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The past few years have seen a dramatic increase in the performance of deep learning architectures applied to fields ranging from computer vision and speech recognition to bio-informatics and drug design. This presentation will consist in three parts. Part 1 is an gentle introduction to the basic ideas that are crucial for training deep neural networks (like logistic regression, SGD and optimization methods). Part 2 focuses on the most common building blocks (convolutions, attention layers and skip connections) of practical neural architectures such as recurrent neural networks, generative models and the more recent graph convolutional networks. Finally, part 3 insists on the importance of carefully designed loss functions across a range a different training methods (may it be for supervised, semi-supervised or unsupervised learning).