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 Reference...
Jul 16, 2019•31 min•Ep. 63
In this episode I have a wonderful conversation with Chris Skinner. Chris and I recently got in touch at The banking scene 2019 , fintech conference recently held in Brussels. During that conference he talked as a real trouble maker - that’s how he defines himself - saying that “People are not educated with loans, credit, money” and that “Banks are failing at digital”. After I got my hands on his last book Digital Human, I invited him to the show to ask him a few questions about innovation, regu...
Jul 09, 2019•42 min•Ep. 64
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/...
Jul 02, 2019•29 min•Ep. 62
In this episode I talk about a new paradigm of learning, which can be found a bit blurry and not really different from the other methods we know of, such as supervised and unsupervised learning. The method I introduce here is called self-supervised learning. Enjoy the show! Don't forget to subscribe to our Newsletter at amethix.com and get the latest updates in AI and machine learning. We do not spam. Promise! References Deep Clustering for Unsupervised Learning of Visual Features Self-supervise...
Jun 25, 2019•19 min•Ep. 61
The successes of deep learning for text analytics, also introduced in a recent post about sentiment analysis and published here are undeniable. Many other tasks in NLP have also benefitted from the superiority of deep learning methods over more traditional approaches. Such extraordinary results have also been possible due to the neural network approach to learn meaningful character and word embeddings , that is the representation space in which semantically similar objects are mapped to nearby v...
Jun 23, 2019•12 min•Ep. 60
The rapid diffusion of social media like Facebook and Twitter, and the massive use of different types of forums like Reddit, Quora, etc., is producing an impressive amount of text data every day. There is one specific activity that many business owners have been contemplating over the last five years, that is identifying the social sentiment of their brand, by analysing the conversations of their users. In this episode I explain how one can get the best shot at classifying sentences with deep le...
Jun 14, 2019•13 min•Ep. 59
In this episode I speak to Alexandr Honchar, data scientist and owner of blog https://medium.com/@alexrachnog Alexandr has written very interesting posts about time series analysis for financial data. His blog is in my personal list of best tutorial blogs. We discuss about financial time series and machine learning, what makes predicting the price of stocks a very challenging task and why machine learning might not be enough. As usual, I ask Alexandr how he sees machine learning in the next 10 y...
Jun 04, 2019•21 min•Ep. 58
In this episode I have a wonderful conversation with Chris Skinner. Chris and I recently got in touch at The banking scene 2019 , fintech conference recently held in Brussels. During that conference he talked as a real trouble maker - that’s how he defines himself - saying that “People are not educated with loans, credit, money” and that “Banks are failing at digital”. After I got my hands on his last book Digital Human, I invited him to the show to ask him a few questions about innovation, regu...
May 28, 2019•42 min•Ep. 57
It all starts from physics. Th e 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/
May 21, 2019•22 min•Ep. 56
Deep learning is the future. Get a crash course on deep learning. Now! In this episode I speak to Oliver Zeigermann, author of Deep Learning Crash Course published by Manning Publications at https://www.manning.com/livevideo/deep-learning-crash-course Oliver (Twitter: @DJCordhose ) is a veteran of neural networks and machine learning. In addition to the course - that teaches you concepts from prototype to production - he's working on a really cool project that predicts something people do every ...
May 16, 2019•40 min•Ep. 55
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 Thys , W...
May 07, 2019•24 min•Ep. 54
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.
Apr 30, 2019•20 min•Ep. 53
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 Comp...
Apr 23, 2019•16 min•Ep. 52
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
Apr 16, 2019•17 min•Ep. 51
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...
Apr 09, 2019•17 min•Ep. 50
In this episode I speak about how important reproducible machine learning pipelines are. When you are collaborating with diverse teams, several tasks will be distributed among different individuals. Everyone will have good reasons to change parts of your pipeline, leading to confusion and definitely a number of options that soon explode. In all those cases, tracking data and code is extremely helpful to build models that are reproducible anytime, anywhere. Listen to the podcast and learn how....
Mar 09, 2019•12 min•Ep. 49
Have you ever wanted to get an estimate of the uncertainty of your neural network? Clearly Bayesian modelling provides a solid framework to estimate uncertainty by design. However, there are many realistic cases in which Bayesian sampling is not really an option and ensemble models can play a role. In this episode I describe a simple yet effective way to estimate uncertainty, without changing your neural network’s architecture nor your machine learning pipeline at all. The post with mathematical...
Jan 23, 2019•15 min•Ep. 48
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.
Jan 17, 2019•16 min•Ep. 47
In this episode I am completing the explanation about the integration fitchain-oceanprotocol that allows secure on-premise compute to operate in the decentralized data marketplace designed by Ocean Protocol . As mentioned in the show, this is a picture that provides a 10000-feet view of the integration. I hope you enjoy the show!
Jan 08, 2019•23 min•Ep. 46
In this episode I briefly explain how two massive technologies have been merged in 2018 (work in progress :) - one providing secure machine learning on isolated data, the other implementing a decentralized data marketplace. In this episode I explain: How do we make machine learning decentralized and secure? How can data owners keep their data private? How can we benefit from blockchain technology for AI and machine learning? I hope you enjoy the show! References fitchain.io decentralized machine...
Dec 26, 2018•24 min•Ep. 45
It's always good to put in perspective all the findings in AI, in order to clear some of the most common misunderstandings and promises. In this episode I make a list of some of the most misleading statements about what artificial intelligence can achieve in the near future.
Dec 19, 2018•21 min•Ep. 44
In this episode - which I advise to consume at night, in a quite place - I speak about private machine learning and blockchain, while I sip a cup of coffee in my home office. There are several reasons why I believe we should start thinking about private machine learning... It doesn't really matter what approach becomes successful and gets adopted, as long as it makes private machine learning possible. If people own their data, they should also own the by-product of such data. Decentralized machi...
Oct 21, 2018•29 min•Ep. 43
Today I am having a conversation with Filip Piękniewski, researcher working on computer vision and AI at Koh Young Research America. His adventure with AI started in the 90s and since then a long list of experiences at the intersection of computer science and physics, led him to the conclusion that deep learning might not be sufficient nor appropriate to solve the problem of intelligence, specifically artificial intelligence. I read some of his publications and got familiar with some of his idea...
Sep 11, 2018•57 min•Ep. 42
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.
Sep 04, 2018•17 min•Ep. 41
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.
Aug 28, 2018•16 min•Ep. 40
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. Behind a tweet of 140 characters there are more than 140 fields of data that draw a much more detailed profile of the sender and the content she is producing... without ever considering the tweet itself. Re...
Aug 21, 2018•21 min•Ep. 39
Today’s episode is about text analysis with python. Python is the de facto standard in machine learning. A large community, a generous choice in the set of libraries, at the price of less performant tasks, sometimes. But overall a decent language for typical data science tasks. 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....
Aug 14, 2018•37 min•Ep. 38
Attacking deep learning models Compromising AI for fun and profit 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 i...
Aug 07, 2018•29 min•Ep. 37
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. References Prediction Analysis Lab Duke University https://users.cs.du...
Jul 31, 2018•18 min•Ep. 36
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. As a very general rule, the more one compresses an image the lower the quality, due to a number of factors like bitrate, quantization error, etcetera. I am glad to be here with Tong Chen, researcher at the School of electronic Science and Engineering of Nanjing University, Chin...
Jul 24, 2018•17 min•Ep. 35
