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June 13, 2019
Stripe Machine Learning Infrastructure with Rob Story and Kelley Rivoire
Machine learning allows software to improve as that software consumes more data. Machine learning is a tool that every software engineer wants to be able to use. Because machine learning is so broadly applicable, software companies want to make the tools more accessible to the developers across the organization. There are many steps that an
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71 min
May 28, 2019
Augmented Reality Gaming with Tony Godar
Augmented reality applications can be used on smartphones and dedicated AR headsets. On smartphones, ARCore (Google) and ARKit (Apple) allow developers to build for the camera on a user’s smartphone. AR headsets such as Microsoft HoloLens and Magic Leap allow for a futuristic augmented reality headset experience. The most prominent use of augmented reality today
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52 min
April 17, 2019
Drishti: Deep Learning for Manufacturing with Krish Chaudhury
RECENT UPDATES: Podsheets is our open source set of tools for managing podcasts and podcast businesses New version of Software Daily, our app and ad-free subscription service Software Daily is looking for help with Android engineering, QA, machine learning, and more FindCollabs Hackathon has ended–winners will probably be announced by the time this episode airs;
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59 min
April 15, 2019
Protein Structure Deep Learning with Mohammed Al Quraishi
RECENT UPDATES: Podsheets is our open source set of tools for managing podcasts and podcast businesses New version of Software Daily, our app and ad-free subscription service Software Daily is looking for help with Android engineering, QA, machine learning, and more FindCollabs Hackathon has ended–winners will probably be announced by the time this episode airs;
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60 min
April 10, 2019
Machine Learning Joins with Arun Kumar
RECENT UPDATES: FindCollabs $5000 Hackathon Ends Saturday April 15th, 2019 New version of Software Daily, our app and ad-free subscription service Software Daily is looking for help with Android engineering, QA, machine learning, and more Data sets can be modeled in a row-wise, relational format. When two data sets share a common field, those data
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67 min
March 11, 2019
Energy Market Machine Learning with Minh Dang and Corey Noone
The demand for electricity is based on the consumption of the electrical grid at a given time. The supply of electricity is based on how much energy is being produced or stored on the grid at a given time. Because these sources of supply and demand fluctuate rapidly but predictably, energy markets present profit opportunities
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51 min
February 20, 2019
Zoox Self-Driving with Ethan Dreyfuss
Zoox is a full-stack self-driving car company. Zoox engineers work on everything a self-driving car company needs, from the physical car itself to the algorithms running on the car to the ride hailing system which the company plans to use to drive around riders. Since starting in 2014, Zoox has grown to over 500 employees.
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63 min
February 19, 2019
Store2Vec: DoorDash Recommendations with Mitchell Koch
DoorDash is a food delivery company where users find restaurants to order from. When a user opens the DoorDash app, the user can search for types of food or specific restaurants from the search bar or they can scroll through the feed section and look at recommendations that the app gives them within their local
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49 min
January 31, 2019
Architects of Intelligence with Martin Ford
Artificial intelligence is reshaping every aspect of our lives, from transportation to agriculture to dating. Someday, we may even create a superintelligence–a computer system that is demonstrably smarter than humans. But there is widespread disagreement on how soon we could build a superintelligence. There is not even a broad consensus on how we can define
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64 min
January 25, 2019
Kubeflow: TensorFlow on Kubernetes with David Aronchick
When TensorFlow came out of Google, the machine learning community converged around it. TensorFlow is a framework for building machine learning models, but the lifecycle of a machine learning model has a scope that is bigger than just creating a model. Machine learning developers also need to have a testing and deployment process for continuous
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62 min
January 16, 2019
Human Sized Robots with Zach Allen
Robots are making their way into every area of our lives. Security robots roll around industrial parks at night, monitoring the area for intruders. Amazon robots tirelessly move packages around in warehouses, reducing the time and cost of logistics. Self-driving cars have become a ubiquitous presence in cities like San Francisco. For a hacker in
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53 min
December 28, 2018
Word2Vec with Adrian Colyer Holiday Repeat
Originally posted on 13 September 2017. Machines understand the world through mathematical representations. In order to train a machine learning model, we need to describe everything in terms of numbers.  Images, words, and sounds are too abstract for a computer. But a series of numbers is a representation that we can all agree on, whether
