If you are interested in learning AI, PyTorch, and how to program with Github Copilot, but you hate Python, don’t worry! There are still plenty of ways you can achieve your goals.
First and foremost, it’s important to understand that Python is a widely-used programming language in the field of artificial intelligence and machine learning. However, it is not the only language you can use to work with AI. In fact, PyTorch, one of the most popular deep learning libraries, can be used with other languages such as C++ and Java.
To start learning AI without using Python, you can begin by familiarizing yourself with the basics of AI and machine learning concepts. This includes understanding algorithms, neural networks, data preprocessing, and model evaluation. While most tutorials and resources will be in Python, you can still follow along with the concepts and implement them in a language of your choice.
Next, you can start exploring PyTorch and how it can be used with languages other than Python. PyTorch provides bindings for C++ and Java, which allows you to build deep learning models and work with tensors in these languages. You can find documentation and tutorials on how to use PyTorch with these languages on the official PyTorch website.
When it comes to programming with Github Copilot, you can still benefit from its code generation capabilities even if you don’t like Python. Github Copilot provides suggestions and completions based on the context of your code, regardless of the programming language you are using. You can use Github Copilot to generate code snippets, refactor existing code, or explore new programming concepts.
In order to use Github Copilot effectively, it’s important to have a good understanding of programming fundamentals and best practices. This includes writing clean and readable code, using proper naming conventions, and following coding standards. Github Copilot can help you with these tasks by providing suggestions and guiding you through the coding process.
In conclusion, while Python is a popular language for AI and machine learning, you can still learn and work with these concepts using other languages. By familiarizing yourself with AI concepts, exploring PyTorch with different languages, and leveraging the power of Github Copilot, you can still pursue your interests in AI and programming, even if you hate Python.
I comment the he'll out if everything…I outline in comments then fill in the code ..I track changes with versions… most important, why I did something a certain way. Because when I come back to it 5, 10, 15 years later ( my main cad app is 20 years old ) I have a way better chance of quickly getting back up to speed with that code, solve bugs, etc, etc, and mist importantly not intrduce new problems. Because if it was written down anywhere else, it will never get looked at. The clean code idea that comments should be minimized is absolutely negligent.
No step on snake, man…. 😉
15:30 TBH, This is like saying that because someone put a statement outside an if block and caused the bug, you don't like bracket scopes.
Just keep going, when I was learning Python I just got used to it after 6 months or so, and now I can see indentation scopes the same way I see bracket scopes.
If it is too difficult, consider adding a blank line after for/if blocks.
Do you all think that one can be a good computer programmer / SWE without a stem degree? I feel like I don't have a hard science background without, at the bear minimum, having doing the calc sequence 🙁
I also thought that there is no excuse for an AI to make syntax errors. But then I realized there are two approaches to this problem. One is to use a compiler. But a lot of the code examples and snippets need some kind of an environment to complile, unless they're doing something really simple and self-contained. Another one is to write a parser that only does a basic grammar check. I am not sure which type of mistakes the Copilot makes and the ideal solution would maybe something that's a custom average of a compiler and a grammar checker. But since the whole attraction of AI is in allowing you to brute force things that used to require thinking, the more you need to customize your solution, the less advantage there is to using an AI.
This video is just great, they should show it in classrooms. You let us with this doubt, though: what are the easiest languages to debug?
Silverlight baby!
21:50
Words to live by, “The project might’ve been dogshit, but hey, at least I learned a few things along the way.” 😂
Thanks for the shout out! Love your work by the way!
I found Introduction to statistical Learning funny. It's also from Stanford. Could not complete it but I really found the course to be hilarious and the book, jupyter notebooks really wholesome.
Thanks for the links regarding Literate Programming, I think it would make an interesting video talking point with how you use it or would use it.
I have a physics degree as well, and it helps knowing partial derivatives and linear algebra which is the basis of machine learning, but I would still recommend diving into the math at least a little bit to get a better intuition of how neural networks work. Even if it's just programming a simple neural net from scratch that can classify different logic gates ( this is a good hello world machine learning example).
Really good C and C++ will always be in demand.. All other languages like python (ugh!!) , jave, etc.. are all written in c and use c libraries.I have been programming c/c++ for 25 years and working on wall street making trading apps. We still use c/c++. At the wall st company i work at we don't ask a single tech question when looking for programmers – we ask the applicants questions to see whether or not they can think and solve problems creatively.
They should use AI to rewrite every python library in another language
This guy is a professional complainer 😂
@2:14 !!!
BLASPHEMY!!! 😮
😅
I need to ask, did you read every book in your room?
Your takes are great man. I'm 25 years in. Sage. From interviews to copilot and all in between.
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Dear Americans, it is endearing that you feel the need to keep apologizing over and over about how you don't know how to pronounce non-English names. At least from my side, it is most often not necessary and perhaps a bit too much. Thank you.
Apart from that, thank you for sharing your experiences with all of us! Entertaining, well-spoken, insightful and honest.