awesome-teachable-machine VS netron

Not sure if I've seen anything of the sort, seems rather specific. Maybe try a Teachable Machine project? https://teachablemachine.withgoogle.com/

Train a computer to recognize your images, sounds, and poses. Use this resource to gain a better understanding.

[Teachable Machine](https://teachablemachine.withgoogle.com/)

We will create an machine learning model that can classify Arabic and English books. To collect, train, and test data, we will use Teachable Machine from Google.

a lot of places! but for a high schooler, better to focus at what you want to do first. or if you still haven't gotten any idea, try a simple explanation on what ml is without the math on youtube and tinker around a no code machine learning platform like https://teachablemachine.withgoogle.com/

The principle is roughly the same as it is possible with teachablemachine.withgoogle.com.

You mentioned app, so check this out: https://teachablemachine.withgoogle.com

Here is a similar tool and source

Netron seems to be the best that I've seen so far. https://github.com/lutzroeder/netron

In exploring open-source projects, I've come across several promising tools capable of managing deep-learning models for images. Significantly, tools such as NETRON provide visualization of neural networks, while SHAP can be used for evaluating the significance of outputs.

We need to find out the exact input and output tensor names. A tool like Netron makes this super easy. Open the original .tflite and/or the ONNX model in Netron and click the Model Properties button in the lower left corner.

How is this different from e.g Netron https://github.com/lutzroeder/netron

Netron (https://netron.app/) is the best and mostly used NN visualizer. Just save your model and then simply load it via netron to look its layers and weights. If you want a more complex visualization then you can also play with Zetane ( but its paid, also have a free version) engine.