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Eduardo Lima Barros

Machine Learning Developer

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About Me

Hi, I'm a professional in IT with more than 9 years of experience, and at least 3 with software development and more than 1 year with ML. I began to work with IT at the age of 13 with my brother making computer repairs troubleshooting and software configurations. I was always self-taught, and make some early sites with HTML and CSS.

At 14 years old, I enrolled in the federal institute for studying Computer Science at technical level where I learned about algorithms, programming languages and databases. After 5 years I began my graduation in law to better understand business: legal issues, opening, compliance, risks, and so on.

After that I worked some time using this knowledge as a consultant to help business to thrive without risks. This experience, allied with my own experience opening my business, gave me a lot of tools to better understand the difficulties a tech company can face while trying to grow or maintain a health business. That's why going to developing ML models for AI purposes was just a natural step in my career that became very early. That way, I can use this knowledge to help business implement ML models to make better decisions.

Latest Projects

EasyFeeling: Sentiment Analysis Made Easy
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EasyFeeling: Sentiment Analysis Made Easy

I fine-tuned Llama-2 model is capable of rapidly and accurately detecting positive or negative sentiment in new text data. By employing techniques like QLoRA and quantization, we've optimized training efficiency without compromising performance. Using a dataset accessible in the Stanford University, this model demonstrates adaptability to diverse sentiment analysis applications. I fine-tuned Llama-2-7b-chat-hf using QLoRA, Adam, Peft, bitsandbytes for quantization, and Hugging Face, achieving training completion in approximately 40 minutes on the free Google Colab platform.

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Luigi.AI

Luigi.AI - The perfect AI assistant for your own data

Luigi.AI — Innovative app that combines advanced technologies like Langchain, OpenAI, and Streamlit to provide intelligent insights from your documents. Simply upload files (PDF, docx, txt), ask questions, and receive accurate answers.

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Sentiment Analisys

Text classification with Social Networks

Classification and prediction of discussions on Reddit, using web scraping and API and supervised learning, applying algorithms such as KNN, Random Forest and Logistic Regression from sklearn, TF-IDF-Vectorizer and TruncatedSVD for Natural Language Processing, and sampling the results with Matplotlib and Seaborn.

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Mario Bross

Using Python and reinforcement learning to make the AI learn to play Mario and learn from each mistake. Using OpenAi GYM, Pytorch, NesPy and the PPO model.

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Diabetes Prediction using Sage Maker

Using the model XGBoost to predict the probability of a pacient develop diabetes. Deploying on AWS Sage Maker and generenating end point for using in others aplications

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Work Experience

Software Engineer - LGPD2U/FortSecure (2023 - present)

Medium-sized company specializing in Information Security and Privacy, with more than 600 active clients in 10 countries. Promoted in less than 6 months to mid-level.

  • Developing automation tools using languages such as Python and JavaScript and technologies such as Docker and Kubernetes, optimizing data analysis processes for security by at least 40% and ML by 07%.
  • Developing APIs and Webhooks for integration with various systems and databases CMBDs, facilitating the input of data for analysis and the automation of actions through workflow and scripts using Python and JS.
  • Training Machine Learning (AI) models for the process of discovering sensitive data in structured and unstructured data systems receiving data from various sources. Using sample data from these sets to refine the model, reducing activities such as searching and generating reports on users and customers by at least 70%.
  • Developing an automated solution with JS and Sales Force and Workflows to identify agents responsible for storing customer data on personal devices, which resulted in a reduction in complaints and an increase in customer compliance.
  • Developing a tool to automate the deletion of customer data, relieving the manual workload of the marketing team.

Systems specialist - Peck Advogados (2021 - 2022)

Large law firm in São Paulo, specializing in law and technology, working for major national and international clients.

  • Analyzing systems, applications, and APIs to determine and identifying security and compliance flaws in application codes, for clients such as Nike, Bimbo and SBT TV.

IT manager - C&T (2015 - 2016)

Medium size accountant firm

  • Managing IT infrastructure and network servers — Linux and Windows.
  • Monitoring threats and applying security measures.

Other Projects

Neural Network From Scratch Friendly

Neural Network from scratch - A Deep Learning introduction

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GPT from Scratch Friendly

Gentle introduction to Generative Pre-trained Transformer

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More on GitHub