PyTorch Essential Training: Deep Learning (2019)
56mIntermediate2019-10-03
Authors
Jonathan Fernandes
Consultant focusing on data science, AI, and big data
Course details
PyTorch is quickly becoming one of the most popular deep learning frameworks around, as well as a must-have skill in your artificial intelligence tool kit. It's gained admiration from industry leaders due to its deep integration with Python; its integration with top cloud platforms, including Amazon SageMaker and Google Cloud Platform; and its computational graphs that can be defined on the fly. In this course, join Jonathan Fernandes as he dives into the basics of deep learning using PyTorch. Starting with a working image recognition model, he shows how the different components fit and work in tandem—from tensors, loss functions, and autograd all the way to troubleshooting a PyTorch network.
Learning objectives
Training a network
Making predictions
Working with classes and tensors
Working with loss, autograd, and optimizers
Troubleshooting a PyTorch network
CPU/GPU usage
Learning objectives
Training a network
Making predictions
Working with classes and tensors
Working with loss, autograd, and optimizers
Troubleshooting a PyTorch network
CPU/GPU usage
Skills covered
PyTorchNeural Networks and Deep LearningPythonEssential TrainingArtificial Intelligence (AI)Open Source
Concepts
0. Introduction
- 01 - Welcome
- 02 - What you should know before watching this course
1. Fashion MNIST and Neural Networks
- 03 - Working with the Fashion MNIST dataset
- 04 - Neural network intuition
2. Working with Classes and Tensors
- 05 - Classes
- 06 - Tensors
- 07 - Training the network
3. Working with Loss, Autograd, and Optimizers
- 08 - Loss
- 09 - Autograd
- 10 - Autograd with tensors
- 11 - Optimizers
- 12 - Using optimizers
4. Troubleshooting and CPU GPU Usage
- 13 - Troubleshooting
- 14 - CPU to GPU
- 15 - Validation
Conclusion
- 16 - Future project ideas