Shows / Data Skeptic
Data Skeptic
The Data Skeptic Podcast features interviews and discussion of topics related to data science, statistics, machine learning, artificial intelligence and the like, all from the perspective of applying critical thinking and the scientific method to evaluate the veracity of claims and efficacy of approaches.
Episodes
- Data Skeptic
Complexity and Cryptography
This week, our host Kyle Polich is joined by guest Tim Henderson from Google to talk about the computational complexity foundations of modern cryptography and…
- Data Skeptic
Mercedes Benz Machine Learning Research
This episode features an interview with Rigel Smiroldo recorded at NIPS 2017 in Long Beach California. We discuss data privacy, machine learning use cases…
- Data Skeptic
[MINI] Parallel Algorithms
When computers became commodity hardware and storage became incredibly cheap, we entered the era of so-call "big" data. Most definitions of big data will…
- Data Skeptic
Quantum Computing
In this week's episode, Scott Aaronson, a professor at the University of Texas at Austin, explains what a quantum computer is, various possible applications…
- Data Skeptic
Azure Databricks
I sat down with Ali Ghodsi, CEO and found of Databricks, and John Chirapurath, GM for Data Platform Marketing at Microsoft related to the recent announcement…
- Data Skeptic
[MINI] Exponential Time Algorithms
In this episode we discuss the complexity class of EXP-Time which contains algorithms which require $O(2^{p(n)})$ time to run. In other words, the worst case…
- Data Skeptic
P vs NP
In this week's episode, host Kyle Polich interviews author Lance Fortnow about whether P will ever be equal to NP and solve all of life's problems. Fortnow…
- Data Skeptic
[MINI] Sudoku \in NP
Algorithms with similar runtimes are said to be in the same complexity class. That runtime is measured in the how many steps an algorithm takes relative to the…
- Data Skeptic
The Computational Complexity of Machine Learning
In this episode, Professor Michael Kearns from the University of Pennsylvania joins host Kyle Polich to talk about the computational complexity of machine…
- Data Skeptic
[MINI] Turing Machines
TMs are a model of computation at the heart of algorithmic analysis. A Turing Machine has two components. An infinitely long piece of tape (memory) with…
- Data Skeptic
The Complexity of Learning Neural Networks
Over the past several years, we have seen many success stories in machine learning brought about by deep learning techniques. While the practical success of…
- Data Skeptic
[MINI] Big Oh Analysis
How long an algorithm takes to run depends on many factors including implementation details and hardware. However, the formal analysis of algorithms focuses on…
- Data Skeptic
Data science tools and other announcements from Ignite
In this episode, Microsoft's Corporate Vice President for Cloud Artificial Intelligence, Joseph Sirosh, joins host Kyle Polich to share some of the Microsoft's…
- Data Skeptic
Generative AI for Content Creation
Last year, the film development and production company End Cue produced a short film, called Sunspring, that was entirely written by an artificial intelligence…
Data Skeptic[MINI] One Shot Learning
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…
- Data Skeptic
Recommender Systems Live from FARCON 2017
Recommender systems play an important role in providing personalized content to online users. Yet, typical data mining techniques are not well suited for the…
- Data Skeptic
[MINI] Long Short Term Memory
Thanks to our sponsor brilliant.org/dataskeptics A Long Short Term Memory (LSTM) is a neural unit, often used in Recurrent Neural Network (RNN) which attempts…
- Data Skeptic
Zillow Zestimate
Zillow is a leading real estate information and home-related marketplace. We interviewed Andrew Martin, a data science Research Manager at Zillow, to learn…
- Data Skeptic
Cardiologist Level Arrhythmia Detection with CNNs
Our guest Pranav Rajpurkar and his coauthored recently published Cardiologist-Level Arrhythmia Detection with Convolutional Neural Networks, a paper in which…
- Data Skeptic
[MINI] Recurrent Neural Networks
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…
- Data Skeptic
Project Common Voice
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…
- Data Skeptic
[MINI] Bayesian Belief Networks
A Bayesian Belief Network is an acyclic directed graph composed of nodes that represent random variables and edges that imply a conditional dependence between…
- Data Skeptic
pix2code
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…
- Data Skeptic
[MINI] Conditional Independence
In statistics, two random variables might depend on one another (for example, interest rates and new home purchases). We call this conditional dependence. An…
- Data Skeptic
Estimating Sheep Pain with Facial Recognition
Animals can't tell us when they're experiencing pain, so we have to rely on other cues to help treat their discomfort. But it is often difficult to tell how…
- Data Skeptic
CosmosDB
This episode collects interviews from my recent trip to Microsoft Build where I had the opportunity to speak with Dharma Shukla and Syam Nair about the…
- Data Skeptic
[MINI] The Vanishing Gradient
This episode discusses the vanishing gradient - a problem that arises when training deep neural networks in which nearly all the gradients are very close to…
- Data Skeptic
Doctor AI
hen faced with medical issues, would you want to be seen by a human or a machine? In this episode, guest Edward Choi, co-author of the study titled Doctor AI…
- Data Skeptic
[MINI] Activation Functions
In a neural network, the output value of a neuron is almost always transformed in some way using a function. A trivial choice would be a linear transformation…
- Data Skeptic
MS Build 2017
This episode recaps the Microsoft Build Conference. Kyle recently attended and shares some thoughts on cloud, databases, cognitive services, and artificial…
- Data Skeptic
[MINI] Max-pooling
