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Guilherme G. Cloud, Data Science and Machine Learning

My name is Guilherme G. and I have over 2 years of experience in the tech industry. I specialize in the following technologies: Python, SQL, Machine Learning, Deep Learning, Cloud Computing, etc.. I hold a degree in Other. Some of the notable projects I’ve worked on include: Time Series Forecasting of Goog Stock, Teeth Instance Segmentation, Bodybuilding Poses Detection. I am based in Guaxupe, Brazil. I've successfully completed 3 projects while developing at Softaims.

My passion is building solutions that are not only technically sound but also deliver an exceptional user experience (UX). I constantly advocate for user-centered design principles, ensuring that the final product is intuitive, accessible, and solves real user problems effectively. I bridge the gap between technical possibilities and the overall product vision.

Working within the Softaims team, I contribute by bringing a perspective that integrates business goals with technical constraints, resulting in solutions that are both practical and innovative. I have a strong track record of rapidly prototyping and iterating based on feedback to drive optimal solution fit.

I’m committed to contributing to a positive and collaborative team environment, sharing knowledge, and helping colleagues grow their skills, all while pushing the boundaries of what's possible in solution development.

Main technologies

  • Cloud, Data Science and Machine Learning

    2 years

  • Python

    1 Year

  • SQL

    1 Year

  • Machine Learning

    1 Year

Additional skills

Direct hire

Potentially possible

Previous Company

PagSeguro

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Experience Highlights

Time Series Forecasting of Goog Stock

Prediction of GOOG Stock Market with LSTM and Web Application with Flask. LSTM isn't a good algorithm to predict a lot of days ahead of a stock market, but the intention of this project was doing an article on medium where I could explain how to simple build web applications.

Teeth Instance Segmentation

The project was based on an X-ray teeth images dataset, where I have the input images (X-ray) and the segmentation masks (outputs). Each tooth is represented with a different color in grayscale. All the project was made with TensorFlow Keras. The U-Net model is mainly used for image segmentation tasks because of it's good performance on small and large datasets. If you have a very small dataset, you can also fine-tune your model by retraining a model that was originally trained on a larger dataset for a similar task. This is beneficial because you can freeze some layers during the retraining process, allowing you to avoid overfitting and reduce computational costs without compromising accuracy. For the training, I applied augmentation by rotating and slightly zooming the images to enhance accuracy on the test dataset predictions. I used a batch size of 4, ran 10 epochs, set the learning rate to 0.001, and specified a height/width of the output image as 512 pixels with 33 filters (considering that a person has 32 teeth, and accounting for the 0 pixel values, the total is 33 filters).

Bodybuilding Poses Detection

This is just a fun project that I made to predict whether bodybuilders are doing the correct poses in the category (just double biceps until now). This project reached more than 3000 likes on Linkedin. It can extend to a world where this technology is on all gym mirrors and "normal" people could pose in front of them, and the result shows at the same time. OpenCV and MediaPipe give us the power to explore the world of computer vision, but we also can do it with segmentation, for instance. It's that thing: "GIVE ME 10,000 LABELED IMAGES, AND WE TALK MORE LATER."

Education

  • Institute of Education, Science and Technology of Southern Minas Gerais

    Other in Computer science

    2018-01-01-2022-01-01

Languages

  • English
  • Portuguese

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