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

🧠 Unlock the Secrets of Self-Supervised Learning with Mohammad H. Rafiei

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

🎓 Headline: Dive into Representation Learning, Contrastive Learning, and the World of Deep Learning with TensorFlow!

Introduction to Self-Supervised Learning: Yann André LeCun, a pioneer in AI, once said that if intelligence is a cake, self-supervised learning is the bulk of it. It's the foundation upon which supervised and reinforcement learnings build upon. In this course, we'll explore the profound impact of self-supervised learning on the field of machine learning and how it enables us to leverage large amounts of unlabeled data.

Before We Begin: To ensure you get the most out of this course, here are some essential prerequisites:

• Deep Learning Familiarity: You should be comfortable with deep learning architectures using TensorFlow and Python 3+.
• Model Development Experience: You must know how to develop, train, and test multi-layer deep learning models in TensorFlow.
• Guarantee Policy: This course is backed by a “100% Money Back Guarantee” under Udemy’s rules. If you're not satisfied, your investment is protected!

Your Instructor: My name is Mohammad H. Rafiei, Ph.D., and I am honored to guide you through this journey in self-supervised learning. With my experience as a machine learning engineer, researcher, and instructor at esteemed institutions, I bring a wealth of knowledge to your learning experience.

Subject & Materials: This course will immerse you in the world of Self-Supervised Learning (SSL) - also known as Representation Learning. We'll explore this hot topic in machine learning that addresses the challenge of limited labeled data. Our focus will be on contrastive models, both supervised and unsupervised, with practical applications across various domains, including images, temporal records, and natural language processing (NLP).

• Hands-On Python Notebooks: Each lecture comes with corresponding Python .ipynb notebooks that are best run with GPU accelerators for optimal performance.
• Video Quality & Accessibility: For the best learning experience, watch lectures at 1080p quality and enable captions. These lectures are optimized for use on Google Colab with GPU support.
• TensorFlow Version: We'll be using TensorFlow version '2.8.2'. You can switch to this version by running %tensorflow_version 2.x in the first cell of your Python notebook.

Course Overview: This course is structured into four sections with ten comprehensive lectures:

1. Section 01: Introduction

   • Lecture 01: An Introduction to the Course
   • Lecture 02: Python Notebooks

2. Section 02: Supervised Models

   • Lecture 03: Supervised Learning
   • Lecture 04: Transfer Learning & Fine-Tuning

3. Section 03: Labeling Task

   • Lecture 05: Labeling Challenges

4. Section 04: Self-Supervised Learning

   • Lecture 06: Introduction to 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

Embark on this enlightening journey with me, Mohammad H. Rafiei, and unlock the full potential of your machine learning projects through self-supervised learning! 🚀📚✨