regression-js VS Keras

Modeling: We are currently exploring this space so our findings are not final yet but let me share what we've found so far. TensorFlow.js is absolutely amazing, it provides a native port from TensorFlow to JavaScript with CPU, WebGL, WebAssembly and NodeJS backends. We were able to write an LSTM to do time series prediction, so far so good. However, we started hitting issues when we started to do simpler models, like a linear regression. We tried Regression.js but we found it falls short, so we wrote our own script to handle single-variable regressions using TF.js and gradient decent. It actually worked quite well but exposed a flaw in this approach; basically, there is a lot of work to be done to bring many models into the web. We also found out Arquero.js does not play nicely with TF.js since well, Arquero.js does not support tensors; so we went on to explore Danfo.js, which integrates great with TF.js but we found out it's hard to scale it's transformations to +1M rows and found other rough edges. Since then, well, we started exploring Pyodide and perhaps contributing to Danfo.js, or perhaps involve more server-side compute, but that will be a story for another time.

The unchallenged leader in AI development is still Python. and Keras, and robust community support.

>Chollet, a French computer scientist and one of the industry’s sharpest skeptics

I feel like this description really buries the lede on Chollet's expertise. (For those who don't know, he's the creator of and lead contributor[0] to Keras)

[0]https://github.com/keras-team/keras/graphs/contributors

Keras API reference

Star on GitHub ⭐ - Keras

At its heart is TensorFlow Core, which provides low-level APIs for building custom models and performing computations using tensors (multi-dimensional arrays). It has a high-level API, Keras, which simplifies the process of building machine learning models. It also has a large community, where you can share ideas, contribute, and get help if you are stuck.

(https://dev-to-uploads.s3.amazonaws.com/uploads/articles/92cup4lywcjfq83xg0ea.png)

The core model architecture for Magika was implemented using Keras, a popular open source deep learning framework that enables Google researchers to experiment quickly with new models.