Email incident: Anyone who has received any email regarding this course is entirely opt-out and will no longer receive such emails from us. We sincerely apologize for any inconvenience, and we will ensure this incident will never happen ever again.
Sinerely,
MHR Group LLC
All the materials here at www.self-supervised-learning.com are FREE to use, and are only to help the machine learning community get familiar with self-supervised learning.
Self Supervised Learning [Contrastive Learning + SimCLR] at www.self-supervised-learning.com
-
This course teaches you "Self-Supervised Learning" (SSL), also known as "Representation Learning."
-
SSL is a relatively new and hot subject in machine learning to deal with repositories with limited labeled data.
-
There are two general SSL techniques, contrastive and generative. This course's focus is on supervised and unsupervised contrastive models only.
-
There are several examples and experiments across this course for you to fully grasp the idea behind SSL.
-
Our domain of focus is the image domain, but you can apply what you learn to other domains, including temporal records and natural language processing (NLP).
-
In every lecture, you can access the corresponding Python .ipynb notebooks. The notebooks are best to be run with a GPU accelerator.
-
Simply navigate to the the section and lecture of your interest and open the .ipynb files in Google Colab.
Section 01: Introduction.
- Lecture 01: An Introduction to the Course.
- Lecture 02: Python Notebooks.
Section 02: Supervised Models.
- Lecture 03: Supervised Learning.
- Lecture 04: Transfer Learning & Fine-Tuning.
Section 03: Labeling Task.
- Lecture 05: Labeling Challenges.
Section 04: Self-Supervised Learning.
- Lecture 06: Self-Supervised Learning.
- Lecture 07: Supervised Contrastive Pretext, Experiment 1.
- Lecture 08: Supervised Contrastive Pretext, Experiment 2.
- Lecture 09: SimCLR, An UnSupervised Contrastive Pretext Model.
- Lecture 10: SimCLR Experiment.