Artificial neural networks course
Master artificial neural networks from mathematical foundations to cutting-edge architectures like transformers and GANs. This course takes you through every critical concept — backpropagation, CNNs, RNNs, and generative models — with hands-on implementation at every stage. Whether you are entering deep learning or levelling up your expertise, you will finish with the skills to build, train, and deploy production-ready neural networks.
flexible workload of 4 to 360h
valid certificate in your country
What will I learn?
You will build a complete understanding of neural networks, starting with perceptron theory and linear algebra essentials, then advancing through multilayer networks, convolutional architectures, and recurrent sequence models. You will master the transformer's self-attention mechanism and apply it to both NLP and vision tasks. Training dynamics, regularisation strategies, and advanced optimisers are covered in depth so your models generalise reliably. You will also design generative models, interpret model decisions using gradient-based attribution methods, and compress networks for deployment. Responsible AI principles, including fairness metrics and adversarial robustness, are integrated throughout the curriculum.
Elevify advantages
Develop skills
- Neural Network Architecture Design: Build and train deep networks from perceptrons to transformers confidently.
- Backpropagation and Optimisation: Derive gradients and apply advanced optimisers to accelerate model convergence.
- Generative Model Development: Implement VAEs and GANs to generate realistic images and structured data.
- Model Compression and Deployment: Prune, quantise, and distil networks for efficient real-world production use.
Suggested summary
Before starting, you can change the chapters and the workload. Choose which chapter to start with. Add or remove chapters. Increase or decrease the course workloadWhat our students say
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