catala VS Keras

Programming and law can go together tho https://github.com/CatalaLang/catala

Law is a mess, in part because its authors take shortcuts. For example, from the first example on CatalaLang's README.md:

> If the property was acquired by gift [and various conditions apply], then for the purpose of determining loss the basis shall be such fair market value. [emphasis added]

I think (and I'm not a lawyer or a tax expert) that this means that the basis of an asset can have a different value for the purpose of determining gain or determining loss. Wow, basis isn't just a number, although one might not notice this if one didn't read the six emphasized words.

But the Catala code seems to completely ignore this. Oops. I filed an issue:

https://github.com/CatalaLang/catala/issues/514

In a real use case, I imagine that substantial refactoring of the parts that consume basis might be needed when one notices that the basis is not a number.

Catals is a language trying to exactly this.

There's even a programming language for that: https://catala-lang.org/

Keras

As a beginner, I was looking for something simple and flexible for developing deep learning models and that is when I found Keras. Many AI/ML professionals appreciate Keras for its simplicity and efficiency in prototyping and developing deep learning models, making it a preferred choice, especially for beginners and for projects requiring rapid development.

After setting the variables for the environment, the next step is to install dependencies. To use Gemma, KerasNLP is the dependency used. KerasNLP is a collection of natural language processing (NLP) models implemented in Keras and runnable on JAX, PyTorch, and TensorFlow.

All breaking changes are listed here: https://github.com/keras-team/keras/issues/18467

You can use this migration guide to identify and fix each of these issues (and further, making your code run on JAX or PyTorch): https://keras.io/guides/migrating_to_keras_3/

I'm guessing the "real" keras code is coming from the keras repository. Is that a correct assumption? How does that version of Keras get there? If I wanted to write my own activation layer next to ELU, where exactly would I do that?

Other popular machine learning tools include PyTorch, Keras, and Scikit-learn. PyTorch is an open-source machine learning library developed by Facebook that is known for its ease of use and flexibility. Keras is a high-level neural networks API that is written in Python and is known for its simplicity. Scikit-learn is a machine learning library for Python that is used for data analysis and data mining tasks.

Click to Learn more...

I'm not aware of anything off-the-shelf, but if you have sufficient programming experience, one way to do this would be to build a large dataset of reference images and pictures and use something like keras to train a convolutional neural network on them.