On Haberman’s Cancer Survival Data set

Making Your First Kaggle Submission

Rapid-Fire EDA process using Python for ML Implementation

Data Pre-Processing in Machine Learning with 🐍+Notebook

How to Estimate Vaccine Efficacy Using a Logistic Regression Model

How to find the best performing Machine Learning algorithm

How I solved Kaggle’s April 2021 tabular competition

Preprocessing data for data science (Part 1)

Data Pre-Processing in Machine Learning with Python and Jupyter

Exploratory Data Analysis : Iris DataSet

Step by Step Guide to Build a Logistic Regression Model in Python

Beginners guide to stock prediction using LSTM.

Exploratory Data Analysis on Meteorological Data using Python

Exploratory Data Analysis(EDA) y Exploratory Data Analysis(Part 2)

Concrete Strength Modeling with 5 Machine Learning Algorithms

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: EDA (part-1)

Solve Machine Learning Problems: Hypothesis Testing (Part-2)

Solve Machine Learning Problems: Data Preprocessing(part-3)

Solve Machine Learning problems: Feature Selection (part 4)

Solve Machine Learning Problems: Dimensionality Reduction (part-5)

Solve Machine Learning Problems: Outlier Detection (part-6)

Solve Machine Learning Problems: Regression & Model Selection(Part -7)

Guías

Nine Things I Learned About Machine Learning During UmojaHack Africa 2021

Basics of Data Cleansing: Las 6 cosas que hay que hacer para limpiar los datos

Data Cleaning Libraries In Python: A Gentle Introduction

10 Deadly Sins of ML Model Training

Data Analytics- Introduction: Tipos de análisis de datos

Getting Started with Data Science with Python (Part-1): Que es la ciencia de datos

Top 7 Feature Selection Techniques in Machine Learning

The Top 25 Python libraries for Data Science

Life Cycle Of Data Science Projects!

Libros online

Dive into Deep Learning. (2021)

Mathematics for Machine Learning-Ed. Cambridge University Press (2020)

The Elements of Statistical Learning Data Mining, Inference, and Prediction-Ed Springer (2017)

Deep Learning-Ed. MIT Press (2016)

Neural Networks and Deep Learning-Ed Determination Press (2015)

Pattern Recognition and Machine Learning-Ed. Microsfot Press (2006)

Compendios

58 Resources To Help Get Started With Deep Learning ( In TF )

Guía rápida de IArtificial.net

Distill — Latest articles about machine learning: Papers muy buenos sobre IA. Tienen mucho nivel.

The Complete Reference to Data Science (ML/AI) and Related Concepts

Important Links of Articles in Data Science and Deep Learning

CS 189/289A Introduction to Machine Learning: Curso de IA de la universidad de Berkeley

Ciencia de datos, teoría y ejemplos prácticos en R y Python

Humor

extrapolar

Python

Charla: "Python packaging: lo estás haciendo mal"

Video de la charla:

Diapositivas

Creating PEP 561 compatible packages

How to make a Python package

Installing Anaconda for Windows 10/8/7

Visual Studio IntelliCode

Google Colaboratory: Make Google Colaboratory GPU session wait for you.

Google Colab

How to Efficiently Load Image Datasets into Colab from Github, Kaggle and Local Machine

8 Tips and Tricks for Working with Large Datasets in Machine Learning

Estructura de ficheros: https://github.com/drivendata/cookiecutter-data-science

Poetry

Are You Still Using Virtualenv for Managing Dependencies in Python Projects?

Python projects with Poetry and VSCode. Part 1

Python projects with Poetry and VSCode. Part 2

Crear un colormap personalizado

Hardware