Text-based and video-based introductory Machine Learning course taught by an experienced instructor and Kaggle's #1 competitor. Using PyTorch and fastai library, this tutorial is focused on practical results rather than theory.
Created by: Jeremy Howard
Produced in 2019
What you will learn
- Train an image classifier to accurately differentiate cats and dogs
- Clasify positive and negative movie reviews with revolutionary NLP ULMFiT algorithm
- Build your own deep learning libraries.
- Use Camvid dataset for image segmentation
- Create image classification model and gradient descent loop
- Important ML techniques of skip connection, U-net architecture, feature loss and gram loss
- How to implement callbacks and event handlers.
- Create fastai Data Block API from scratch
- Implement advanced training techniques such as Mixed precision training, Label smoothing, xresnet
- Build deep learning library in Swift
- Much, Much more!
- Experienced instructor that provides easy to understand explanations and teaches you "how-to" instead of "why".
- Top-down learning approach perfect for students that want to apply Machine Learning fast.
- Great community of fellow-learners to help you along the course.
- This course uses fastai library that can be too difficult for beginners.
- To understand the theory befind the course, further readings and additional information are necessary.
Jeremy Howard is an entrepreneur, business strategist, developer, and educator. Jeremy is a founding researcher at fast.ai, a research institute dedicated to making deep learning more accessible. He is also a faculty member at the University of San Francisco, and is Chief Scientist at doc.ai and platform.ai. Jeremy has invested in, mentored, and advised many startups, and contributed to many open source projects.
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