Keep pursuing this and ignore critics. What you're doing is important b/c ML is just out of reach of a big percentage of developers and technical lay people. It will take time to get your approach right, but it will make a difference.
As a suggestion - provide more real-world examples (eg. business, sports, etc) so that users can tinker with your samples as pathway toward learning.
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
Combining it with bash and psql + csvkit + xsv will give you a powerful combination for data ingestion, wrangling and training all on the command line this would seem to have clear benefits for fast development and prototyping.
Keep pursuing this and ignore critics. What you're doing is important b/c ML is just out of reach of a big percentage of developers and technical lay people. It will take time to get your approach right, but it will make a difference.
As a suggestion - provide more real-world examples (eg. business, sports, etc) so that users can tinker with your samples as pathway toward learning.
Please don't give up on this. Great job.