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61 min
December 27, 2018
Self-Driving Deep Learning with Lex Fridman Holiday Repeat
Originally posted on 28 July 2017. Self-driving cars are here. Fully autonomous systems like Waymo are being piloted in less complex circumstances. Human-in-the-loop systems like Tesla Autopilot navigate drivers when it is safe to do so, and lets the human take control in ambiguous circumstances. Computers are great at memorization, but not yet great at
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59 min
November 21, 2018
Poker Artificial Intelligence with Noam Brown Holiday Repeat
Originally posted on May 12, 2015. Humans have now been defeated by computers at heads up no-limit holdem poker. Some people thought this wouldn’t be possible. Sure, we can teach a computer to beat a human at Go or Chess. Those games have a smaller decision space. There is no hidden information. There is no
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55 min
November 16, 2018
Reflow: Distributed Incremental Processing with Marius Eriksen
The volume of data in the world is always increasing. The costs of storing that data is always decreasing. And the means for processing that data is always evolving. Sensors, cameras, and other small computers gather large quantities of data from the physical world around us. User analytics tools gather information about how we are
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72 min
November 7, 2018
Computer Architecture with Dave Patterson
An instruction set defines a low level programming language for moving information throughout a computer. In the early 1970’s, the prevalent instruction set language used a large vocabulary of different instructions. One justification for a large instruction set was that it would give a programmer more freedom to express the logic of their programs. Many
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51 min
October 31, 2018
Diffbot: Knowledge Graph API with Mike Tung
Google Search allows humans to find and access information across the web. A human enters an unstructured query into the search box, the search engine provides several links as a result, and the human clicks on one of those links. That link brings up a web page, which is a set of unstructured data. Humans
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57 min
October 30, 2018
Drift: Sales Bot Engineering with David Cancel
David Cancel has started five companies, most recently Drift. Drift is a conversational marketing and sales platform. David has a depth of engineering skills and a breadth of business experience that make him an amazing source of knowledge. In today’s episode, David discusses topics ranging from the technical details of making a machine learning-driven sales
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60 min
October 11, 2018
Generative Models with Doug Eck
Google Brain is an engineering team focused on deep learning research and applications. One growing area of interest within Google Brain is that of generative models. A generative model uses neural networks and a large data set to create new data similar to the ones that the network has seen before. One approach to making
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68 min
September 11, 2018
Real Estate Machine Learning with Or Hiltch
Stock traders have access to high volumes of information to help them make decisions on whether to buy an asset. A trader who is considering buying a share of Google stock can find charts, reports, and statistical tools to help with their decision. There are a variety of machine learning products to help a technical
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58 min
August 31, 2018
RideOS: Fleet Management with Rohan Paranjpe
Self-driving transportation will be widely deployed at some point in the future. How far off is that future? There are widely varying estimations: maybe you will summon a self-driving Uber in a New York within 5 years, or maybe it will take 20 years to work out all of the challenges in legal and engineering.
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58 min
August 23, 2018
Stitch Fix Engineering with Cathy Polinsky
Stitch Fix is a company that recommends packages of clothing based on a set of preferences that the user defines and updates over time. Stitch Fix’s software platform includes the website, data engineering infrastructure, and warehouse software. Stitch Fix has over 5000 employees, including a large team of engineers. Cathy Polinsky is the CTO of
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57 min
August 16, 2018
DoorDash Engineering with Raghav Ramesh
DoorDash is a last mile logistics company that connects customers with their favorite national and local businesses. When a customer orders from a restaurant, DoorDash needs to identify the ideal driver for picking up the order from the restaurant and dropping it off with the customer. This process of matching an order to a driver
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58 min
August 8, 2018
Self-Driving Engineering with George Hotz
In the smartphone market there are two dominant operating systems: one closed source (iPhone) and one open source (Android). The market for self-driving cars could play out the same way, with a company like Tesla becoming the closed source iPhone of cars, and a company like Comma.ai developing the open source Android of self-driving cars.
