v.03

Teeny-Tiny Castle 🏰

AI Ethics and Safety are (relatively) new fields, and their tools (and how to handle them) are still not known to most of the development community. To address this problem, we created the Teeny-Tiny Castle, an open-source repository containing "Educational tools for AI Ethics and Safety Research." There, the developer can find many examples of how to use programming tools (functions, classes, libraries, etc.) to work with and deal with various problems raised in the literature (e.g., Algorithmic Discrimination, Model Opacity, etc.). At the moment, our repository has several examples of how to work ethically and safely with AI, using one of the most widely used languages in the community (Python). Our lines of focus are on issues related to "Accountability & Sutentability" "Interpretability" "Robustness/Adversarial" "Fairness" and "Cybersecurity", all being worked through examples that refer to some of the most common contemporary AI applications (e.g., Computer Vision, Natural language Processing, Synthetic Data Generation, Classification & Forecasting, etc.).

You can also find an introductory course on ML organized by the AIRES at PUCRS. To run the notebooks just open them in your Google Drive as a Colab Notebook, or as a Jupyter notebook. You can also follow our Python and VS Code installation tutorial if you want to run these notebooks on your own workstation. All requirements are specified in the requirements.txt file. Python version is 3.9.13.

If you would like to enable your GPU to follow our notebooks, you will need to install the drivers related to your NVIDIA/Cuda/TPU/GPU. If you use NVIDIA, you will also need the NVIDIA Toolkit and cudaDNN. You can find a comprehensive guide on how to configure your NVIDIA card in this tutorial (- by deeplizard -).

Join AIRES at PUCRS.

AI Ethics ⚖️🤖⚖️

• Learn about the state-of-the-art in AI Ethics by browsing our Dash or Power BI dashboard;
• Learn about the major organizations pushing AI R&D and their commitment to AI Safety research in this dasboard;
• Track the current developments in the AI field with the AI Tracker tool, an online table that lists the largest (and most proficient) models ever created, along with an analysis of the potential risks associated with each model being tracked (developed by Gladstone AI);
• Interested in AI regulation? On September 29, 2021, the Chamber of Deputies of the Federative Republic of Brazil approved Bill n. 21/2020, which establishes foundations and principles for the development and application of artificial intelligence (AI) in Brazil (together with Bills 5051/2019 and 872/2021). Here you can find a technical report (Portuguese only...sorry) structured from the main topics addressed in the three bills. This report was made by a collaboration between AIRES at PUCRS and the Post-Graduate Program in Law (PPGD), linked to the PUCRS School of Law.

Introduction Course on ML 📈

• If you want to learn how to build your own workstation, check this Python and VS Code installation guide;
• Here you can find a Basic Python Tutorial to get you started on the language and syntax we are using in the available notebooks in this repository;
• Some of the most utilized libraries in ML and Data Science (if you are a 🐍) are Pandas, Scikit-learn, and Numpy. Here you can find a brief tutorial on how to use some of the (many) features and functions of these libraries;
• For many practitioners, the "Hello World" of Deep Learning is classifying one of the MNIST datasets. Here you can find a Basic Keras and Tensor Flow tutorial using the Digit-MNIST and Fashion-MNIST datasets;
• But if you want to check the Pytorch implementation of the same algorithm we built in the last Keras/TensorFlow tutorial, go to this notebook;
• One of the most famous problems in ML (- actually Neural Networks -) history is the XOR Problem. Here you can find an example of how to implement a Multi-Layer Perceptron using only NumPy to solve the XOR problem;
• Here you can find a notebook showing how to implement a Feed-Forward Neural Network using NumPy. You will learn the inner workings of many of the pre-built functions available in libraries like TensorFlow and Pytorch;
• One of the most basic ML models you can build is a Linear Regression (LR) model. Here you will find how to build an LR model from scratch, implementing the Gradient Descent algorithm using only NumPy;
• Here you can find a visual representation of how the Gradient Descent algorithm works while trying to find the correct model that approximates a ∿ mystery function ∿;
• Among the different techniques that we find within Machine Learning, Reinforcement Learning deserves some introduction. As much as this methodology is not yet mainstream, RL was the paradigm responsible for creating some of the most general and proficient agents of today. If you want to understand some of the basics behind RL, we have provided two notebooks where we trained an agent to deal with the [n-armed-bandit problem](- Among the different techniques that we find within Machine Learning, Reinforcement Learning deserves some introduction. As much as this methodology is not yet mainstream, RL was the paradigm responsible for creating some of the most general and proficient agents of today. If you want to understand some of the basics behind RL, we have provided two notebooks where we trained an agent to deal with the n-armed-bandit problem (a classic RL introduction problem), and OpenAI Gym's FrozenLake environment (a simple environment to learn the basics of $Q$-learning);
• One of the most common applications of AI systems in industry is in creating recommendation systems. Learn how to build three different types of recommendation systems with this notebook;
• Nowadays, (almost) everything is a transformer model, and one of the areas that have taken the most advantage of this is NLP (Natural Language Processing). Create your own encoder-transformer model to tackle a problem of text classification (more specifically, toxicity detection).
• Learn how to work with sequence-to-sequence tasks using RNNs and the [original (encoder-decoder) version of the transformer architecture](https://github.com/Nklug...

