Archive for the 'optimisation' Category

In this episode I am with Jadiel de Armas, senior software engineer at Disney and author of Videflow, a Python framework that facilitates the quick development of complex video analysis applications and other series-processing based applications in a multiprocessing environment. 

I have inspected the videoflow repo on Github and some of the capabilities of this framework and I must say that it’s really interesting. Jadiel is going to tell us a lot more than what you can read from Github 

 

References

Videflow Github official repository
https://github.com/videoflow/videoflow

 

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In this episode, I am with Dr. Charles Martin from Calculation Consulting a machine learning and data science consulting company based in San Francisco. We speak about the nuts and bolts of deep neural networks and some impressive findings about the way they work. 

The questions that Charles answers in the show are essentially two:

  1. Why is regularisation in deep learning seemingly quite different than regularisation in other areas on ML?

  2. How can we dominate DNN in a theoretically principled way?

 

References 

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Training neural networks faster usually involves the usage of powerful GPUs. In this episode I explain an interesting method from a group of researchers from Google Brain, who can train neural networks faster by squeezing the hardware to their needs and making the training pipeline more dense.

Enjoy the show!

 

References

Faster Neural Network Training with Data Echoing
https://arxiv.org/abs/1907.05550

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In this episode, I am with Dr. Charles Martin from Calculation Consulting a machine learning and data science consulting company based in San Francisco. We speak about the nuts and bolts of deep neural networks and some impressive findings about the way they work. 

The questions that Charles answers in the show are essentially two:

  1. Why is regularisation in deep learning seemingly quite different than regularisation in other areas on ML?

  2. How can we dominate DNN in a theoretically principled way?

 

References 

 

 

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Today I am with David Kopec, author of Classic Computer Science Problems in Python, published by Manning Publications.

His book deepens your knowledge of problem solving techniques from the realm of computer science by challenging you with interesting and realistic scenarios, exercises, and of course algorithms.
There are examples in the major topics any data scientist should be familiar with, for example search, clustering, graphs, and much more.

Get the book from https://www.manning.com/books/classic-computer-science-problems-in-python and use coupon code poddatascienceathome19 to get 40% discount.

 

References

Twitter https://twitter.com/davekopec

GitHub https://github.com/davecom

classicproblems.com

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It all starts from physics. The entropy of an isolated system never decreases… Everyone at school, at some point of his life, learned this in his physics class. What does this have to do with machine learning?
To find out, listen to the show.

 

References

Entropy in machine learning 
https://amethix.com/entropy-in-machine-learning/

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In this episode I met three crazy researchers from KULeuven (Belgium) who found a method to fool surveillance cameras and stay hidden just by holding a special t-shirt. 
We discussed about the technique they used and some consequences of their findings.

They published their paper on Arxiv and made their source code available at https://gitlab.com/EAVISE/adversarial-yolo

Enjoy the show!

 

References

Fooling automated surveillance cameras: adversarial patches to attack person detection 
Simen ThysWiebe Van RanstToon Goedemé

 

Eavise Research Group KULeuven (Belgium)
https://iiw.kuleuven.be/onderzoek/eavise

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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.

 

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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

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

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In this episode I continue the conversation from the previous one, about failing machine learning models.

When data scientists have access to the distributions of training and testing datasets it becomes relatively easy to assess if a model will perform equally on both datasets. What happens with private datasets, where no access to the data can be granted?

At fitchain we might have an answer to this fundamental problem.

 

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In this episode I explain the differences between L1 and L2 regularization that you can find in function minimization in basically any machine learning model.

 

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Despite what researchers claim about genetic evolution, in this episode we give a realistic view of the field.

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Continuing the discussion of the last two episodes, there is one more aspect of deep learning that I would love to consider and therefore left as a full episode, that is parallelising and distributing deep learning on relatively large clusters.

As a matter of fact, computing architectures are changing in a way that is encouraging parallelism more than ever before. And deep learning is no exception and despite the greatest improvements with commodity GPUs - graphical processing units, when it comes to speed, there is still room for improvement.

Together with the last two episodes, this one completes the picture of deep learning at scale. Indeed, as I mentioned in the previous episode, How to master optimisation in deep learning, the function optimizer is the horsepower of deep learning and neural networks in general. A slow and inaccurate optimisation method leads to networks that slowly converge to unreliable results.

In another episode titled “Additional strategies for optimizing deeplearning” I explained some ways to improve function minimisation and model tuning in order to get better parameters in less time. So feel free to listen to these episodes again, share them with your friends, even re-broadcast or download for your commute.

While the methods that I have explained so far represent a good starting point for prototyping a network, when you need to switch to production environments or take advantage of the most recent and advanced hardware capabilities of your GPU, well... in all those cases, you would like to do something more.  

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In the last episode How to master optimisation in deep learning I explained some of the most challenging tasks of deep learning and some methodologies and algorithms to improve the speed of convergence of a minimisation method for deep learning.
I explored the family of gradient descent methods - even though not exhaustively - giving a list of approaches that deep learning researchers are considering for different scenarios. Every method has its own benefits and drawbacks, pretty much depending on the type of data, and data sparsity. But there is one method that seems to be, at least empirically, the best approach so far.

Feel free to listen to the previous episode, share it, re-broadcast or just download for your commute.

In this episode I would like to continue that conversation about some additional strategies for optimising gradient descent in deep learning and introduce you to some tricks that might come useful when your neural network stops learning from data or when the learning process becomes so slow that it really seems it reached a plateau even by feeding in fresh data.

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The secret behind deep learning is not really a secret. It is function optimisation. What a neural network essentially does, is optimising a function. In this episode I illustrate a number of optimisation methods and explain which one is the best and why.

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