This is a very informative course offered by IBM. In this course, you will learn how to build deep learning models by using PyTorch. This course contains a lot of content that is simple and easy to understand. At the beginning of the course, you will learn Pytorch's tensors and Automatic differentiation package. As the course move, you will learn fundamentals of deep learning with PyTorch such as Linear Regression, logistic regression, Feedforward deep neural networks, different activation function roles, normalization, dropout layers, convolutional Neural Networks, Transfer learning, etc. In short, this is the best course for those who want to learn PyTorch for deep learning. Now let's see the syllabus of the course-
PyTorch is a deep learning library developed by Facebook to develop machine learning models for NLP, Computer Vision and AI, to name a few. It was developed by Facebook's Artificial Intelligence Research Group and is used to run deep learning frameworks. PyTorch is an excellent framework for entering the actual machine learning and neural network building process. It is ideal for complex neural networks such as RNNNs, CNNs, LSTMs and neural networks that you want to design for a specific purpose. PyTorch is a very different kind of deep learning library (dynamic vs. static) that was adopted by many researchers if not most, and it's flexible approach and easy-to-understand style have won over newcomers and industry veterans alike.
PyTorch is an open source machine learning library. The name PyTorch is derived from its main programming language, Python, and Torch, the library on which it is based. Since PyTorch's release in 2016, it has grown in popularity with developers due to its ease of use, flexibility, easy debugging, fast speed, and community support. Developed by Facebook, PyTorch is similar to Google's TensorFlow in that it runs on tensors, but instead of using static computation graphs like TensorFlow, it utilizes dynamic computation graphs. If you are interested in learning PyTorch, the following list of resources can help you get started.
You'll start by absorbing the most valuable PyTorch basics and techniques. I use easy-to-understand, hands-on methods to simplify and address even the most difficult concepts. My course will help you implement the methods using real data obtained from different sources. Many courses use made-up data that does not empower students to implement Python-based data science in real -life. After taking this course, you'll easily use packages like Numpy, Pandas, and PIL to work with real data in Python along with gaining fluency in PyTorch.
In this section, we will introduce the deep learning framework we'll be using through this course, which is PyTorch. We will show you how to install it, how it works and why it's special, and then we will code some PyTorch tensors and show you some operations on tensors, as well as show you Autograd in code!