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64 min
July 19, 2018
Botchain with Rob May
“Bots” are becoming increasingly relevant to our everyday interactions with technology. A bot sometimes mediates the interactions of two people. Examples of bots include automated reply systems, intelligent chat bots, classification systems, and prediction machines. These systems are often powered by machine learning systems that are black boxes to the user. Today’s guest Rob May
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53 min
July 13, 2018
Machine Learning Deployments with Diego Oppenheimer
Machine learning models allow our applications to perform highly accurate inferences. A model can be used to classify a picture as a cat, or to predict what movie I might want to watch. But before a machine learning model can be used to make these inferences, the model must be trained and deployed. In the
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60 min
July 5, 2018
Machine Learning Stroke Identification with David Golan
When a patient comes into the hospital with stroke symptoms, the hospital will give that patient a CAT scan, a 3-dimensional imaging of the patient’s brain. The CAT scan needs to be examined by a radiologist, and the radiologist will decide whether to refer the patient to an interventionist–a surgeon who can perform an operation
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57 min
June 15, 2018
Digital Evolution with Joel Lehman, Dusan Misevic, and Jeff Clune
Evolutionary algorithms can generate surprising, effective solutions to our problems. Evolutionary algorithms are often let loose within a simulated environment. The algorithm is given a function to optimize for, and the engineers expect that algorithm to evolve a solution that optimizes for the objective function given the constraints of the simulated environment. But sometimes these
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57 min
June 7, 2018
Future of Computing with John Hennessy
Moore’s Law states that the number of transistors in a dense integrated circuit doubles about every two years. Moore’s Law is less like a “law” and more like an observation or a prediction. Moore’s Law is ending. We can no longer fit an increasing amount of transistors in the same amount of space with a
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61 min
June 4, 2018
OpenAI: Compute and Safety with Dario Amodei
Applications of artificial intelligence are permeating our everyday lives. We notice it in small ways–improvements to speech recognition; better quality products being recommended to us; cheaper goods and services that have dropped in price because of more intelligent production. But what can we quantitatively say about the rate at which artificial intelligence is improving? How
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63 min
May 21, 2018
Voice with Rita Singh
A sample of the human voice is a rich piece of unstructured data. Voice recordings can be turned into visualizations called spectrograms. Machine learning models can be trained to identify features of these spectrograms. Using this kind of analytic strategy, breakthroughs in voice analysis are happening at an amazing pace. Rita Singh researches voice at
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62 min
May 19, 2018
Machine Learning with Data Skeptic and Second Spectrum at Telesign
Data Skeptic is a podcast about machine learning, data science, and how software affects our lives. The first guest on today’s episode is Kyle Polich, the host of Data Skeptic. Kyle is one of the best explainers of machine learning concepts I have met, and for this episode, he presented some material that is perfect
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70 min
May 10, 2018
Deep Learning Topologies with Yinyin Liu
Algorithms for building neural networks have existed for decades. For a long time, neural networks were not widely used. Recent changes to the cost of compute and the size of our data have made neural networks extremely useful. Our smart phones generate terabytes of useful data. Lower storage costs make it economical to keep that
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60 min
April 28, 2018
Keybase Architecture / Clarifai Infrastructure Meetup Talks
Keybase is a platform for managing public key infrastructure. Keybase’s products simplify the complicated process of associating your identity with a public key. Keybase is the subject of the first half of today’s show. Michael Maxim, an engineer from Keybase gives an overview for how the technology works and what kinds of applications Keybase unlocks.
