Machine Learning – Software Engineering Daily

By Machine Learning – Software Engineering Daily

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Machine learning and data science episodes of Software Engineering Daily.

Episode Date
Digital Evolution with Joel Lehman, Dusan Misevic, and Jeff Clune
57:50

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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Jun 15, 2018
Future of Computing with John Hennessy
1:01:44

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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Jun 07, 2018
OpenAI: Compute and Safety with Dario Amodei
1:03:13

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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Jun 04, 2018
Voice with Rita Singh
1:02:59

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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May 21, 2018
Machine Learning with Data Skeptic and Second Spectrum at Telesign
1:10:00

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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May 19, 2018
Deep Learning Topologies with Yinyin Liu
1:00:05

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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May 10, 2018
Keybase Architecture / Clarifai Infrastructure Meetup Talks
1:12:50

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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Apr 28, 2018
TensorFlow Applications with Rajat Monga
56:40

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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Apr 26, 2018
Scale Self-Driving with Alexandr Wang
49:52

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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Feb 27, 2018
Machine Learning Deployments with Kinnary Jangla
47:12

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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Feb 14, 2018
Deep Learning Hardware with Xin Wang
57:44

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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Jan 29, 2018
Edge Deep Learning with Aran Khanna
57:36

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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Jan 26, 2018
Machine Learning and Technical Debt with D. Sculley Holiday Repeat
33:41

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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Dec 25, 2017
Training the Machines with Russell Smith
1:00:10

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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Nov 17, 2017
Model Training with Yufeng Guo
49:17

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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Oct 18, 2017
Sports Deep Learning with Yu-Han Chang and Jeff Su
58:15

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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Sep 29, 2017
Deep Learning Systems with Milena Marinova
54:48

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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Sep 19, 2017
Visual Search with Neel Vadoothker
54:18

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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Sep 15, 2017
Word2Vec with Adrian Colyer
1:01:47

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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Sep 13, 2017
Artificial Intelligence APIs with Simon Chan
56:40

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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Sep 05, 2017
Healthcare AI with Cosima Gretton
49:06

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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Sep 01, 2017
Similarity Search with Jeff Johnson
59:30

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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Aug 22, 2017
Self-Driving Deep Learning with Lex Fridman
59:05

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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Jul 28, 2017
Instacart Data Science with Jeremy Stanley
1:00:08

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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Jun 29, 2017
Distributed Deep Learning with Will Constable
57:29

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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Jun 14, 2017
Video Object Segmentation with the DAVIS Challenge Team
53:15

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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Jun 05, 2017
Poker Artificial Intelligence with Noam Brown
55:39

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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May 12, 2017
Convolutional Neural Networks with Matt Zeiler
54:37

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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May 10, 2017
Google Brain Music Generation with Doug Eck
46:26

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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May 01, 2017
Hedge Fund Artificial Intelligence with Xander Dunn
58:05

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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Apr 03, 2017
Multiagent Systems with Peter Stone
45:44

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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Mar 21, 2017
Biological Machine Learning with Jason Knight
1:05:41

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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Mar 20, 2017
Stripe Machine Learning with Michael Manapat
57:37

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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Mar 17, 2017
Machine Learning is Hard with Zayd Enam
54:34

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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Feb 16, 2017
Deep Learning with Adam Gibson
50:56

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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Feb 10, 2017
Go Data Science with Daniel Whitenack
1:01:08

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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Feb 09, 2017
Translation with Vasco Pedro
56:25

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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Jan 25, 2017
Medical Machine Learning with Razik Yousfi and Leo Grady
57:22

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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Jan 17, 2017
Python Data Visualization with Jake VanderPlas
48:24

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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Jan 16, 2017
PANCAKE STACK Data Engineering with Chris Fregly
58:24

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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Oct 17, 2016
Scikit-learn with Andreas Mueller
34:07

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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Sep 27, 2016
Music Deep Learning with Feynman Liang
46:35

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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Sep 02, 2016
Automated Content with Robbie Allen
50:54

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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Sep 01, 2016
Artificial Intelligence with Oren Etzioni
1:04:16

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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Aug 29, 2016
TensorFlow in Practice with Rajat Monga
44:58

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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Aug 18, 2016
Data Validation with Dan Morris
42:41

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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Aug 17, 2016
Machine Learning for Sales with Per Harald Borgen
45:14

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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Aug 16, 2016
Phone Spam with Truecaller CTO Umut Alp
56:02

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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Jun 08, 2016
Machine Learning in Healthcare with David Kale
59:59

“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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Mar 08, 2016
Data Science at Monsanto with Tim Williamson
57:26

“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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Feb 29, 2016
Deep Learning and Keras with François Chollet
55:25

“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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Jan 29, 2016
Machine Learning for Businesses with Joshua Bloom
57:55

“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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Jan 19, 2016
TensorFlow with Greg Corrado
41:54

“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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Dec 15, 2015
Data Science at Spotify with Boxun Zhang
57:35

“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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Dec 11, 2015
Learning Machines with Richard Golden
56:16

“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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Dec 08, 2015
Machine Learning and Technical Debt with D. Sculley
33:07

“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…

The post Machine Learning and Technical Debt with D. Sculley appeared first on Software Engineering Daily.

Nov 17, 2015
Bridging Data Science and Engineering with Greg Lamp
47:15

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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Oct 05, 2015
Kaggle with Ben Hamner
49:53

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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Oct 03, 2015
Teaching Data Science with Vik Paruchuri
44:43

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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Sep 30, 2015