# From my talk at PyConES 2018

On past October, I gave a talk “Building APIs in no time using Flask” at PyConES in Malaga, Spain.

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# From my talk at PyConES 2018

# An introduction to Gradient Descent Algorithm

# From my talk for Google I/O extended in Guatemala

# Simple guide to Confusion Matrix

# L0 Norm, L1 Norm, L2 Norm & L-Infinity Norm

# Easy and quick explanation: Naive Bayes algorithm

# About my experience in La Idea Incubator program

On past October, I gave a talk “Building APIs in no time using Flask” at PyConES in Malaga, Spain.

Gradient Descent is one of the most used algorithms in Machine Learning and Deep Learning.

On past April 9th, I gave a talk for the Google I/O extended in Guatemala about Google Colaboratory.

Putting it in a few words, a confusion matrix is a summarization of the performance of an algorithm. It is a table that describes the performance of a classifier model with known labels.

First of all, what is a Norm? In Linear Algebra, a Norm refers to the total length of all the vectors in a space.

Naive Bayes is one of the simplest yet reliable algorithms used in Machine Learning, particularly in Natural Language Processing (NLP) problems.

Last March, I was glad to be announced as one of the participants for the 2018 cohort of La Idea incubator program for my entrepreneurship that I recently co-founded in Guatemala.

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