September 22, 2017
One Shot Learning

by Kyle Polich

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 mo... View More >

September 15, 2017

A deep dive on Recommender Systems with Joseph Konstan in our first live episode ever, which took place at FARCon 2017.... View More >

September 8, 2017

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.... View More >

September 1, 2017
Zillow Zestimate

by Kyle Polich

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.... View More >

August 25, 2017

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 d... View More >

August 18, 2017

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 ta... View More >

August 11, 2017
Project Common Voice

by Kyle Polich

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.... View More >

August 4, 2017

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 propogate belief updates throughout the network when new information is ad... View More >

July 28, 2017

by Kyle Polich

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 wearab... View More >

July 21, 2017

In statistics, two random variables might depend on one another (for example, interest rates and new home purchases). We call this conditional dependence. An important related concept exists called conditional independence. This phrase describes situations in which two variables are independent of one another given some other varia... View More >

July 14, 2017

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 much an animal is suffering. The sheep, for instance, is the most inscrutable of animals. However, scientists have figured out a way to understand sheep facial expressions using artificial intelligence.... View More >

July 7, 2017
Cosmos DB

by Kyle Polich

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 recently announced CosmosDB. CosmosDB is a globally consistent, distributed datastore that supports all the popular persistent storage formats (relational, key/value pair, document database, and graph) under a single streamlined API. The system provides tunable consistency, allowing the user to make choices about how consistency trade-offs are managed under the hood, if a consumer wants to go beyond the selected defau... View More >

June 30, 2017

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 zero by the time back-propogation has reached the first hidden layer. This makes learning virtually impossible without some clever trick or improved methodology to help earlier layers begin to le... View More >

June 23, 2017
Doctor AI

by Kyle Polich

When 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: Predicting Clinical Events via Recurrent Neural Network shares his thoughts. Edward presents his team's efforts in developing a temporal model that can learn from human doctors based on their collective knowledge, i.e. the large amount of Electronic Health Record (EHR) data.... View More >

June 9, 2017
Ms Build 2017

by Kyle Polich

This episode recaps the Microsoft Build Conference. Kyle recently attended and shares some thoughts on cloud, databases, cognitive services, and artificial intelligence. The episode includes interiews with Rohan Kumar and David Carm... View More >

June 9, 2017
Activation Functions

by Kyle Polich

desc... View More >

June 2, 2017
Max Pooling

by Kyle Polich

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 them to a single value for future layers to receive as input. It can also prevent overfitting, since it takes a large set of inputs and admits only one value, making it harder to memorize the input. In this episode, we discuss the intuitive interpretation of max-pooling and why it's more common than mean-pooling or (theoretically) quartile-pool... View More >

May 26, 2017

This episode is an interview with Tinghui Zhou. In the recent paper "Unsupervised Learning of Depth and Ego-motion from Video", Tinghui and collaborators propose an deep learning architecture which is able to learn depth and pose information from unlabeled videos. We discuss details of this project and it's applicati... View More >

May 18, 2017

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 entire image. In this way, CNNs may achieve the property of translational invariance - once trained to recognize certain things, changing the position of that thing in an image should not disrupt the CNN's ability to recognize it. In this episode, we discuss a few high level details of this important architect... View More >

May 12, 2017

In this episode, Arnab Ghosh and Viveka Kulharia from Oxford University join Kyle in a lively discussion on one of the major pitfalls in generative adversarial networks-- mode collapse. Arnab and Viveka share a solution to tackle this problem.... View More >

May 5, 2017

GANs are an unsupervised learning method involving two neural networks iteratively competing. The discriminator is a typical learning system. It attempts to develop the ability to recognize members of a certain class, such as all photos which have birds in them. The generator attempts to create false examples which the discriminator incorrectly classifies. In successive training rounds the networks examine each and play a mini-max game of trying to harm the performance of the ot... View More >

April 28, 2017

Recently, we've seen opinion polls come under some skepticism. But is that skepticism truly justified? The recent Brexit referendum and US 2016 Presidential Election are examples where some claim the polls "got it wrong." This episode explores this i... View More >

April 21, 2017

by Kyle Polich

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 in studying this data, our hands are complete tied. Opportunities like testing sociological theories, exploring economic impacts, study market forces, or simply research the value of an investment when buying a home are all blocked by the lack of easy access to this dataset. OpenHouse seeks to correct that by centralizing and standardizing all publicly available home sales transactional data. In this episode, we discuss the achievements of OpenHouse to date, and what plans exist for the fut... View More >

April 14, 2017

by Kyle Polich

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 to be highly optimized for doing floating point computations in parallel. These types of operations were very useful for high end video games, but as it turns out, those same processors are extremely useful for machine learning. In this mini-episode we discuss ... View More >

April 7, 2017

by Kyle Polich

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 using stochastic gradient descent to iteratively fine tune the weights of the network. In this episode, we compare this concept to finding a location on a map, marble maze games, and g... View More >

March 31, 2017

Maura Church is a Data Science Manager at Patreon, where she focuses on growth modeling, product analytics, and building a great Data Science team. Before joining Patreon in 2015, she worked at Google fighting spam and abuse in communications products. Maura's work explores the intersection of art and technology, specifically how to use computational methods to change the way people relate to art. Maura holds a B.A. in Applied Mathematics with a concentration in Music from Harvard University. When she's not working, you can find her singing in the San Francisco Bach choir, biking in Marin, or serving as a board member of the Grammy-award winning ensemble Chanticl... View More >

March 24, 2017

In a feed forward neural network, neurons cannot form a cycle. In this episode, we explore how such a network would be able to represent three common logical opperators: OR, AND, and XOR. The XOR opperation is the interesting c... View More >

March 17, 2017

Internet search engines are indispensable navigational tools for interacting on the web. In addition to addressing information requests, search result pages have become a thriving advertising platform. Online advertising offers customers a more interactive way to shop and buy, while at the same time, they give sellers a more layered method of reaching the pub... View More >

March 10, 2017
The Perceptron

by Kyle Polich

Today's episode overviews the perceptron algorithm. This rather simple approach is characterized by a few particular features. It updates it's weights after seeing every example, rather than as a batch. It uses a step function as an activation function. It's only appropriate for linearly separable data, and it will converge to a solution if the data meets this criteria. Being a fairly simple algorithm, it can run very efficiently. Although we don't discuss it in this episode, multi-layer perceptron networks are what makes this technique most attract... View More >

March 3, 2017

DataRefuge is a public collaborative, grassroots effort around the United States in which scientists, researchers, computer scientists, librarians and other volunteers are working to download, save, and re-upload government data. The DataRefuge Project, which is led by the UPenn Program in Environmental Humanities and the Penn Libraries group at University of Pennsylvania, aims to foster resilience in an era of anthropogenic global climate change and raise awareness of how social and political events affect transparen... View More >