- Training a network
- Making predictions
- Working with classes and tensors
- Working with loss, autograd, and optimizers
- Troubleshooting a PyTorch network
- CPU/GPU usage
Skill Level Intermediate
- [Jonathan] PyTorch is an increasingly popular deep learning framework and primarily developed by Facebook's AI research group. It has gained popularity because of its pythonic approach, its flexibility and it allows you to run computations immediately. It even allows you to use a Python debugger, making it hit all the right notes with researchers in Python's developer community. PyTorch has even been integrated with some of the biggest cloud platforms including AWSH maker, Google's GCP, and Azure's machine learning service. A while back, Andrej Karpathy, director of AI at Tesla and deep learning specialist tweeted, "I've been using PyTorch a few months now "and I've never felt better. "I have more energy. "My skin is clearer and my eyesight has improved." Joking aside, join me as we learn how to use the PyTorch deep learning framework. I am Jonathan Fernandes and I work in data science, machine learning, and AI for a consultancy.