In this step-by-step tutorial, you will learn how to implement SimCLR using the Lightly library, which is focused on contrastive learning for self-supervised image representation. In this video, we cover: ✅ What SimCLR is and why it's important ✅ Setting up a SimCLR pipeline using Lightly ✅ Using NTXentLoss, data augmentations, and projection heads ✅ Training on custom image datasets with PyTorch Lightning This guide is perfect for ML practitioners, CV enthusiasts, and students looking to get hands-on with self-supervised learning in Python. 00:00 - What is SimCLR? 02:00 - Importing Packages and Experiment Config 03:25 - Download Dataset 03:41 - SimCLR Architecture and NTXentLoss 05:30 - Data Augmentation 06:40 - Creating SimCLR 10:30 - Training SimCLR 12:10 - Visualizing Learned Representations 🛠️ Tools Used: Lightly, PyTorch Lightning, torchvision 💡 Level: Intermediate 📦 Use Cases: Unlabeled image learning, representation learning 👍 Like the video 💬 Leave your questions in the comments 🔔 Subscribe for more tutorials on deep learning and computer vision