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72 min
April 26, 2018
TensorFlow Applications with Rajat Monga
Rajat Monga is a director of engineering at Google where he works on TensorFlow. TensorFlow is a framework for numerical computation developed at Google. The majority of TensorFlow users are building machine learning applications such as image recognition, recommendation systems, and natural language processing–but TensorFlow is actually applicable to a broader range of scientific computation
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56 min
February 27, 2018
Scale Self-Driving with Alexandr Wang
The easiest way to train a computer to recognize a picture of cat is to show the computer a million labeled images of cats. The easiest way to train a computer to recognize a stop sign is to show the computer a million labeled stop signs. Supervised machine learning systems require labeled data. Today, most
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49 min
February 14, 2018
Machine Learning Deployments with Kinnary Jangla
Pinterest is a visual feed of ideas, products, clothing, and recipes. Millions of users browse Pinterest to find images and text that are tailored to their interests. Like most companies, Pinterest started with a large monolithic application that served all requests. As Pinterest’s engineering resources expanded, some of the architecture was broken up into microservices
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47 min
January 29, 2018
Deep Learning Hardware with Xin Wang
Training a deep learning model involves operations over tensors. A tensor is a multi-dimensional array of numbers. For several years, GPUs were used for these linear algebra calculations. That’s because graphics chips are built to efficiently process matrix operations. Tensor processing consists of linear algebra operations that are similar in some ways to graphics processing–but
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57 min
January 26, 2018
Edge Deep Learning with Aran Khanna
A modern farm has hundreds of sensors to monitor the soil health, and robotic machinery to reap the vegetables. A modern shipping yard has hundreds of computers working together to orchestrate and analyze the freight that is coming in from overseas. A modern factory has temperature gauges and smart security cameras to ensure workplace safety.
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57 min
December 25, 2017
Machine Learning and Technical Debt with D. Sculley Holiday Repeat
Originally published November 17, 2015 “Changing anything changes everything.” Technical debt, referring to the compounding cost of changes to software architecture, can be especially challenging in machine learning systems. D. Sculley is a software engineer at Google, focusing on machine learning, data mining, and information retrieval. He recently co-authored the paper Machine Learning: The High
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33 min
November 17, 2017
Training the Machines with Russell Smith
Automation is changing the labor market. To automate a task, someone needs to put in the work to describe the task correctly to a computer. For some tasks, the reward for automating a task is tremendous–for example, putting together mobile phones. In China, companies like FOXCONN are investing time and money into programming the instructions
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60 min
October 18, 2017
Model Training with Yufeng Guo
Machine learning models can be built by plotting points in space and optimizing a function based off of those points. For example, I can plot every person in the United States in a 3 dimensional space: age, geographic location, and yearly salary. Then I can draw a function that minimizes the distance between my function
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49 min
September 29, 2017
Sports Deep Learning with Yu-Han Chang and Jeff Su
A basketball game gives off endless amounts of data. Cameras from all angles capture the players making their way around the court, dribbling, passing, and shooting. With computer vision, a computer can build a well-defined understanding for what a sport looks like. With other machine learning techniques, the computer can make predictions by combining historical
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58 min
September 19, 2017
Deep Learning Systems with Milena Marinova
The applications that demand deep learning range from self-driving cars to healthcare, but the way that models are developed and trained is similar. A model is trained in the cloud and deployed to a device. The device engages with the real world, gathering more data. That data is sent back to the cloud, where it
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54 min
September 15, 2017
Visual Search with Neel Vadoothker
If I have a picture of a dog, and I want to search the Internet for pictures that look like that dog, how can I do that? I need to make an algorithm to build an index of all the pictures on the Internet. That index can define the different features of my images. I
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54 min
September 13, 2017
Word2Vec with Adrian Colyer
Machines understand the world through mathematical representations. In order to train a machine learning model, we need to describe everything in terms of numbers.  Images, words, and sounds are too abstract for a computer. But a series of numbers is a representation that we can all agree on, whether we are a computer or a
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61 min
September 5, 2017
Artificial Intelligence APIs with Simon Chan
