Hi thanks a lot. I received positive interactions on github from the community, however, your comment is the first encouraging feedback I ve got here :D so, I appreciate it.
I will take your suggestion into consideration. You are right, there should be more real-world examples that will help users get started and see how this can be useful.
The thing is, I started the project two weeks ago, so it still relatively new. I ve been coding day n night because the idea got me excited. I published the first stable release this week. However, there are new features that will be implemented in the next releases.
If the project is only 2 weeks old, all the more reason to ignore any critics. Particularly here where people are likely to criticize a baby in the crib for not working on coding projects outside of naptime.
What you're doing is creating a declarative syntax for applying machine learning tasks directly to data. This makes it learnable by machines, effectively teaching them how to do their own machine learning experiments. I think this project is greater than the sum of its parts.
Someone posted this tool earlier in the comments too. I was surprised since I never heard of it and find it great!
However, I think it is only for building deep learning models and does not have any general ML support or am I missing something? If yes then that fact makes it very different from igel as a tool
I will take your suggestion into consideration. You are right, there should be more real-world examples that will help users get started and see how this can be useful.
The thing is, I started the project two weeks ago, so it still relatively new. I ve been coding day n night because the idea got me excited. I published the first stable release this week. However, there are new features that will be implemented in the next releases.