Data Skeptic (general)

How long an algorithm takes to run depends on many factors including implementation details and hardware.  However, the formal analysis of algorithms focuses on how they will perform in the worst case as the input size grows.  We refer to an algorithm's runtime as it's "O" which is a function of its input size "n".  For example, O(n) represents a linear algorithm - one that takes roughly twice as long to run if you double the input size.  In this episode, we discuss a few everyday examples of algorithmic analysis including sorting, search a shuffled deck of cards, and verifying if a grocery list was successfully completed.

Thanks to our sponsor, who right now is featuring a related problem as their Brilliant Problem of the Week.

Direct download: big-oh-analysis.mp3
Category:general -- posted at: 8:00am PDT

One Shot Learning is the class of machine learning procedures that focuses learning something from a small number of examples.  This is in contrast to "traditional" machine learning which typically requires a very large training set to build a reasonable model.

In this episode, Kyle presents a coded message to Linhda who is able to recognize that many of these new symbols created are likely to be the same symbol, despite having extremely few examples of each.  Why can the human brain recognize a new symbol with relative ease while most machine learning algorithms require large training data?  We discuss some of the reasons why and approaches to One Shot Learning.

Direct download: one-shot-learning.mp3
Category:general -- posted at: 8:00am PDT

Recommender systems play an important role in providing personalized content to online users. Yet, typical data mining techniques are not well suited for the unique challenges that recommender systems face. In this episode, host Kyle Polich joins Dr. Joseph Konstan from the University of Minnesota at a live recording at FARCON 2017 in Minneapolis to discuss recommender systems and how machine learning can create better user experiences. 

Direct download: recommender-systems-live-from-farcon.mp3
Category:general -- posted at: 8:00am PDT

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A Long Short Term Memory (LSTM) is a neural unit, often used in Recurrent Neural Network (RNN) which attempts to provide the network the capacity to store information for longer periods of time. An LSTM unit remembers values for either long or short time periods. The key to this ability is that it uses no activation function within its recurrent components. Thus, the stored value is not iteratively modified and the gradient does not tend to vanish when trained with backpropagation through time.

Direct download: long-short-term-memory.mp3
Category:general -- posted at: 8:00am PDT

Zillow is a leading real estate information and home-related marketplace. We interviewed Andrew Martin, a data science Research Manager at Zillow, to learn more about how Zillow uses data science and big data to make real estate predictions.

Direct download: zillow-zestimate.mp3
Category:general -- posted at: 8:00am PDT

Our guest Pranav Rajpurkar and his coauthored recently published Cardiologist-Level Arrhythmia Detection with Convolutional Neural Networks, a paper in which they demonstrate the use of Convolutional Neural Networks which outperform board certified cardiologists in detecting a wide range of heart arrhythmias from ECG data.

Direct download: cardiologist-level-arrhythmia-detection-with-cnns.mp3
Category:general -- posted at: 8:00am PDT

RNNs are a class of deep learning models designed to capture sequential behavior.  An RNN trains a set of weights which depend not just on new input but also on the previous state of the neural network.  This directed cycle allows the training phase to find solutions which rely on the state at a previous time, thus giving the network a form of memory.  RNNs have been used effectively in language analysis, translation, speech recognition, and many other tasks.

Direct download: recurrent-neural-networks.mp3
Category:general -- posted at: 8:00am PDT

Thanks to our sponsor Springboard.

In this week's episode, guest Andre Natal from Mozilla joins our host, Kyle Polich, to discuss a couple exciting new developments in open source speech recognition systems, which include Project Common Voice.

In June 2017, Mozilla launched a new open source project, Common Voice, a novel complementary project to the TensorFlow-based DeepSpeech implementation. DeepSpeech is a deep learning-based voice recognition system that was designed by Baidu, which they describe in greater detail in their research paper. DeepSpeech is a speech-to-text engine, and Mozilla hopes that, in the future, they can use Common Voice data to train their DeepSpeech engine.

Direct download: project-common-voice.mp3
Category:general -- posted at: 8:00am PDT

A Bayesian Belief Network is an acyclic directed graph composed of nodes that represent random variables and edges that imply a conditional dependence between them. It's an intuitive way of encoding your statistical knowledge about a system and is efficient to propagate belief updates throughout the network when new information is added.

Direct download: bayesian-belief-networks.mp3
Category:general -- posted at: 11:58pm PDT

In this episode, Tony Beltramelli of UIzard Technologies joins our host, Kyle Polich, to talk about the ideas behind his latest app that can transform graphic design into functioning code, as well as his previous work on spying with wearables.

Direct download: pix2code.mp3
Category:general -- posted at: 8:00am PDT