Requirements:
Basics in linear algebra:
- Have routine in doing matrix-vector operations and their properties
- Matrix decompositions and properties (Eigenvalue d., Singular value d.)
- see: Linear Algebra 1+2, Numerics 1
Basics in calculus:
- Multivariate calculus, integration and differentiation, partial
derivatives
- Basics of optimization: Properties of minimum, maximum, saddle point
- see: Calculus 1+2
Basics in statistics:
- Random variables, PDF, CDF, moments and their properties.
- Transformations between random variables, Jacobians.
- see: Stochastics 1 or Statistical Physics 1
Basics in functional transforms:
- Fourier transform / DFT / FFT.
Programming:
Python. Numpy. Scipy. Jupyter notebooks. Git. Github
Check the worksheets of this course to see if you are ready:
https://github.com/cwehmeyer/scipro
Qualification objectives: The students have a basic understanding of algebraic and computational methods for deep neural networks, their application scope and can practically build and train them with state-of-the-art software tools. They are familiar with typical deep learning structures and understand the relationship to their shallow counterparts.
Content:
- Perceptron
- Multilayer neural network and universal represenation theorem
- Backpropagation
- Deep feedforward networks
- Convolutional Neural Networks
- Autoencoder versus principal component analysis
- Time-autoencoder versus time-lagged independent component analysis
- Generative networks: Variational Autoencoders and Adversarial Generative Networks
- Active learning