“I've had the pleasure in working with Daniel. He has deep knowledge about software engineering, infrastructure and security, always mentoring and helping everyone around him. Daniel was one the first engineers around me that adopted and really understood when to use Docker and Kubernetes. His approach of how to better manage the infrastructure makes him a valuable engineer for every company.”
Sobre
AI Engineer with 25 years in tech and hands-on experience inside OpenAI, contributing to…
Experiência e formação acadêmica
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Mechron
Ver experiência completa de Daniel
Veja o cargo, tempo na empresa e muito mais dessa pessoa.
ou
Licenças e certificados
Publicações
Cursos
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Ansible in a nutshell - Howto
2014
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Background Job in Ruby on Rails - Howto
2013
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Improving OO with Ruby - Caelum - (RR-17)
2012
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Java Developer - Fap
2009
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Java and Object Orientation - Caelum - (FJ-11)
2010
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Java for Web Development - Caelum - (FJ-21)
2010
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Laboratory tests with Java, XML and Design Patterns - Caelum - (FJ-16)
2010
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Organizing JavaScript - Howto
2015
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Rails Caching - Howto
2013
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Rails Engines - Howto
2013
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Ruby on Rails from Beginning to Advanced - Egenial
2011
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Security in Ruby on Rails - Howto
2013
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Web Development with Rails - Howto
2012
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Web programmer - Senac
2007
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Workshop BootStrappers
2012
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Workshop Startup Dev
2012
Projetos
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JusGPT
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A Python-based RAG application tailored for the legal domain, leveraging Langchain and Pinecone to navigate Brazilian legal texts with OpenAI's GPT-4 API.
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Reinforcement Learning
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AI is playing Street Fighter.
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EDA wine reviews
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This project conducted exploratory data analysis using a Wine Review dataset found on Kaggle containing approximately 130k reviews from the Wine Enthusiast to understand better:
Where does the best wine in the world come from?
Is price related to quality?
Are certain varieties of wine of higher quality?
What is the best wine in each price bracket?
What words are most used when talking about wine? -
Time series forecasting in TensorFlow
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This notebook's goal is to familiarize you with working with time series data and TensorFlow.
⚠️ This is not financial advice!
What is a time series problem?
Time series problems deal with data over time.
Such as the number of staff members in a company over ten years, sales of computers for the past five years, and electricity usage for the past 50 years. -
Convolutional Neural Networks and Computer Vision with TensorFlow
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In deep learning, many model architectures can be used for different problems. For example, you could use a convolutional neural network to make predictions on image and/or text data. However, in practice, some architectures typically work better than others.
Workflow
Getting a dataset to work with
Architecture of a convolutional neural network
A quick end-to-end example
Steps in modeling for binary image classification with CNNs
Becoming one with the data
Preparing…In deep learning, many model architectures can be used for different problems. For example, you could use a convolutional neural network to make predictions on image and/or text data. However, in practice, some architectures typically work better than others.
Workflow
Getting a dataset to work with
Architecture of a convolutional neural network
A quick end-to-end example
Steps in modeling for binary image classification with CNNs
Becoming one with the data
Preparing data for modeling
Creating a CNN model
Fitting a model
Evaluating a model
Improving a model
Predicting with a trained model
Contains a little experiment with data augmentation
Idiomas
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English
Nível avançado
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Portuguese
Nível nativo ou bilíngue
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