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Do you have any question? sergio [at] globallabs.org

Hi! I’m Sergio Mendoza

Problem solver / Machine Learning | Deep Learning Engineer / Data Science
Go to my resume! Download PDF
Resume

About Me

Two activities have given meaning to my life, learning and help people in their projects.

Learning something new every day has become the engine of all the changes in my life, to help people to grow, to discover, to learn and open a universe of possibilities.

Skills & abilities

Data Analysis
Machine Learning
Deep Learning
Hardware/Software integration

My Specialities

Problem solving

Design and implementation of complex systems.

Data Science

Data Analysis and BI.

Machine/Deep Lerning Engineering

ML Systems Implementation.

Last experience & jobs

2017 - Present DOD - SMART RETAIL Deep Learning Engineer Brazil/Mexico
2016 - Present ASIAHUB - CARING TRADING AND LOGISTICS LIMITED Chief Scientific Officer Shanghai/Brazil/Mexico
2009 - Present GLOBAL LABS MEXICO Founder/Senior Technology and Science Project Management Mexico
2004 - 2009 SECRETARÍA DE MARINA ARMADA DE MÉXICO-INAOE Communications Senior Main Developer and Project Manager Mexico

Language skills

Spanish
English
French

Hobbies & Interests

  • Spanish Guitar
  • Table Tennis
  • Travel
Portfolio
Scalable Data Processing in R
Scalable Data Processing in R R
Happy house DL
Keras Tutorial - The Happy House in R Keras
Twitter Shiny App
Twitter Shiny App R (Deprecated API)
CNN People Detection Darknet Framework
YOLO detection on a table tennis match Darknet Framework
YOLOv3 Gunfire detection demo PyTorch Framework
Recommendations

17 years of experience - Many happy new friends!

Look at their opinions!
It is rare to come across highly competitive but always humble talent like Sergio. We met thanks to the MIT Bootcamp, getting help from each other every now and then. Sergio has proven to be always available to offer good, logical, structured advice. In constant updating of his abilities, Sergio is a scientist by nature. He won't fail to deliver results under pressure.
Octavio Jiménez Blanco MIT Innovator Under 35 | Arvolution | Simplify
Sergio is a highly motivated entrepreneur that always is looking for a new Idea. I meet Sergio on the Global Entrepreneurship Bootcamp, we worked on the same team and was a great experience. He amazed me with his logical thinking, expertise, passion and positive can-do attitude. After that, I had the pleasure to participate with Sergio in a couple of entrepreneurial initiatives that involved solving complex problems for what we found creative solutions as a team. Sergio has a scientific mindset that gives him the capacity to learn fast and apply his knowledge for unexplored processes, because of that and many other reasons I have no doubt to recommend him.
José Carlos Reyes Romero MIT GEB | Site Manager, PPG | Pricing Professor
Sergio is a well prepared business person. His entrepreneurial spirit together with IT expertise, make him an avid manager. He is reliable, honest and intelligent.
Elizabeth Perez Atristain International Sales Director at Dairy Products, Inc.
AI Literature Links [Multiple sources]
Tensorflow on Google Cloud
1 Alex Vaysburd Tensorflow
December 14, 2017

Real-time forecasts in the cloud: from market feed capture to ML predictions

By Alex Vaysburd, Software Engineer

If you’re in the financial services industry or have an interest in predicting market movements with machine learning, you may be eager to learn how to move your trading signal and forecast generation code into the cloud. You can easily scale up your computational loads, distribute data processing pipelines to run in parallel on multiple machines, speed up the time required to run complex analytics, eliminate the need for management of data storage, and ultimately eliminate the need for multiple data centers. In this post, we’ll show how to build a data processing pipeline that starts with a market data feed as the input and uses machine learning to generate real-time forecasts as the output, with all application components running natively in Google Cloud.

Read more
Tensorflow in images classification
8 Adam Monsen Tensorflow
December 8, 2017

Learn how to classify images with TensorFlow

by Adam Monsen

Recent advancements in deep learning algorithms and hardware performance have enabled researchers and companies to make giant strides in areas such as image recognition, speech recognition, recommendation engines, and machine translation. Six years ago, the first superhuman performance in visual pattern recognition was achieved. Two years ago, the Google Brain team unleashed TensorFlow, deftly slinging applied deep learning to the masses. TensorFlow is outpacing many complex tools used for deep learning.

Read more
Tensorflow over Kubernetes
1 Niklas Heidloff Tensorflow
December 11, 2017

Deploying TensorFlow Models to Kubernetes on IBM Cloud

by Niklas Heidloff

TensorFlow supports various image recognition models that can be retrained for certain objects. MobileNet is small and can be run in OpenWhisk. The more precise and bigger Inception model however is too big for the 512 MB RAM memory limit of OpenWhisk. Instead it can be deployed to Kubernetes, for example on the IBM Cloud which provides a free plan that can be used to run this sample.

Read more
Time Series Forecasting with Tensorflow
1 Justin Brandenburg Tensorflow
June 10, 2017

Applying Deep Learning to Time Series Forecasting with TensorFlow

by Justin Brandenburg

Time series analysis has significance in econometrics and financial analytics but can be utilized in any field, where understanding trends is important to decision making and reacting to changes in behavioral patterns. In finance, time series analytics is used for financial forecasting for stock prices, assets, and commodities. Econometricians have long leveraged “autoregressive integrated moving average” (ARIMA) models to perform univariate forecasts.

Read more

Don’t hesitate to contact me!

  • Puebla, Puebla, Mexico
  • sergio [at] globallabs.org
  • Skype: smendoza.barrera
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