If you’ve read any of my posts you probably know that I’m a PyTorch user, and this article won’t be an exception. The only thing which remains for me to uncover is to state which Deep learning library I’m using. Deep Learning Studio has a drag-and-drop interface, simplifying the building process and making it more accessible and efficient. Okay, you now have some basic info about the dataset. Build deep learning modules faster with Deep Learning Studio Our award-winning deep learning platform is the most convenient way to build, train, and deploy your deep learning modules.
To be more precise, we have these 10 classes of images: Each example is a 28x28 grayscale image, associated with a label from 10 classes. If you’ve done any deep learning I’m sure you are familiar with it, but just in case you haven’t, here’s a little background - source: Kaggle.įashion-MNIST is a dataset of Zalando’s article images - consisting of a training set of 60,000 examples and a test set of 10,000 examples. Recently at my university, we dealt with the Fashion-MNIST dataset.
Let me know if you’d like a full rundown on this dataset.Īnyway, this intro got longer then I expected, so let’s end it and get started with what you came here for. Now, this won’t be a deep learning tutorial, as I’ll only share how both laptops performed on training. You’ll have to stay tuned for the reasons and performance comparisons.
#DEEP LEARNING STUDIO FOR MAC FOR MAC#
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. 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. I’ve tr ied 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…). While I still didn’t get used to the keyboard (layout-wise), the whole OS feels significantly better for me than Windows. Nevertheless, I decided to just go for it, since I wanted a MacBook for a long time.
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