Data Science at Home

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.

The success of a machine learning model depends on several factors and events. True generalization to data that the model has never seen before is more a chimera than a reality. But under specific conditions a well trained machine learning model can generalize well and perform with testing accuracy that is similar to the one performed during training.

In this episode I explain when and why machine learning models fail from training to testing datasets.

In this episode I don't talk about data. In fact, I talk about metadata.

While many machine learning models rely on certain amounts of data eg. text, images, audio and video, it has been proved how powerful is the signal carried by metadata, that is all data that is invisible to the end user.

References

Today’s episode is about text analysis with python.

I am with Rebecca Bilbro, co-author of Applied Text Analysis with Python, with Benjamin Bengfort and Tony Ojeda.

We speak about the evolution of applied text analysis, tools and pipelines, chatbots.

Deep learning models have shown very promising results in computer vision and sound recognition. As more and more deep learning based systems get integrated in disparate domains, they will keep affecting the life of people. Autonomous vehicles, medical imaging and banking applications, surveillance cameras and drones, digital assistants, are only a few real applications where deep learning plays a fundamental role. A malfunction in any of these applications will affect the quality of such integrated systems and compromise the security of the individuals who directly or indirectly use them.

In this episode, we explain how machine learning models can be attacked and what we can do to protect intelligent systems from being  compromised.

Today’s episode  will be about deep learning and reasoning. There has been a lot of discussion about the effectiveness of deep learning models and their capability to generalize, not only across domains but also on data that such models have never seen.

But there is a research group from the Department of Computer Science, Duke University that seems to be on something with deep learning and interpretability in computer vision.

Prediction Analysis Lab Duke University https://users.cs.duke.edu/~cynthia/lab.html

This looks like that: deep learning for interpretable image recognition https://arxiv.org/abs/1806.10574

Today’s episode  will be about deep learning and compression of data, and in particular compressing images. We all know how important compressing data is, reducing the size of digital objects without affecting the quality.

Tong developed a deep learning based compression algorithm for images, that seems to improve over state of the art approaches like BPG, JPEG2000 and JPEG.

Deep Image Compression via End-to-End Learning - Haojie Liu, Tong Chen, Qiu Shen, Tao Yue, and Zhan Ma School of Electronic Science and Engineering, Nanjing University, Jiangsu, China

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.

In the second part of this episode I am interviewing Johannes Castner from CollectiWise, a platform for collective intelligence.

1. Opencog.org