Demystifying Deep Learning Frameworks : Function Approximati
Siddhartha Rai | Friday 14:30 | Room D
This talk attempts to demystify some aspects of Deep Learning using Tensorflow, a popular deep learning framework. Under the hood, Tensorflow is essentially a way to approximate functions. We show how Tensorflow can be used as a generic function approximation tool, and some approaches to tune it.
I propose to cover the following topics in the talk -
- How to use Tensorflow to model an arbitrary function
- Some of the model tuning choices
- The effect of various tuning choices on the quality and convergence of the optimization algorithm.
- Most importantly, this talk demonstrates that it is possible to use the same neural network to approximate different types of functions - cubic, quassi-periodic, and exponential . As practitioners and users of deep learning frameworks, this should give us the confidence and encouragement, that an appropriately defined deep learning network is capable of being a universal function approximator - i.e. even in the absence of any prior information between the relationship between the input and output, a deep learning network is capable of learning the relationship using the given training data.