Hi there, Just a quick heads up — Today is already the last day to enroll in PyImageSearch University and learn about multi-task learning and HydraNets with 15% off. Our latest course is from self-driving car expert Jeremy Cohen from Think Autonomous. If you haven't decided yet, here is everything you'll learn: - How to Build Advanced Multi-Task Models with PyTorch (even if you're a complete beginner)
- 2 Beheading Techniques to add heads to any pretrained Neural Network
- PyTorch 101 — My cookbook to build a DataLoader, Create Models, and Train Parameters from scratch with PyTorch
- How to tune your hyperparameters when heads are learning at different rates
- Multi-Task Learning in Computer Vision — Deep Dive inside an untold Experimentation that reveals which task you should train together ... and avoid mixing!
- The Intermediate Python Concepts you should know to make your code look more professional
- ✅ PROJECT — Build your first multi-task learning algorithm from scratch with PyTorch to process images and do binary classification, regression, and multi-class classification
- The Encoder-Decoder Architectures used to build multi-head decoders that work better and faster than single task networks
- ✅ HYDRANET PROJECT — Build a HydraNet trained with 2 to 3 heads that does real-time semantic segmentation and depth estimation in self-driving cars
The course is in Python and will require you to know about Deep Learning and CNNs. We think you'll love Jeremy's visual and hands-on approach. He really guides you through each step of the learning journey with simple explanations that cover powerful concepts like Multi-Task Learning with PyTorch. HydraNets are part of the future of Deep Learning, and we know this course can help you master HydraNets fast! See what other students are saying about Multi-Task Learning with PyTorch. Your 15% discount expires tonight, so don't miss your big chance. Click here to join PyImageSearch University The PyImageSearch Team Not interested in hearing more about HydraNets? No problem Opt-out of HydraNet updates |
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