Software companies that have been around for a decade have a ton of data. Modern machine learning techniques are able to turn that data into extremely useful models. Salesforce users have been entering petabytes of data into the company’s CRM tool since 1999. With its Einstein suite of products, Salesforce is using that data to
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56 min
September 1, 2017
Healthcare AI with Cosima Gretton
Automation will make healthcare more efficient and less prone to error. Today, machine learning is already being used to diagnose diabetic retinopathy and improve radiology accuracy. Someday, an AI assistant will assist a doctor in working through a complicated differential diagnosis. Our hospitals look roughly the same today as they did ten years ago, because
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49 min
August 22, 2017
Similarity Search with Jeff Johnson
Querying a search index for objects similar to a given object is a common problem. A user who has just read a great news article might want to read articles similar to it. A user who has just taken a picture of a dog might want to search for dog photos similar to it. In
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59 min
July 28, 2017
Self-Driving Deep Learning with Lex Fridman
Self-driving cars are here. Fully autonomous systems like Waymo are being piloted in less complex circumstances. Human-in-the-loop systems like Tesla Autopilot navigate drivers when it is safe to do so, and lets the human take control in ambiguous circumstances. Computers are great at memorization, but not yet great at reasoning. We cannot enumerate to a
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59 min
June 29, 2017
Instacart Data Science with Jeremy Stanley
Instacart is a grocery delivery service. Customers log onto the website or mobile app and pick their groceries. Shoppers at the store get those groceries off the shelves. Drivers pick up the groceries and drive them to the customer. This is an infinitely complex set of logistics problems, paired with a rich data set given
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60 min
June 14, 2017
Distributed Deep Learning with Will Constable
Deep learning allows engineers to build models that can make decisions based on training data. These models improve over time using stochastic gradient descent. When a model gets big enough, the training must be broken up across multiple machines. Two strategies for doing this are “model parallelism” which divides the model across machines and “data
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57 min
June 5, 2017
Video Object Segmentation with the DAVIS Challenge Team
Video object segmentation allows computer vision to identify objects as they move through space in a video. The DAVIS challenge is a contest among machine learning researchers working off of a shared dataset of annotated videos. The organizers of the DAVIS challenge join the show today to explain how video object segmentation models are trained
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53 min
May 12, 2017
Poker Artificial Intelligence with Noam Brown
Humans have now been defeated by computers at heads up no-limit holdem poker. Some people thought this wouldn’t be possible. Sure, we can teach a computer to beat a human at Go or Chess. Those games have a smaller decision space. There is no hidden information. There is no bluffing. Poker must be different! It
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55 min
May 10, 2017
Convolutional Neural Networks with Matt Zeiler
Convolutional neural networks are a machine learning tool that uses layers of convolution and pooling to process and classify inputs. CNNs are useful for identifying objects in images and video. In this episode, we focus on the application of convolutional neural networks to image and video recognition and classification. Matt Zeiler is the CEO of
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54 min
May 1, 2017
Google Brain Music Generation with Doug Eck
Most popular music today uses a computer as the central instrument. A single musician is often selecting the instruments, programming the drum loops, composing the melodies, and mixing the track to get the right overall atmosphere. With so much work to do on each song, popular musicians need to simplify–the result is that pop music
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46 min
April 3, 2017
Hedge Fund Artificial Intelligence with Xander Dunn
A hedge fund is a collection of investors that make bets on the future. The “hedge” refers to the fact that the investors often try to diversify their strategies so that the direction of their bets are less correlated, and they can be successful in a variety of future scenarios. Engineering-focused hedge funds have used
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58 min
March 21, 2017
Multiagent Systems with Peter Stone
Multiagent systems involve the interaction of autonomous agents that may be acting independently or in collaboration with each other. Examples of these systems include financial markets, robot soccer matches, and automated warehouses. Today’s guest Peter Stone is a professor of computer science who specializies in multiagent systems and robotics. In this episode, we discuss some
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45 min
March 20, 2017
Biological Machine Learning with Jason Knight
Biology research is complex. The sample size of a biological data set is often too small to make confident judgments about the biological system being studied. During Jason Knight’s PhD research, the RNA sequence data that he was studying was not significant enough to make strong conclusions about the gene regulatory networks he was trying
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65 min
March 17, 2017
Stripe Machine Learning with Michael Manapat
