awesome-datascience VS Keras

Awesome Data Science by Academic

- https://github.com/academic/awesome-datascience

9. Awesome Data Science If you’re on the hunt for data science resources, Awesome Data Science is a goldmine. This curated list includes MOOCs, books, courses, blogs, podcasts, software, and more, all related to data science.

Awesome Data Science – The awesome lists repositories often provides a good collection of resources around a specific topic, and the awesome-datascience repository is no exception. It contains a very comprehensive list of books, moocs, tutorials, and other content for all learnes of all levels of experience.

There are several on github, such as: https://github.com/academic/awesome-datascience

Awesome Data Science

If you want to expand your skills and knowledge in data science, there's a ton of free online resources out there. For example, this page is a good place to get started. There's lots of communities like /r/learndatascience or similar subs if you get stuck on something.

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.