Self-Supervised Learning A-Z: Theory & Hands-On Python

Self-Supervised Learning A-Z: Theory & Hands-On Python, available at $59.99, has an average rating of 4.2, with 10 lectures, 8 quizzes, based on 38 reviews, and has 1422 subscribers.

You will learn about Self-Supervised Learning | Representation Learning | Contrastive Learning | SimCLR of Chen et al. (2020) Pretext Model | Downstream Model | Transfer Learning | Fine-Tuning Machine Learning | Deep Learning | Supervised Learning | Unsupervised Learning Python 3+ | TensorFlow | Google Colab This course is ideal for individuals who are Machine Learning Students and Enthusiasts or Those Want to Learn Self-Supervised Learning and Practice It in Python 3+ It is particularly useful for Machine Learning Students and Enthusiasts or Those Want to Learn Self-Supervised Learning and Practice It in Python 3+.

Enroll now: Self-Supervised Learning A-Z: Theory & Hands-On Python

Summary

Title: Self-Supervised Learning A-Z: Theory & Hands-On Python

Price: $59.99

Average Rating: 4.2

Number of Lectures: 10

Number of Quizzes: 8

Number of Published Lectures: 10

Number of Published Quizzes: 8

Number of Curriculum Items: 18

Number of Published Curriculum Objects: 18

Original Price: $199.99

Quality Status: approved

Status: Live

What You Will Learn

Self-Supervised Learning | Representation Learning | Contrastive Learning | SimCLR of Chen et al. (2020)
Pretext Model | Downstream Model | Transfer Learning | Fine-Tuning
Machine Learning | Deep Learning | Supervised Learning | Unsupervised Learning
Python 3+ | TensorFlow | Google Colab

Who Should Attend

Machine Learning Students and Enthusiasts
Those Want to Learn Self-Supervised Learning and Practice It in Python 3+

Target Audiences

Machine Learning Students and Enthusiasts
Those Want to Learn Self-Supervised Learning and Practice It in Python 3+

“If intelligence is a cake, the bulk is self-supervised learning, the icing on the cake is supervised learning, and the cherry on the cake is reinforcement learning.”

Yann André LeCun

Chief AI Scientist at Meta

Some “Musts” Before Starting

You must be familiar with deep learning architectures, including stacks of convolutional, recurrent, dense, pooling, average, and normalization layers using the TensorFlow library in Python 3+.
You must know how to develop, train, and test multi-layer deep learning models using the TensorFlow library in Python 3+.
You must know that this is a“100% Money Back Guarantee” course under Udemy rules.

Course Instructor

My name is Mohammad H. Rafiei, Ph.D. I am honored and humbled to serve as your instructor.
I am a machine learning engineer, researcher, and instructor at Johns Hopkins University, College of Engineering, and Georgia State University, Department of Computer Science. I am also the founder of MHR Group LLC in Georgia.

Subject & Materials

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. Watch the following lecture for more details.
If the videos are too fast or too slow, you can always change their speed. You can also turn on the video caption.
It is best to watch the videos of this course using 1080p quality with the caption on.
The lectures are created to work best on Google Colab with GPU accelerators.
The TensorFlow version used in these lectures is ‘2.8.2.’ You may use %tensorflow_version 2.x at the very first cell of your Python notebook.

Machine learning libraries in Python, including TensorFlow, are evolving. As such, you must keep yourself updated with changes and modify your codes.

Course Overview

Four Sections and ten Lectures:

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.

Course Curriculum

Chapter 1: Introduction

Lecture 1: An Introduction to the Course

Lecture 2: Python Notebooks

Chapter 2: Supervised Models

Lecture 1: Supervised Learning

Lecture 2: Transfer Learning & Fine-Tuning

Chapter 3: Labeling Task

Lecture 1: Labeling Challenges

Chapter 4: Self-Supervised Learning

Lecture 1: Self-Supervised Learning

Lecture 2: Supervised Contrastive Pretext, Experiment 1

Lecture 3: Supervised Contrastive Pretext, Experiment 2

Lecture 4: SimCLR, An UnSupervised Contrastive Pretext Model

Lecture 5: SimCLR Experiment

Instructors

Mohammad H. Rafiei
Instructor and Academic Advisor

Rating Distribution

1 stars: 0 votes
2 stars: 1 votes
3 stars: 6 votes
4 stars: 9 votes
5 stars: 22 votes

Frequently Asked Questions

How long do I have access to the course materials?

You can view and review the lecture materials indefinitely, like an on-demand channel.

Can I take my courses with me wherever I go?

Definitely! If you have an internet connection, courses on Udemy are available on any device at any time. If you don’t have an internet connection, some instructors also let their students download course lectures. That’s up to the instructor though, so make sure you get on their good side!

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