v.02

Teeny-Tiny Castle 🏰

AI Ethics and Safety are (relatively) new fields, and their tools (and how to handle them) are still not known to most of the development community. To address this problem, we created the Teeny-Tiny Castle, an open-source repository containing "Educational tools for AI Ethics and Safety Research." There, the developer can find many examples of how to use programming tools (functions, classes, libraries, etc.) to work with and deal with various problems raised in the literature (e.g., Algorithmic Discrimination, Model Opacity, etc.). At the moment, our repository has several examples of how to work ethically and safely with AI, using one of the most widely used languages in the community (Python). Our lines of focus are on issues related to "Accountability & Sutentability" "Interpretability" "Robustness/Adversarial" "Fairness" and "Cybersecurity", all being worked through examples that refer to some of the most common contemporary AI applications (e.g., Computer Vision, Natural language Processing, Synthetic Data Generation, Classification & Forecasting, etc.).

You can also find an introductory course on ML organized by the AIRES at PUCRS. To run the notebooks just open them in your Google Drive as a Colab Notebook, or as a Jupyter notebook. You can also follow our Python and VS Code installation tutorial if you want to run these notebooks on your own workstation. All requirements are specified in the requirements.txt file. Python version is 3.9.13.

If you would like to enable your GPU to follow our notebooks, you will need to install the drivers related to your NVIDIA/Cuda/TPU/GPU. If you use NVIDIA, you will also need the NVIDIA Toolkit and cudaDNN. You can find a comprehensive guide on how to configure your NVIDIA card in this tutorial (- by deeplizard -).

Join AIRES at PUCRS.

AI Ethics ⚖️🤖⚖️

• Learn about the state-of-the-art in AI Ethics by browsing our Dash or Power BI dashboard;
• Learn about the major organizations pushing AI R&D and their commitment to AI Safety research in this dasboard;
• Track the current developments in the AI field with the AI Tracker tool, an online table that lists the largest (and most proficient) models ever created, along with an analysis of the potential risks associated with each model being tracked (developed by Gladstone AI);
• Interested in AI regulation? On September 29, 2021, the Chamber of Deputies of the Federative Republic of Brazil approved Bill n. 21/2020, which establishes foundations and principles for the development and application of artificial intelligence (AI) in Brazil (together with Bills 5051/2019 and 872/2021). Here you can find a technical report (Portuguese only...sorry) structured from the main topics addressed in the three bills. This report was made by a collaboration between AIRES at PUCRS and the Post-Graduate Program in Law (PPGD), linked to the PUCRS School of Law.

