Deep Learning with PyTorch Is Not Torturing

Many readers want to learn deep learning in PyTorch, but the concept is complex and the programming code (either Python or R) is daunting. This post gives you a comprehensive overview of Deep Learning and its applications in Image Recognition. If you just want to get an overall idea of how deep learning works, this post will give you a good overview. However, this post is written to show you how to use PyTorch for deep learning. By following the code, you will build your deep-learning model for image classification.

I know the best way to digest a large whale is to chew it piece by piece. So I prepare the following (A) through (M) learning steps. By following this post for about one or two hours (remember to have your coffee breaks!), you will be able to build your first deep-learning model in PyTorch. It isn’t a bad time investment, isn’t it? Also, because we model image data, you will see how images are stored and how deep learning classifies imagines. This will be a good starter for you to dig into expert areas such as computer vision (CV) or Convolutional Neural Networks (CNN) in the future.

I thought it is helpful to mention the three broad data categories. The three data categories are (1) multivariate data (In contrast with serial data), (2) Serial data (including text and voice stream data), and (3) Image data. Deep learning has three…