Practical Deep Learning for Coders, v3
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!
Course Depth & Coverage
Overall Score : 86 / 100
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machine learning Awards Best Practical Course
Welcome! If you're new to all this deep learning stuff, then don't worry - we'll take you through it all step by step. (And if you're an old hand, then you may want to check out our advanced course: Deep Learning From The Foundations.) We do however assume that you've been coding for at least a year, and also that (if you haven't used Python before) you'll be putting in the extra time to learn whatever Python you need as you go. (For learning Python, we have a list of python learning resources available.)You might be surprised by what you don't need to become a top deep learning practitioner. You need one year of coding experience, a GPU and appropriate software (see below), and that's it. You don't need much data, you don't need university-level math, and you don't need a giant data center. For more on this, see our article: What you need to do deep learning.The easiest way to get started is to just start watching the first video right now! On the sidebar just click ''Lesson'' and then click on lesson 1, and you'll be on your way.
- 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.
- 4.3 Rating
- 24 Reviews
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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