Graph Neural Network - Step 1

 

References:

[1] Short review https://neptune.ai/blog/graph-neural-network-and-some-of-gnn-applications

[2] Coding GNN https://keras.io/examples/graph/gnn_citations/

[2.1] https://keras.io/examples/graph/gat_node_classification/

AI tools to create videos

[0] Create a Story using chatGPT

[1] Text to speach https://www.speachy.ai  #no includes english/doesnt work

[2] Text and Speach to Video https://invideo.io/

[3] Free Atatars https://www.synthesia.io

Data Visualization tools

 

[1] A new library for LLMs and NNs that provides visual clustering analysis and model interpretability. Super useful.

!pip install arize-phoenix

ML Resources

 

[1] Machine Learning Simplified https://themlsbook.com/

 

 

 

COVID-19

Trackers
[1] https://gisanddata.maps.arcgis.com/apps/opsdashboard/index.html#/bda7594740fd40299423467b48e9ecf6
[2] https://who.sprinklr.com/
[3] Perú https://www.bing.com/covid/local/peru
[4] https://extranet.who.int/publicemergency
[5] Brasil https://olhardigital.com.br/coronavirus/noticia/coronavirus-brasil-registra-904-casos-confirmados-e-11-mortes/98089

Codes
[1] https://laconicml.com/predict-infected-people-coronavirus-python
[2] https://github.com/fincahuanaco/covid19peru

[3] https://www.tecmint.com/convert-files-to-utf-8-encoding-in-linux/

Datasets
[1] Timeseries https://github.com/CSSEGISandData/COVID-19
[2] images https://www.kaggle.com/bachrr/covid-chest-xray/data
[2.1] https://theaisummer.com/medical-image-deep-learning

[3] Brazil daily report https://github.com/wcota/covid19br

Other Datasets

[1] Datasus http://www2.datasus.gov.br/DATASUS/index.php

PCA/WPCA

Video Lectures
[1] SVD to PCA https://www.youtube.com/watch?v=mBcLRGuAFUk

Lectures
[1] https://skymind.ai/wiki/eigenvector
[2] https://www.projectrhea.org/rhea/index.php/PCA_Theory_Examples


[3] PCA Theory Example http://www.cs.otago.ac.nz/cosc453/student_tutorials/principal_components.pdf

[4] Animation 2D PCA https://www.intmath.com/matrices-determinants/eigenvalues-eigenvectors-concept-applet.php


[5] PCA on OpenCV
https://robospace.wordpress.com/2013/10/09/object-orientation-principal-component-analysis-opencv/


[6] Wavelets, Fourier, Kalman http://www.cs.otago.ac.nz/cosc453/





Resources:
[1] https://machinelearningmastery.com/calculate-principal-component-analysis-scratch-python/
[2] https://jakevdp.github.io/PythonDataScienceHandbook/05.09-principal-component-analysis.html
[3] Draw 2D principal axis PCA https://stackoverflow.com/questions/18299523/basic-example-for-pca-with-matplotlib

Machine Learning topics 2019

References:
[1] Encoding categorical data https://medium.com/@rabinpoudyal1995/encoding-categorical-data-in-python-fab150d6e21b
[2] Espectral clustering https://towardsdatascience.com/spectral-clustering-82d3cff3d3b7

Linear Regresion

[1] Regresion linear http://onlinestatbook.com/2/regression/intro.html
[2] https://towardsdatascience.com/linear-regression-with-example-8daf6205bd49

Machine Learning Lectures & Tools

References:

[1] Clustering notes: http://www.cs.toronto.edu/~mbrubake/teaching/C11/Handouts/Clustering.pdf

Datasets:


EMOTIV EPOC+ 14 Channel Mobile EEG https://archive.ics.uci.edu/ml/datasets/EEG+Eye+State#
https://archive.ics.uci.edu/ml/datasets

Resources:

[1] Optimizing parameters of Classifier https://weka.wikispaces.com/Optimizing+parameters
[2] CSV to ARFF online http://ikuz.eu/csv2arff/

Machine Learning Resources

 SVM References code
[1] https://www.youtube.com/watch?v=HtdhnGp-m74
Code
[1] iris and others
http://archive.ics.uci.edu/ml/datasets.html
[2] Tutorial code for SVM/NN and DecisionTree
http://breckon.eu/toby/teaching/ml/examples/c++/handwritten_ex/
[3] Adaboost source code
     https://github.com/huneng


People
[1] Sift author https://scholar.google.fr/citations?hl=en&user=PXm1lPAAAAAJ

About Feature selection

References:
[1] http://machinelearningmastery.com/an-introduction-to-feature-selection/