Data Science
This post aims to explore the art of storytelling with data and why it is crucial to communicate data effectively.
Data Science Python Tutorial Streamlit
Tools like Streamlit, FastAPI & Hugging Face have emerged as a quick way to expose functional results for Machine Learning Applications, and don’t require complex implementations.
Data Science Python Tutorial
How can you make your code shine with isort, Black, and Flake8?
Web app to predict knee osteoarthritis grade using Deep Learning and Streamlit.
Data Science Git Python Tutorial
How to set up a data science project?. Where do I start?. How do I structure my project?. Just a few questions we frequently have when starting a project.
How to manage virtual environments, packages, and dependencies in Python and start your data science project with Conda or Poetry.
We present a series of 6 steps to integrate some useful tools like Kanban and Git and get more productive projects.
A brief introduction to some tools for a cleaner Python code by applying isort, Black, Flake8, and Pylint automatically using GitLab CI Pipelines.
Augmented Reality - AR
What is the basic process of an Augmented Reality system, and what are its main components of Hardware and Software?
The development of Augmented Reality systems can be divided into two trends, Trend horizontal or AR applications, and trend vertical or AR techniques.
Augmented Reality - AR Series
In this series, a brief introduction to the basic notions that involve the concept of Augmented Reality will be presented. Some basic knowledge that we all should know to start!
What is Augmented Reality? What are its features basic? What are some of Augmented Reality problems and challenges?
User Experience - UX
A brief introduction to User Experience — UX Work Process. LEAN UX is based on Design Thinking, Agile Methodology, and Lean Startup.
A brief introduction to User Experience — UX Work Process. One of the main UX methodologies, Design Sprint!
A brief introduction to User Experience — UX. A list of the main UX tools and methods. Strategy, Ideas, Planning, Validation, Design, Metrics, and Launch!
Deep Learning Generative Adversarial Nets MNIST Python Tutorial
Brief theoretical introduction to Wasserstein GAN or WGANs and practical implementation using Python and Keras/TensorFlow in Jupyter Notebook.
Brief theoretical introduction to Least Squares Generative Adversarial Nets or LSGANs and practical implementation using Python and Keras/TensorFlow in Jupyter Notebook.
Deep Learning Generative Adversarial Nets CIFAR10 Python Tutorial
Brief theoretical introduction to Deep Convolutional Generative Adversarial Networks or DCGANs and practical implementation using Python and Keras/TensorFlow in Jupyter Notebook.
Brief theoretical introduction to Context-Conditional Generative Adversarial Nets or CCGANs and practical implementation using Python and Keras/TensorFlow in Jupyter Notebook.
Brief theoretical introduction to Conditional Generative Adversarial Nets or CGANs and practical implementation using Python and Keras/TensorFlow in Jupyter Notebook.
Deep Learning Generative Adversarial Nets Series
Keras implementations of Generative Adversarial Networks. GANs, DCGAN, CGAN, CCGAN, WGAN, and LSGAN models with MNIST and CIFAR-10 datasets.
A brief theoretical introduction to Generative Adversarial Networks or GANs and practical implementation using Python and Keras/TensorFlow in Jupyter Notebook.
Deep Learning Generative Adversarial Nets
This post presents the basic notions that involve the concept of Generative Adversarial Networks — GANs.
Machine Learning Deep Learning Tutorial Python
Convolutional neural networks extract the most useful information for the task at hand.
Artificial neural network + data volume + computational power = Deep artificial networks.
Machine Learning Tutorial Python
Artificial neural networks are called networks because they are represented by the composition of several different functions.
Logistic regression is a regression model where the dependent variable is categorical, and the output can take only two values 0 or 1.
Linear regression defines the relationship between two variables, but how to find the “best” combination of parameters?
Machine Learning Deep Learning Series
In this series, an introduction to the basic notions that involve the concept of Machine Learning and Deep Learning will be presented.
Machine Learning Deep Learning
What is machine learning, description of the development environment, and workflow implementation with Keras.
Unicamp
Engenheira desenvolve método de localização, baixando custo de mobilidade autônoma.