h264, yuv420p, 1280x720 |ENGLISH, 44100 Hz, Stereo | 1h 45mn | 776 MB

Develop an understanding in pyTorch step by step - pyTorch for beginners

What you'll learn

You will understand pyTorch step by step

You will develop this understanding by implementing pyTorch classes and functions

You will learn how pyTorch can make your code much more easy

You will first create a neural network from scratch and then make it easier with pyTorch modules

Requirements

my first answer to this is always - curiousity!

You should now basics of neural networks and what a tensor is

You should have basic knowledge of python

Your personal interest in the topic and willingness to learn something new

Your commitment

You want to take advantage of the data driven opportunity ahead

Description

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

Who this course is for:

beginners in pyTorch

students who want to understand pyTorch better

students who already know tensorflow and want to acquire better knowledge in pyTorch (Note a tensorflow vs. pyTorch comparison is NOT part of this course but you might be able to decide for yourself after finishing it)