With the advances and success of deep learning technology in solving complex AI problems, the natural step forward lies in building systems that automate the decisions required for setting up a deep learning pipeline. Tackling this automation is not only crucial for speeding up the deployment of deep learning models, it also helps in expanding the capabilities of models to other challenging scenarios like modelling with limited data or modelling under resource constraints. This tutorial seeks to provide a comprehensive overview of the approaches used in this regard by means of neural architecture search. It is also the first tutorial that strongly focuses on transfer and meta-learning, going beyond classic neural architecture search. The tutorial is geared toward graduate students, AI researchers, and practitioners, who are interested in automating parts of their deep learning pipelines, want to learn about principles of automated machine learning and deep learning, and apply those principles to make their own work more effective and less arduous. The prerequisite knowledge assumed of the audience includes basic understanding of deep learning, optimization, and machine learning concepts. Familiarity with some state-of-the-art convolutional neural network architecture can facilitate the understanding but is not required.