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In this course, you will learn:
- Everything from getting started with PyTorch to creating your own real-world models is covered.
- Recognize how to incorporate Deep Learning into tools and apps.
- Create and publish your own publicly available custom trained PyTorch neural network.
- Master deep learning and position yourself as a top candidate for recruiters looking for Deep Learning Engineers.
- The talents required to become a Deep Learning Engineer and be employed with a salary of $100,000 or more per year.
- Why PyTorch is a terrific method to get started with machine learning.
- Create and use machine learning algorithms in the same way that you would develop a Python program.
- How to gather data, create a machine learning algorithm to detect patterns, and then use that algorithm as an AI to improve your applications.
Syllabus:
- PyTorch Fundamentals
- PyTorch Workflow
- PyTorch Neural Network Classification
- PyTorch Computer Vision
- PyTorch Custom Datasets
- PyTorch Going Modular
- PyTorch Transfer Learning
- PyTorch Experiment Tracking
- PyTorch Paper Replicating
- PyTorch Model Deployment
- Introduction to PyTorch 2.0 and torch.compile
