PyTorch Tutorial : Introduction to PyTorch

Want to learn more? Take the full course at https://learn.datacamp.com/courses/introduction-to-deep-learning-with-pytorch at your own pace. More than a video, you'll learn hands-on coding & quickly apply skills to your daily work. --- Hi, I am Ismail Elezi and I will introduce you to Deep Learning with PyTorch. In this course, we are going to learn the basic concepts of deep learning, a subfamily of machine learning algorithms which has been at the forefront of recent developments in Artificial Intelligence. You might have heard of successes of image classification, machine translation, autonomous driving, Alpha Go or computer bots defeating professional players in Starcraft (AlphaStar). All of these technologies have been empowered by neural networks, another name for deep learning. The magic of neural networks compared to traditional models is in the fact that classical models used one algorithm for feature extraction followed by a machine learning classifier, while neural networks do the optimization altogether. The first few layers transform the input into features which are easy to be classified, while the final layer separates the data based on the features which the previous layers have generated. During the course, we are going to implement many of deep learning central algorithms using the PyTorch library. While the concepts we are going to study are general, the examples we are going to use are mostly computer vision-oriented, as are the datasets. There are tons of great deep learning libraries right there. We chose PyTorch because of its simplicity because it has strong GPU support and it has already implemented many deep learning algorithms. It having strong OOP support makes a natural choice for many companies like Facebook and Salesforce, while also being one of the most used deep learning libraries in academic research. Calculating derivatives and gradients is a very important aspect of deep learning algorithms. Luckily PyTorch is very good at