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
Introduction
- Welcome
- What you should know before watching this course
Fashion MNIST and Neural Networks
- Working with the Fashion MNIST dataset
- Neural network intuition
Working with Classes and Tensors
- Classes
- Tensors
- Training the network
Working with Loss, Autograd, and Optimizers
- Loss
- Autograd
- Autograd with tensors
- Optimizers
- Using optimizers
Troubleshooting and CPU GPU Usage
- Troubleshooting
- CPU to GPU
- Validation
Conclusion
- Future project ideas