PyTorch is an open-source deep-learning framework that resides inside the torch module. It is available in python and C++ interfaces.
It is mainly used for natural language processing. It was developed by the Facebook AI research group along with Uber’s ‘Pyro’ software. It was released in the year 2016.
Many companies like Amazon, Nvidia, etc use PyTorch.
Now, let’s get started with our roadmap to learn PyTorch.
Prerequisites: Before we start learning PyTorch, we must have basic knowledge of Python, NumPy, and Anaconda framework. Knowledge of AI is a plus.
Step1:
Begin with its installation on your PC. Learn mathematical building blocks of neural networks.
Step2:
Understand the concept of neural networks. Learn Universal workflow of machine learning.
Step3:
Learn the process of neural networks to functional blocks. Learn terminologies related to it.
Step4:
Move on to some more interesting and important concepts. Learn loading data, linear regression, and datasets.
Step5:
We are almost done with it. Learn about convolutional neural networks and recurrent neural networks.
Step6:
Get familiar with convents. Learn about training a convent from scratch, features extracting in convents, visualization of convents, and sequence processing with convents.
Step7:
Learn word embedding and recursive neural networks.
Step8:
Whoa! We are done with PyTorch. Explore more with PyTorch to get better at it.
