By the end of this training, you will have learnt:
Data science:
- Why and when we need data augmentation;
- What a Generative Adversarial Network (GAN) is;
- How to choose the correct architecture/framework based on your task and data;
- How to perform data imputation (generate missing data);

Developer
- The tools to implement data augmentation: Docker, PyTorch, GPUs, Git, etc;
- How to set up your project repository, version your code, manage your experiments;
- How to use pre-trained models or build your own from scratch;
- How to use visualization tools to make life much easier for you;