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Self-Supervised Learning with SimCLR & BYOL
Learn self-supervised representation learning using SimCLR and BYOL frameworks to build powerful, unlabeled vision models.
Course Duration: 10 Hours
Preview Self-Supervised Learning with SimCLR & BYOL course
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Self-Supervised Learning with SimCLR & BYOL – Online Course
Self-Supervised Learning with SimCLR & BYOL is an advanced-level online course designed for machine learning practitioners, computer vision researchers, and AI engineers who want to master the cutting-edge field of self-supervised learning (SSL). This course offers hands-on and theoretical insights into powerful representation learning techniques that eliminate the need for massive labeled datasets.
With the explosive growth of data, labeling has become a bottleneck. Self-supervised learning (SSL) addresses this challenge by learning meaningful representations directly from raw, unlabeled data. This course focuses on two of the most impactful SSL methods—SimCLR (Simple Contrastive Learning of Representations) and BYOL (Bootstrap Your Own Latent). These frameworks have set new benchmarks in unsupervised visual learning and revolutionized how models can pretrain on large image datasets.
You’ll explore the theory behind contrastive and non-contrastive learning, learn how to design data augmentation pipelines, implement projection heads, and train ResNet-based encoders using SimCLR and BYOL on datasets like CIFAR-10, STL-10, and ImageNet-subsets. By the end of the course, you’ll be able to pretrain self-supervised models and transfer them effectively to downstream tasks like classification and object detection.
What is Self-Supervised Learning with SimCLR & BYOL?
Self-Supervised Learning (SSL) is a form of unsupervised learning where models are trained on automatically generated labels, often by leveraging structure within the data. In computer vision, SSL typically involves learning from image transformations, enabling the model to learn rich features without annotations.
- SimCLR is a contrastive learning framework that pulls together similar (augmented) views of the same image and pushes apart views of different images.
- BYOL, on the other hand, eliminates the need for negative samples and relies on two neural networks—a student and a momentum teacher—to bootstrap latent representations.
These approaches are foundational in pretraining vision encoders that generalize well across downstream tasks, especially when labeled data is scarce.
How to Use This Course Effectively
To maximize your learning experience:
- Start with foundational modules on SSL, contrastive learning, and data augmentations.
- Move step-by-step through SimCLR and BYOL implementations using PyTorch or TensorFlow.
- Run provided notebooks and experiments to gain hands-on fluency.
- Analyze training curves and embedding visualizations to interpret model behavior.
- Complete the capstone project to demonstrate representation transfer on real-world tasks.
This course is ideal for:
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Deep learning and CV practitioners seeking cutting-edge pretraining strategies
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Researchers and students working in unsupervised and semi-supervised domains
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Data scientists looking to reduce reliance on labeled data
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ML engineers building general-purpose vision models
Course/Topic 1 - Coming Soon
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The videos for this course are being recorded freshly and should be available in a few days. Please contact info@uplatz.com to know the exact date of the release of this course.
Course Objectives Back to Top
By the end of this course, you will be able to:
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Understand the principles and motivation behind self-supervised learning.
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Implement contrastive learning frameworks like SimCLR from scratch.
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Learn how BYOL works without negative samples and its stability dynamics.
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Build advanced augmentation pipelines for contrastive learning.
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Train deep convolutional encoders on image datasets without labels.
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Evaluate SSL models via linear probing and transfer learning.
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Compare SSL to supervised and unsupervised learning paradigms.
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Visualize embeddings and cluster structures using t-SNE and UMAP.
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Optimize pretraining stability using momentum, projection heads, and batch sizes.
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Apply SSL models to real-world problems including classification and retrieval.
Course Syllabus Back to Top
Course Syllabus
Module 1: Introduction to Self-Supervised Learning (SSL)
- The need for SSL in modern ML
- Supervised vs unsupervised vs self-supervised
- Overview of SSL in vision and NLP
Module 2: Data Augmentations & Pretext Tasks
- Augmentation strategies: crop, blur, color jitter
- Pretext task design (e.g., jigsaw, rotation prediction)
- Importance of invariance and equivariance
Module 3: Contrastive Learning Theory
- Positive and negative pairs
- Contrastive loss (NT-Xent, InfoNCE)
- Batch size, temperature scaling, and representation collapse
Module 4: SimCLR Framework in Detail
- Architecture of SimCLR
- Projection head design
- Training a ResNet encoder with NT-Xent loss
- Visualizing learned embeddings
Module 5: BYOL Framework in Detail
- Student-teacher network design
- Exponential Moving Average (EMA)
- Latent prediction loss and target networks
- Removing negative samples in practice
Module 6: Implementation with PyTorch/TensorFlow
- Setting up datasets: CIFAR-10, STL-10, ImageNet-100
- Writing contrastive training loops
- Logging with TensorBoard and Weights & Biases
Module 7: Evaluation Techniques
- Linear probing
- KNN classification
- Transfer learning and fine-tuning on downstream tasks
Module 8: Scaling and Optimization
- Batch normalization and large batch sizes
- Distributed training (DDP)
- Impact of temperature and projection dimensions
Module 9: Comparison of SimCLR, BYOL, MoCo, DINO, VICReg
- When to choose which SSL method
- Pros and cons of contrastive vs non-contrastive
- Real-world adoption in industry
Module 10: Capstone Project – End-to-End SSL Pipeline
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Pretrain encoder with BYOL
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Evaluate on classification and retrieval tasks
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Report metrics and visualize results
Certification Back to Top
Upon successful completion, learners will receive a Certificate of Completion from Uplatz, validating their knowledge and practical ability to apply self-supervised learning techniques using SimCLR and BYOL.
