Data Science at Home
Episodes
Tuesday Apr 30, 2019
Episode 58: There is physics in deep learning!
Tuesday Apr 30, 2019
Tuesday Apr 30, 2019
There is a connection between gradient descent based optimizers and the dynamics of damped harmonic oscillators. What does that mean? We now have a better theory for optimization algorithms.In this episode I explain how all this works.
All the formulas I mention in the episode can be found in the post The physics of optimization algorithms
Enjoy the show.
Tuesday Apr 23, 2019
Episode 57: Neural networks with infinite layers
Tuesday Apr 23, 2019
Tuesday Apr 23, 2019
How are differential equations related to neural networks? What are the benefits of re-thinking neural network as a differential equation engine? In this episode we explain all this and we provide some material that is worth learning. Enjoy the show!
Residual Block
References
[1] K. He, et al., “Deep Residual Learning for Image Recognition”, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 770-778, 2016
[2] S. Hochreiter, et al., “Long short-term memory”, Neural Computation 9(8), pages 1735-1780, 1997.
[3] Q. Liao, et al.,”Bridging the gaps between residual learning, recurrent neural networks and visual cortex”, arXiv preprint, arXiv:1604.03640, 2016.
[4] Y. Lu, et al., “Beyond Finite Layer Neural Networks: Bridging Deep Architectures and Numerical Differential Equation”, Proceedings of the 35th International Conference on Machine Learning (ICML), Stockholm, Sweden, 2018.
[5] T. Q. Chen, et al., ” Neural Ordinary Differential Equations”, Advances in Neural Information Processing Systems 31, pages 6571-6583}, 2018
Tuesday Apr 16, 2019
Episode 56: The graph network
Tuesday Apr 16, 2019
Tuesday Apr 16, 2019
Since the beginning of AI in the 1950s and until the 1980s, symbolic AI approaches have dominated the field. These approaches, also known as expert systems, used mathematical symbols to represent objects and the relationship between them, in order to depict the extensive knowledge bases built by humans. The opposite of the symbolic AI paradigm is named connectionism, which is behind the machine learning approaches of today
Tuesday Apr 09, 2019
Episode 55: Beyond deep learning
Tuesday Apr 09, 2019
Tuesday Apr 09, 2019
The successes that deep learning systems have achieved in the last decade in all kinds of domains are unquestionable. Self-driving cars, skin cancer diagnostics, movie and song recommendations, language translation, automatic video surveillance, digital assistants represent just a few examples of the ongoing revolution that affects or is going to disrupt soon our everyday life.But all that glitters is not gold…Read the full post on the Amethix Technologies blog