About this course
Neural networks, computer vision and transfer learning, through to deploying a trained model.
This is an 8-week specialist track - our most in-depth program, taking you to a professional, hireable standard. It runs online in the August 2026 cohort (starting 1 August 2026) and is taught the EchoLens way: you learn by doing real, gradeable work rather than just watching lectures.
What's included
- Live, instructor-led online sessions across 8 weeks (32 hours total).
- Hands-on coding quests you solve inside the EchoLens browser compiler - nothing to install.
- Gems, stages and a leaderboard that keep you moving instead of grade anxiety.
- A verified certificate with a scannable QR code, ready to share on LinkedIn, when you finish.
- The first week is open free so you can try the course before you pay.
Course learning outcomes
By the end of this course, you will be able to:
- CLO 1. Build neural networks in PyTorch using tensors and autograd from first principles.
- CLO 2. Train convolutional networks and apply transfer learning for computer vision tasks.
- CLO 3. Deploy a trained deep learning model as a working applied product.
Course outline - level by level
5 leveles, each with hands-on quests you clear in the portal.
- Level 1. Tensors and Autograd - PyTorch fundamentals
- Level 2. First Neural Networks - MLPs, activation, loss functions, training craft
- Level 3. Convolutional Vision - CNNs and image pipelines
- Level 4. Transfer Learning - Pretrained models and fine-tuning
- Level 5. Capstone: Applied DL Product - Complete deep learning project
How you submit: Coding quests solved in the built-in EchoLens compiler.
Who it's for
Deep Learning with PyTorch suits learners at a intermediate to advanced level who want a practical, project-based route into Deep Learning with PyTorch. You need only a browser and an internet connection - all coding runs inside the EchoLens compiler, so there is nothing to set up.
Certificate
Finish every stage and EchoLens issues a verified certificate carrying a QR code anyone can scan to confirm it on our site. You can add it to your CV or share it to LinkedIn in one click.