Free course · Advanced Technology
Deep Learning Starter
A free conceptual tour of deep learning: neural networks, convolutional networks for images, recurrent networks for sequences, transfer learning and how training is tuned.
Free · self-study · no certificate
About 6 hours
2 modules · 5 lessons
What you will be able to do
- Explain how layers, weights and activations work together
- Describe when CNNs, RNNs and transfer learning are used
- Explain what loss functions, optimisers and learning rates control
Who it's for
- Learners who know Python and basic machine learning
- Developers exploring image or text models
Before you start
Python and introductory machine learning concepts.
Module 1 · about 120 minutes
Neural Network Architectures
Artificial neural networks, CNNs for images and RNNs for sequences.
- Describe the main architectures
- Match architectures to data types
Module 2 · about 80 minutes
Training in Practice
Transfer learning and the settings that control training.
- Reuse a pre-trained model
- Explain loss, optimisers and learning rate
Further learning
- PyTorch — Learn the Basics — The official step-by-step introduction: tensors, datasets, building models, autograd, optimisation and saving models.
- TensorFlow Tutorials — Notebook tutorials on vision, text, audio and more that run in Google Colab; useful for comparing frameworks.
Continue with a full program
- Deep Learning (DL) — Work with neural networks for images, language, and complex pattern recognition.
- Machine Learning (ML) — Build models that learn from data for prediction, classification, and decision-making.
- Artificial Intelligence (AI) — Understand how AI systems work and how they are applied in real-world products.
Want the full Deep Learning (DL) program?
This free course is an introduction. The full program is taught live by an instructor, with practical assignments, assessments, projects and a certificate.
See the Deep Learning (DL) program