English | MP4 | AVC 1280×720 | AAC 48KHz 2ch | 1h 21m | 159 MB
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.
This course includes Code Challenges powered by CoderPad. Code Challenges are interactive coding exercises with real-time feedback, so you can get hands-on coding practice alongside the course content to advance your programming skills.
Table of Contents
Introduction
1 Deep learning with PyTorch
2 What you should know
3 Tour of CoderPad
PyTorch Overview and Introduction to Google Colaboratory
4 Introduction to deep learning
5 Why should you use PyTorch
6 Google Colaboratory basics
Tensors
7 Introduction to tensors
8 Creating a tensor CPU example
9 Creating a tensor GPU example
10 Moving tensors between CPUs and GPUs
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
Manipulate Tensors
17 Tensor operations
18 Mathematical functions
19 Linear algebra operations
20 Automatic differentiation (Autograd)
21 Solution Split tensors to form new tensors
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
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