Data Science and Machine Learning (Theory and Projects) A to Z - DNN and Deep Learning Basics: PyTorch Installation and

The video tutorial covers the installation and setup of PyTorch, including creating a Conda environment, installing PyTorch and Jupyter, and performing basic tensor operations. It emphasizes the importance of using separate environments for different tasks to avoid package conflicts. The tutorial also demonstrates how to work with tensors in PyTorch, similar to Numpy arrays, and addresses common installation issues on Windows.

What are the different operating systems that Pytorch is available for?

What is the recommended practice for creating environments in Python?

How can you install Pytorch using Conda?

What should you do if you encounter writing permissions issues on Windows?

What is the purpose of installing Jupyter Notebook in the new environment?

What is the significance of using different environments for different tasks in Python?

What is the command to install the torch vision package?

What error might occur when installing Pytorch on Windows, and how can it be fixed?

What is the difference between working with tensors in Pytorch and arrays in Numpy?

How can you convert a Pytorch tensor to a Numpy array?