Data Science at Home

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  

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, regulation and technology in finance.

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

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-supervised Visual Feature Learning with Deep Neural Networks: A Survey  

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 vectors. All this is strictly related to a field one might initially find disconnected or off-topic: biology.   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 [1] Rives A., et al., “Biological structure and function emerge from scaling unsupervised learning to 250 million protein sequences”, biorxiv, doi: https://doi.org/10.1101/622803 [2] Vaswani A., et al., “Attention is all you need”, Advances in neural information processing systems, pp. 5998–6008, 2017. [3] Bahdanau D., et al., “Neural machine translation by jointly learning to align and translate”, arXiv, http://arxiv.org/abs/1409.0473.

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 learning and word embedding.     Additional material Schematic representation of how to learn a word embedding matrix E by training a neural network that, given the previous M words, predicts the next word in a sentence.        Word2Vec example source code https://gist.github.com/rlangone/ded90673f65e932fd14ae53a26e89eee#file-word2vec_example-py     References [1] Mikolov, T. et al., "Distributed Representations of Words and Phrases and their Compositionality", Advances in Neural Information Processing Systems 26, pages 3111-3119, 2013. [2] The Best Embedding Method for Sentiment Classification, https://medium.com/@bramblexu/blog-md-34c5d082a8c5 [3] The state of sentiment analysis: word, sub-word and character embedding  https://amethix.com/state-of-sentiment-analysis-embedding/  

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 years. His answer - in my opinion quite futuristic - makes perfect sense.  You can contact Alexandr on Twitter https://twitter.com/AlexRachnog Facebook https://www.facebook.com/rachnog Medium https://medium.com/@alexrachnog   Enjoy the show!  

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, regulation and technology in finance.

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/

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 day... clicking their mouse.  If you use promo code poddatascienceathome19 you get a 40% discount for all products on the Manning platform Enjoy the show!   References:   Deep Learning Crash Course (Manning Publications) https://www.manning.com/livevideo/deep-learning-crash-course?a_aid=djcordhose&a_bid=e8e77cbf   Companion notebooks for the code samples of the video course "Deep Learning Crash Course" https://github.com/DJCordhose/deep-learning-crash-course-notebooks/blob/master/README.md   Next-button-to-click predictor source code https://github.com/DJCordhose/ux-by-tfjs