hal9ai VS Keras

We are not using libfortran not gdpr, we are basically using whatever libraries are available for the web. Since most data scientists don't want to use JS per se, you can build the apps as blocks in the Hal9 site or using a soon-to-be-released Python/R package, see https://notebooks.hal9.com

Feel free to check out our repo as well, all the "primitives" / blocks code is in the scripts folder: https://github.com/hal9ai/hal9ai

https://hal9.com helps data scientists build faster web applications.

It uses WebGL and WebAssembly to process larger datasets, perform inference in the browser with TensorFlow.js, and enables running Python code with Pyodide.

If you want to build a web application on top of your ML project, give https://hal9.com a shot. We designed Hal9 with ease of use for deployment and maximum compatibility with web technologies that enable you to build ML apps with React, Vue, etc. We launched a couple months ago but could use some early feedback and users. Thank you!

BTW. If you are ever interested in helping us out, you can send a PR's to our GitHub repo. For instance, the summarize and convert blocks are here: https://github.com/hal9ai/hal9ai/blob/main/scripts/transforms/summarize.txt.js and https://github.com/hal9ai/hal9ai/blob/main/scripts/transforms/convert.txt.js

We are working in https://hal9.com which is language agnostic and allows you to compose different programming languages; however, we are focused at the moment at 1D-graphs but have plans to support 2D-graphs in the coming weeks.

If you want a demo or just time to chat, I'm available at javier at hal9.ai.

We would love to help out since this is a perfect use case for https://hal9.ai; we are about to release our beta version that makes this as easy as copy-pasting code. You can find me at javier at hal9.ai to find some time to chat and give you a walkthrough of our code-to-api functionality.

Hi there, would love to help you out. We are building a JavaScript + Python/R/AI service called https://hal9.ai to enable you to build React/Vue/Svelte/Angular apps against Python/R models. Want to send me an email to javier at hal9.ai to chat and help out?

Would love to hear your feedback for https://hal9.ai -- We are building an open source platform for data analysis based on reusable code blocks and a community to build and monetize their contributions. We are pretty early in our journey, launched our alpha and getting ready for our beta release, but would love to hear your thoughts. You can find me at javier at hal9.ai. Cheers!

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.

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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.