Max-pooling is a procedure in a neural network which has several benefits. It performs dimensionality reduction by taking a collection of neurons and reducing…
- Data Skeptic
Unsupervised Depth Perception
This episode is an interview with Tinghui Zhou. In the recent paper "Unsupervised Learning of Depth and Ego-motion from Video", Tinghui and collaborators…
- Data Skeptic
[MINI] Convolutional Neural Networks
CNNs are characterized by their use of a group of neurons typically referred to as a filter or kernel. In image recognition, this kernel is repeated over the…
- Data Skeptic
Multi-Agent Diverse Generative Adversarial Networks
Despite the success of GANs in imaging, one of its major drawbacks is the problem of 'mode collapse,' where the generator learns to produce samples with…
- Data Skeptic
[MINI] Generative Adversarial Networks
GANs are an unsupervised learning method involving two neural networks iteratively competing. The discriminator is a typical learning system. It attempts to…
- Data Skeptic
Opinion Polls for Presidential Elections
Recently, we've seen opinion polls come under some skepticism. But is that skepticism truly justified? The recent Brexit referendum and US 2016 Presidential…
- Data Skeptic
OpenHouse
No reliable, complete database cataloging home sales data at a transaction level is available for the average person to access. To a data scientist interesting…
- Data Skeptic
[MINI] GPU CPU
There's more than one type of computer processor. The central processing unit (CPU) is typically what one means when they say "processor". GPUs were introduced…
- Data Skeptic
[MINI] Backpropagation
Backpropagation is a common algorithm for training a neural network. It works by computing the gradient of each weight with respect to the overall error, and…
- Data Skeptic
Data Science at Patreon
In this week's episode of Data Skeptic, host Kyle Polich talks with guest Maura Church, Patreon's data science manager. Patreon is a fast-growing crowdfunding…
- Data Skeptic
[MINI] Feed Forward Neural Networks
Feed Forward Neural Networks In a feed forward neural network, neurons cannot form a cycle. In this episode, we explore how such a network would be able to…
- Data Skeptic
Reinventing Sponsored Search Auctions
In this Data Skeptic episode, Kyle is joined by guest Ruggiero Cavallo to discuss his latest efforts to mitigate the problems presented in this new world of…
- Data Skeptic
[MINI] The Perceptron
Today's episode overviews the perceptron algorithm. This rather simple approach is characterized by a few particular features. It updates its weights after…
- Data Skeptic
The Data Refuge Project
DataRefuge is a public collaborative, grassroots effort around the United States in which scientists, researchers, computer scientists, librarians and other…
- Data Skeptic
[MINI] Automated Feature Engineering
If a CEO wants to know the state of their business, they ask their highest ranking executives. These executives, in turn, should know the state of the business…
- Data Skeptic
Big Data Tools and Trends
In this episode, I speak with Raghu Ramakrishnan, CTO for Data at Microsoft. We discuss services, tools, and developments in the big data sphere as well as the…
Data Skeptic[MINI] Primer on Deep Learning
In this episode, we talk about a high-level description of deep learning. Kyle presents a simple game (pictured below), which is more of a puzzle really, to…
- Data Skeptic
Data Provenance and Reproducibility with Pachyderm
Versioning isn't just for source code. Being able to track changes to data is critical for answering questions about data provenance, quality, and…
Data Skeptic[MINI] Logistic Regression on Audio Data
Logistic Regression is a popular classification algorithm. In this episode, we discuss how it can be used to determine if an audio clip represents one of two…
- Data Skeptic
Studying Competition and Gender Through Chess
Prior work has shown that people's response to competition is in part predicted by their gender. Understanding why and when this occurs is important in areas…
Data Skeptic[MINI] Dropout
Deep learning can be prone to overfit a given problem. This is especially frustrating given how much time and computational resources are often required to…
- Data Skeptic
The Police Data and the Data Driven Justice Initiatives
In this episode I speak with Clarence Wardell and Kelly Jin about their mutual service as part of the White House's Police Data Initiative and Data Driven…
Data SkepticThe Library Problem
We close out 2016 with a discussion of a basic interview question which might get asked when applying for a data science job. Specifically, how a library might…
Data Skeptic2016 Holiday Special
Today's episode is a reading of Isaac Asimov's Franchise. As mentioned on the show, this is just a work of fiction to be enjoyed and not in any way some…
Data Skeptic[MINI] Entropy
Classically, entropy is a measure of disorder in a system. From a statistical perspective, it is more useful to say it's a measure of the unpredictability of…
- Data Skeptic
MS Connect Conference
Cloud services are now ubiquitous in data science and more broadly in technology as well. This week, I speak to Mark Souza, Tobias Ternström, and Corey Sanders…
Data SkepticCausal Impact
Today's episode is all about Causal Impact, a technique for estimating the impact of a particular event on a time series. We talk to William Martin about his…
Data Skeptic[MINI] The Bootstrap
The Bootstrap is a method of resampling a dataset to possibly refine it's accuracy and produce useful metrics on the result. The bootstrap is a useful…
Data Skeptic[MINI] Gini Coefficients
The Gini Coefficient (as it relates to decision trees) is one approach to determining the optimal decision to introduce which splits your dataset as part of a…
- Data Skeptic
Unstructured Data for Finance
Financial analysis techniques for studying numeric, well structured data are very mature. While using unstructured data in finance is not necessarily a new…