You can mostly convert Python code to Julia line by line (in fact ChatGPT can do it, albeit not very well since there aren't that many Julia examples in the tra

You can mostly convert Python code to Julia line by line (in fact ChatGPT can do it, albeit not very well since there aren't that many Julia examples in the training data). Sometimes you have to look up a function that doesn't have the same name. Writing new Julia code is often even easier than Python since you can write loops in numeric code and have good performance (also you have opt-in rigorous type checking which helps define interfaces and catch bugs, an excellent package manager, as well as sane threading/multiprocessing, contrary to Python). However, I must caution against trying to replace Torch within Julia. The Julia ecosystem does not have these huge libraries for neutral networks yet. Building such a thing requires a huge investment (by a company like Google or Facebook) and Julia is not there yet. You can do neural networks with GPU support in Julia (even with extremely fancy autodiff capabilities) but it's not "production ready" in that you will have to deal with the quickly moving ecosystem and probably even end up contributing to it, if you stick with it long enough to build something interesting. On the other hand, if you ever wanted to add a "neutral network term" to a PDE to simultaneously solve and train the network, Julia is the place to go. It's crazy what kinds of modeling you could potentially do with stuff like that.