Every company that deals with payments deals with fraud. The question is not whether fraud will occur on your system, but rather how much of it you can detect and prevent. If a payments company flags too many transactions as fraudulent, then real transactions might accidentally get flagged as well. But if you don’t reject
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57 min
February 16, 2017
Machine Learning is Hard with Zayd Enam
Machine learning frameworks like Torch and TensorFlow have made the job of a machine learning engineer much easier. But machine learning is still hard. Debugging a machine learning model is a slow, messy process. A bug in a machine learning model does not always mean a complete failure. Your model could continue to deliver usable
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54 min
February 10, 2017
Deep Learning with Adam Gibson
Deep learning uses neural networks to identify patterns. Neural networks allow us to sequence “layers” of computing, with each layer using learning algorithms such as unsupervised learning, supervised learning, and reinforcement learning. Deep learning has taken off in the last few years, but it has been around for much longer. Adam Gibson founded Skymind, the
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50 min
February 9, 2017
Go Data Science with Daniel Whitenack
Data science is typically done by engineers writing code in Python, R, or another scripting language. Lots of engineers know these languages, and their ecosystems have great library support. But these languages have some issues around deployment, reproducibility, and other areas. The programming language Golang presents an appealing alternative for data scientists. Daniel Whitenack transitioned
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61 min
January 25, 2017
Translation with Vasco Pedro
Translation is a classic problem in computer science. How do you translate a sentence from one human language into another? This seems like a problem that computers are well-suited to solve. Languages follow well-defined rules, we have lots of sample data to train our machine learning models. And yet, the problem has not been solved–largely
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56 min
January 17, 2017
Medical Machine Learning with Razik Yousfi and Leo Grady
Medical imaging is used to understand what is going on inside the human body and prescribe treatment. With new image processing and machine learning techniques, the traditional medical imaging techniques such as CT scans can be enriched to get a more sophisticated diagnosis. HeartFlow uses data from a standard CT scan to model a human
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57 min
January 16, 2017
Python Data Visualization with Jake VanderPlas
Data visualization tools are required to translate the findings of data scientists into charts, graphs, and pictures. Understanding how to utilize these tools and display data is necessary for a data scientist to communicate with people in other domains. In this episode, Srini Kadamati hosts a discussion with Jake VanderPlas about the Python ecosystem for
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48 min
October 17, 2016
PANCAKE STACK Data Engineering with Chris Fregly
Data engineering is the software engineering that enables data scientists to work effectively. In today’s episode, we explore the different sides of data engineering–the data science algorithms that need to be processed and the implementation of software architectures that enable those algorithms to run smoothly. The PANCAKE STACK is a 12-letter acronym that Chris Fregly
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58 min
September 27, 2016
Scikit-learn with Andreas Mueller
Scikit-learn is a set of machine learning tools in Python that provides easy-to-use interfaces for building predictive models. In a previous episode with Per Harald Borgen about Machine Learning For Sales, he illustrated how easy it is to get up and running and productive with scikit-learn, even if you are not a machine learning expert.
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34 min
September 2, 2016
Music Deep Learning with Feynman Liang
Machine learning can be used to generate music. In the case of Feynman Liang’s research project BachBot, the machine learning model is seeded with the music of famous composer Bach. The music that BachBot creates sounds remarkably similar to Bach, although it has been generated by an algorithm, not by a human.   BachBot is
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46 min
September 1, 2016
Automated Content with Robbie Allen
You have probably read a news article that was written by a machine. When earnings reports come out, or a series of sports events like the Olympics occurs, there are so many small stories that need to be written that a news organization like the Associated Press would have to use all of its resources
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50 min
August 29, 2016
Artificial Intelligence with Oren Etzioni
Research in artificial intelligence takes place mostly at universities and large corporations, but both of these types of institutions have constraints that cause the research to proceed a certain way. In a university, basic research might be hindered by lack of funding. At a big corporation, the researcher might be encouraged to study a domain
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64 min
August 18, 2016
TensorFlow in Practice with Rajat Monga
TensorFlow is Google’s open source machine learning library. Rajat Monga is the engineering director for TensorFlow. In this episode, we cover how to use TensorFlow, including an example of how to build a machine learning model to identify whether a picture contains a cat or not. TensorFlow was built with the mission of simplifying the
