Pytorch for beginners - how machine learning with pytorch really works

Dan We, Anything is possible

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14 Lessons (1h 44m)
    • 1. 1 Understanding pyTorch download dataset

    • 2. 2 Understanding pyTorch loading the dataset

    • 3. 3 Understanding pyTorch visualizing MNIST

    • 4. 4 Understanding pyTorch from numpy array to tensor

    • 5. 5 Understanding pyTorch weights and biases

    • 6. 6 Understanding pyTorch loss and accuracy

    • 7. 7 Understanding pyTorch training our neural network

    • 8. 8 Understanding pyTorch Make our code easier

    • 9. 9 Understanding pyTorch creating a network class

    • 10. 10 Understanding pyTorch Implementing layers

    • 11. 11 Understanding pyTorch the optimizer

    • 12. 12 Understanding pyTorch tensordataset and dataloader

    • 13. 13 Understanding pyTorch training validation

    • 14. 14 Bonus Understanding pyTorch ConvNets in pyTorch


About This Class

Understanding pyTorch

Develop an understanding in pyTorch step by step. We will first develop a simple neural network in python and then implement pyTorch functionalities step by step to make our code easier to understand, shorter and more flexible.

The main goal is to give you an overview of the great modules and classes which pyTorch provides and to help you understand what those modules and classes actually do.

This is a guided "hands on" course which means you should code along with me to get the most out of this course.

Basic knowledge of python and neural networks is recommended (We do not introduce the definition of a tensor here).

If you have prior experience in tensorflow you might also like to go through this course to decide for yourself whether pyTorch is  the right deep learning framework for you

pyTorch is a great deep learning framework for pyTorch and develop a better understanding of how it works should help you to apply it to your own deep learning projects

If you are committed to learn then let's get into it