Introduction Course on ML 📈

• If you want to learn how to build your own workstation, check this Python and VS Code installation guide;
• Here you can find a Basic Python Tutorial to get you started on the language and syntax we are using in the available notebooks in this repository;
• Some of the most utilized libraries in ML and Data Science (if you are a 🐍) are Pandas, Scikit-learn, and Numpy. Here you can find a brief tutorial on how to use some of the (many) features and functions of these libraries;
• For many practitioners, the "Hello World" of Deep Learning is classifying one of the MNIST datasets. Here you can find a Basic Keras and Tensor Flow tutorial using the Digit-MNIST and Fashion-MNIST datasets;
• But if you want to check the Pytorch implementation of the same algorithm we built in the last Keras/TensorFlow tutorial, go to this notebook;
• One of the most famous problems in ML (- actually Neural Networks -) history is the XOR Problem. Here you can find an example of how to implement a Multi-Layer Perceptron using only NumPy to solve the XOR problem;
• Here you can find a notebook showing how to implement a Feed-Forward Neural Network using NumPy. You will learn the inner workings of many of the pre-built functions available in libraries like TensorFlow and Pytorch;
• One of the most basic ML models you can build is a Linear Regression (LR) model. Here you will find how to build an LR model from scratch, implementing the Gradient Descent algorithm using only NumPy;
• Here you can find a visual representation of how the Gradient Descent algorithm works while trying to find the correct model that approximates a ∿ mystery function ∿;
• Among the different techniques that we find within Machine Learning, Reinforcement Learning deserves some introduction. As much as this methodology is not yet mainstream, RL was the paradigm responsible for creating some of the most general and proficient agents of today. If you want to understand some of the basics behind RL, we have provided two notebooks where we trained an agent to deal with the [n-armed-bandit problem](- Among the different techniques that we find within Machine Learning, Reinforcement Learning deserves some introduction. As much as this methodology is not yet mainstream, RL was the paradigm responsible for creating some of the most general and proficient agents of today. If you want to understand some of the basics behind RL, we have provided two notebooks where we trained an agent to deal with the n-armed-bandit problem (a classic RL introduction problem), and OpenAI Gym's FrozenLake environment (a simple environment to learn the basics of $Q$-learning);
• One of the most common applications of AI systems in industry is in creating recommendation systems. Learn how to build three different types of recommendation systems with this notebook;
• Nowadays, (almost) everything is a transformer model, and one of the areas that have taken the most advantage of this is NLP (Natural Language Processing). Create your own encoder-transformer model to tackle a problem of text classification (more specifically, toxicity detection).
• Learn how to work with sequence-to-sequence tasks using RNNs and the [original (encoder-decoder) version of the transformer architecture](https://github.com/Nkluge-correa/teeny-tiny_castle/blob/bbe9c0a77499fa68de7c6d53bf5ef7e0b43a25e0/ML%20Intro%20Course/seu...

v.01

Teeny-Tiny Castle 🏰

AI Ethics and Safety are (relatively) new fields, and their tools (and how to handle them) are still not known to most of the development community. To address this problem, we created the Teeny-Tiny Castle, an open-source repository containing "Educational tools for AI Ethics and Safety Research." There, the developer can find many examples of how to use programming tools (functions, classes, libraries, etc.) to work with and deal with various problems raised in the literature (e.g., Algorithmic Discrimination, Model Opacity, etc.). At the moment, our repository has several examples of how to work ethically and safely with AI, using one of the most widely used languages in the community (Python). Our lines of focus are on issues related to "Accountability & Sutentability" "Interpretability" "Robustness/Adversarial" "Fairness" and "Cybersecurity", all being worked through examples that refer to some of the most common contemporary AI applications (e.g., Computer Vision, Natural language Processing, Synthetic Data Generation, Classification & Forecasting, etc.).

You can also find an introductory course on ML organized by the AIRES at PUCRS. To run the notebooks just open them in your Google Drive as a Colab Notebook, or as a Jupyter notebook. You can also follow our Python and VS Code installation tutorial if you want to run these notebooks on your own workstation. All requirements are specified in the requirements.txt file. Python version is 3.9.13.

