By the end of this course, you will
- understand the basic functioning of modern deep learning libraries, including concepts like automatic differentiation, gradient-based optimization
- be able to implement several standard deep learning architectures (MLPs, ConvNets, RNNs, Transformers), truly from scratch
- understand how hardware acceleration (e.g., on GPUs) works under the hood for modern deep learning architectures, and be able to develop your own highly efficient code