内容简介：Nevertheless, I decided to just go for it, since I wanted a MacBook for a long time. While I still didn’t get used to the keyboard (layout-wise), the whole OS feels significantly better for me than Windows.I’ve tried multiple times with various distros ofT
Nevertheless, I decided to just go for it, since I wanted a MacBook for a long time. While I still didn’t get used to the keyboard (layout-wise), the whole OS feels significantly better for me than Windows.
I’ve tried multiple times with various distros of Linux , but all of them felt like they were still in pre-alpha, even though that wasn’t the case (overheating issues, sleep issues, wifi issues…).
This article is aimed at data scientists that are facing the same MacBook dilemma I was facing till yesterday — to buy or not to buy . If you don’t have time to read through the entire article, the short answer is YES — go for Mac if you have the money and want something new.
You’ll have to stay tuned for the reasons and performance comparisons.
The article is structured as follows:
- Hardware comparison
- Dataset and libraries used
- Deep learning — performance comparison
- Conclusion
Now, this won’t be a deep learning tutorial, as I’ll only share how both laptops performed on training. Let me know if you’d like a full rundown on this dataset.
Anyway, this intro got longer then I expected, so let’s end it and get started with what you came here for.
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