PyTorch Essential Training: Deep Learning

1h 22mIntermediate2024-04-15

Authors

Terezija Semenski

Software Developer, Mathematician, Writer, and Learner

Course details

PyTorch is the most flexible and expressive library for deep learning, and offers simple Python API, GPU support, and flexibility. It’s designed to load data, apply transforms, and build deep learning models with just a few lines of code. Many machine learning developers and researchers use PyTorch to accelerate deep learning research, experimentation, and prototyping. In this course, software developer Terezija Semenski teaches you the important features of PyTorch with a hands-on approach to help you develop the skills you need to dive into your deep learning projects.

Skills covered

PyTorchNeural Networks and Deep LearningPythonEssential TrainingArtificial Intelligence (AI)Open Source

Concepts

0. Introduction

01 - Deep learning with PyTorch
02 - What you should know
03 - Tour of CoderPad

1. PyTorch Overview and Introduction to Google Colaboratory

04 - Introduction to deep learning
05 - Why should you use PyTorch
06 - Google Colaboratory basics

2. Tensors

07 - Introduction to tensors
08 - Creating a tensor CPU example
09 - Creating a tensor GPU example
10 - Moving tensors between CPUs and GPUs

3. Creating Tensors

11 - Different ways to create tensors
12 - Tensor attributes
13 - Tensor data types
14 - Creating tensors from random samples
15 - Creating tensors like other tensors
16 - Solution - Create tensors

4. Manipulate Tensors

17 - Tensor operations
18 - Mathematical functions
19 - Linear algebra operations
20 - Automatic differentiation (Autograd)
21 - Solution - Split tensors to form new tensors

5. Developing a Deep Learning Model

22 - Introduction to the DL training process
23 - Data preparation
24 - Data loading
25 - Data transforms
26 - Data batching
27 - Model development and training
28 - Validation and testing

Conclusion

29 - Next steps