If you would like to enable your GPU to follow our notebooks, you will need to install the drivers related to your NVIDIA/Cuda/TPU/GPU. If you use NVIDIA, you will also need the NVIDIA Toolkit and cudaDNN. You can find a comprehensive guide on how to configure your NVIDIA card in this tutorial (- by deeplizard -).

Join AIRES at PUCRS.

AI Ethics ⚖️🤖⚖️

• Learn about the state-of-the-art in AI Ethics by browsing our Dash or Power BI dashboard;
• Learn about the major organizations pushing AI R&D and their commitment to AI Safety research in this dasboard;
• Track the current developments in the AI field with the AI Tracker tool, an online table that lists the largest (and most proficient) models ever created, along with an analysis of the potential risks associated with each model being tracked (developed by Gladstone AI);
• Interested in AI regulation? On September 29, 2021, the Chamber of Deputies of the Federative Republic of Brazil approved Bill n. 21/2020, which establishes foundations and principles for the development and application of artificial intelligence (AI) in Brazil (together with Bills 5051/2019 and 872/2021). Here you can find a technical report (Portuguese only...sorry) structured from the main topics addressed in the three bills. This report was made by a collaboration between AIRES at PUCRS and the Post-Graduate Program in Law (PPGD), linked to the PUCRS School of Law.

Introduction Course on ML 📈

• If you want to learn how to build your own workstation, check this Python and VS Code installation guide;
• Here you can find a Basic Python Tutorial to get you started on the language and syntax we are using in the available notebooks in this repository;
• Some of the most utilized libraries in ML and Data Science (if you are a 🐍) are Pandas, Scikit-learn, and Numpy. Here you can find a brief tutorial on how to use some of the (many) features and functions of these libraries;
• For many practitioners, the "Hello World" of Deep Learning is classifying one of the MNIST datasets. Here you can find a Basic Keras and Tensor Flow tutorial using the Digit-MNIST and Fashion-MNIST datasets;
• But if you want to check the Pytorch implementation of the same algorithm we built in the last Keras/TensorFlow tutorial, go to this notebook;
• One of the most famous problems in ML (- actually Neural Networks -) history is the XOR Problem. Here you can find an example of how to implement a Multi-Layer Perceptron using only NumPy to solve the XOR problem;
• Here you can find a notebook showing how to implement a Feed-Forward Neural Network using NumPy. You will learn the inner workings of many of the pre-built functions available in libraries like TensorFlow and Pytorch;
• One of the most basic ML models you can build is a Linear Regression (LR) model. Here you will find how to build an LR model from scratch, implementing the Gradient Descent algorithm using only NumPy;
• Here you can find a visual representation of how the Gradient Descent algorithm works while trying to find the correct model that approximates a ∿ mystery function ∿;
• Among the different techniques that we find within Machine Learning, Reinforcement Learning deserves some introduction. As much as this methodology is not yet mainstream, RL was the paradigm responsible for creating some of the most general and proficient agents of today. If you want to understand some of the basics behind RL, we have provided two notebooks where we trained an agent to deal with the [n-armed-bandit problem](- Among the different techniques that we find within Machine Learning, Reinforcement Learning deserves some introduction. As much as this methodology is not yet mainstream, RL was the paradigm responsible for creating some of the most general and proficient agents of today. If you want to understand some of the basics behind RL, we have provided two notebooks where we trained an agent to deal with the n-armed-bandit problem (a classic RL introduction problem), and OpenAI Gym's FrozenLake environment (a simple environment to learn the basics of $Q$-learning);
• One of the most common applications of AI systems in industry is in creating recommendation systems. Learn how to build three different types of recommendation systems with this notebook;
• Nowadays, (almost) everything is a transformer model, and one of the areas that have taken the most advantage of this is NLP (Natural Language Processing). Create your own encoder-transformer model to tackle a problem of text classification (more specifically, toxicity detection).
• Learn how to work with sequence-to-sequence tasks using RNNs and the [original (encoder-decoder) version of the transformer architecture](https://github.com/Nkluge-correa/teeny-tiny_castle/blob/bbe9c0a77499fa68de7c6d53bf5ef7e0b43a25e0/ML%20Intro%20Course/seu...