The certificate demonstrates advanced understanding of machine learning concepts, contrastive learning design, and the ability to implement and fine-tune unsupervised learning pipelines. It’s especially valuable for roles in AI R&D, computer vision engineering, and data science.
Adding this certification to your resume or LinkedIn profile shows that you’re proficient in one of the most transformative areas of deep learning—unsupervised representation learning.
Career & Jobs Back to Top
Self-supervised learning is reshaping how machines learn from data. As labeled datasets become expensive and scarce, SSL offers a scalable alternative—and companies are paying attention. From autonomous driving to medical imaging, recommendation systems to facial recognition, SSL is being adopted across sectors.
Roles that benefit from this course include:
- Machine Learning Researcher
- Computer Vision Engineer
- Deep Learning Scientist
- AI/ML Engineer (Vision/NLP)
- Data Scientist working on representation learning
- AI Architect or Technical Lead
Industries like autonomous vehicles, healthcare, fintech, and e-commerce are using SSL to extract more value from raw data. With skills in SimCLR and BYOL, you'll be prepared to work on the forefront of this revolution in data-efficient AI.
Interview Questions Back to Top
1. What is self-supervised learning and how does it differ from supervised learning?
SSL learns representations without human-labeled data using pretext tasks, while supervised learning requires labeled examples for training.
SSL learns representations without human-labeled data using pretext tasks, while supervised learning requires labeled examples for training.
2. What are positive and negative pairs in contrastive learning?
Positive pairs are augmented views of the same image; negative pairs are different images in the batch used to contrast against positives.
Positive pairs are augmented views of the same image; negative pairs are different images in the batch used to contrast against positives.
3. What is the role of the projection head in SimCLR?
It maps representations to a latent space where contrastive loss is applied. This separation improves learning of better features in the base encoder.
It maps representations to a latent space where contrastive loss is applied. This separation improves learning of better features in the base encoder.
4. Why does BYOL not require negative samples?
BYOL uses a moving-average target network to prevent representational collapse, allowing it to learn meaningful features without explicit contrast.
BYOL uses a moving-average target network to prevent representational collapse, allowing it to learn meaningful features without explicit contrast.
5. How do you evaluate a self-supervised model?
Through linear probing, fine-tuning on downstream tasks, or clustering performance using unsupervised techniques.
Through linear probing, fine-tuning on downstream tasks, or clustering performance using unsupervised techniques.
6. What is NT-Xent loss and why is it used?
NT-Xent (Normalized Temperature-scaled Cross Entropy) is a contrastive loss function that pulls positive pairs together and pushes negatives apart, scaled by a temperature parameter.
NT-Xent (Normalized Temperature-scaled Cross Entropy) is a contrastive loss function that pulls positive pairs together and pushes negatives apart, scaled by a temperature parameter.
7. How does momentum improve training in BYOL?
It stabilizes the target network by updating it as an exponential moving average of the student, preventing collapse during learning.
It stabilizes the target network by updating it as an exponential moving average of the student, preventing collapse during learning.
8. Can self-supervised models outperform supervised models?
Yes, especially in low-label or transfer learning settings. SSL-pretrained encoders have outperformed supervised ones on many tasks after fine-tuning.
Yes, especially in low-label or transfer learning settings. SSL-pretrained encoders have outperformed supervised ones on many tasks after fine-tuning.
9. What are the challenges in training SSL models?
Challenges include training instability, representation collapse, high resource requirements, and the need for large batch sizes or memory banks.
Challenges include training instability, representation collapse, high resource requirements, and the need for large batch sizes or memory banks.
10. What real-world applications benefit from SSL?
Applications in medical imaging, autonomous vehicles, recommendation systems, facial recognition, and robotics benefit due to reduced reliance on labeled data.
Applications in medical imaging, autonomous vehicles, recommendation systems, facial recognition, and robotics benefit due to reduced reliance on labeled data.
Course Quiz Back to Top
FAQs
Back to Top
Q1. What are the payment options?
A1. We have multiple payment options:
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A17. Our course is designed to provide you with a solid foundation in the subject and equip you with valuable skills. While the course is a significant step toward your career goals, its important to note that the job market can vary, and some positions might require additional certifications or experience.
Remember that the job landscape is constantly evolving. We encourage you to continue learning and stay updated on industry trends even after completing the course. Many successful professionals combine formal education with ongoing self-improvement to excel in their careers. We are here to support you in your journey!
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A18. While our course is designed to provide you with a comprehensive understanding of the subject, we currently do not offer placement services as part of the course package. Our main focus is on delivering high-quality education and equipping you with essential skills in this field.
However, we understand that finding job opportunities is a crucial aspect of your career journey. We recommend exploring various avenues to enhance your job search:
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While we may not offer placement services, we are here to support you in other ways. If you have any questions about the industry, job search strategies, or interview preparation, please dont hesitate to reach out. Remember that taking an active role in your job search process can lead to valuable experiences and opportunities.
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