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44 min
August 17, 2016
Data Validation with Dan Morris
Data Validation is the process of ensuring that data is accurate. In many software domains, an application is pulling in large quantities of data from external sources. That data will eventually be exposed to users, and it needs to be correct. Radius Intelligence is a company that aggregates data on small businesses. In order to
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42 min
August 16, 2016
Machine Learning for Sales with Per Harald Borgen
Machine learning has become simplified. Similar to how Ruby on Rails made web development approachable, scikit-learn takes away much of the frustrating aspects of machine learning, and lets the developer focus on building functionality with high-level APIs.   Per Harald Borgen is a developer at Xeneta. He started programming fairly recently, but has already built
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45 min
June 8, 2016
Phone Spam with Truecaller CTO Umut Alp
The war against spam has been going on for decades. Email spam blockers and ad blockers help protect us from unwanted messages in our communication and browsing experience. These spam prevention tools are powered by machine learning, which catches most of the emails and ads that we don’t want to see. TrueCaller is a company
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56 min
March 8, 2016
Machine Learning in Healthcare with David Kale
“Building a model to predict disease and deploying that in the wild – the bar for success is much higher there than, say, deciding what ad to show you.” Diagnosing illness today requires the trained eye of a doctor. With machine learning, we might someday be able to diagnose illness using only a data set.
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59 min
February 29, 2016
Data Science at Monsanto with Tim Williamson
“Nothing’s cool unless you call it ‘as a service.’ ” Monsanto is a company that is known for its chemical and biological engineering. It is less well known for its data science and software engineering teams. Tim Williamson is a data scientist at Monsanto, and on today’s show he talked about how he and a
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57 min
January 29, 2016
Deep Learning and Keras with François Chollet
“I definitely think we can try to abstract away the first principles of intelligence and then try to go from these principles to an intelligent machine that might look nothing like the brain.” Continue reading…
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55 min
January 19, 2016
Machine Learning for Businesses with Joshua Bloom
“You’ve got software engineers who are interested in machine learning, and think what they need to do is just bring in another module and then that will solve their problem. It’s particularly important for those people to understand that this is a different type of beast.” Continue reading…
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57 min
December 15, 2015
TensorFlow with Greg Corrado
“You don’t mind if failures slow things down, but its very important that failures do not stop forward progress.” Continue reading…
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41 min
December 11, 2015
Data Science at Spotify with Boxun Zhang
“I normally try to sit together or very close to a product team or engineering team. And by doing so, I get very close to the source of all kinds of challenging problems.” Continue reading…
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57 min
December 8, 2015
Learning Machines with Richard Golden
“When I was a graduate student, I was sitting in the office of my advisor in electrical engineering and he said, ‘Look out that window – you see a Volkswagon, I see a realization of a random variable.’ ” Continue reading…
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56 min
November 17, 2015
Machine Learning and Technical Debt with D. Sculley
“Changing anything changes everything.” Technical debt, referring to the compounding cost of changes to software architecture, can be especially challenging in machine learning systems. Continue reading…
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33 min
October 5, 2015
Bridging Data Science and Engineering with Greg Lamp
Current infrastructure makes it difficult for data scientists to share analytical models with the software engineers who need to integrate them. Yhat is an enterprise software company tackling the challenge of how data science gets done. Their products enable companies and users to easily deploy data science environments and translate analytical models into production code. Continue reading…
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47 min
October 3, 2015
Kaggle with Ben Hamner
Data science competitions are an effective way to crowdsource the best solutions for challenging datasets. Kaggle is a platform for data scientists to collaborate and compete on machine learning problems with the opportunity to win money from the competitions' sponsors. Continue reading…
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49 min
September 30, 2015
Teaching Data Science with Vik Paruchuri
There is a need for more data scientists to make sense of the vast amounts of data we produce and store. Dataquest is an in-browser platform for learning data science that is tackling this problem. Vik Paruchuri is the founder of Dataquest. He was previously a machine learning engineer at EdX and before that a U.S. diplomat. Continue reading…
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44 min
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