Software Engineering Daily

Technical interviews about software topics.

Eine durchschnittliche Folge dieses Podcasts dauert 55m. Bisher sind 1598 Folge(n) erschienen. Jeden Tag erscheint eine Folge dieses Podcasts.

Gesamtlänge aller Episoden: 59 days 13 hours 25 minutes


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Technically Sentient with Rob May

The impact of artificial intelligence on our everyday lives will be so profound that our modern institutions will change completely. Employment, government, romance, social norms–all of these things will be upended. To see the signs of this coming,


 2017-02-20  53m

Where Machines Go to Learn with Auren Hoffman

If you wanted to build a machine learning model to understand human health, where would you get the data? A hospital database would be useful, but privacy laws make it difficult to disclose that patient data to the public.


 2017-02-17  56m

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.


 2017-02-16  54m

Data Applications With Dave King

Data scientists need flexible interfaces for displaying and manipulating data sets. Data engineers need to be able to visualize how their data pipelines wire together databases and data processing frameworks.


 2017-02-15  1h4m

Service Proxying with Matt Klein

Most tech companies are moving toward a highly distributed microservices architecture. In this architecture, services are decoupled from each other and communicate with a common service language, often JSON over HTTP.


 2017-02-14  56m

Infrastructure with Datanauts’ Chris Wahl and Ethan Banks

Infrastructure is a term that can mean many different things: your physical computer, the data center of your Amazon EC2 cluster, the virtualization layer, the container layer–on and on. In today’s episode,


 2017-02-13  49m

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.


 2017-02-10  50m

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,


 2017-02-09  1h1m

Engineering Management with Mike Borozdin

Engineering managers face a different set of problems than engineers themselves. Whether they are hiring new employees, firing underperformers, or guiding a team of existing engineers, engineering management is all about people.


 2017-02-08  1h4m

Open Source Contribution with Shubheksha Jalan

Open source software is publicly available code that is worked on in the open by large crowds of developers. Almost all new software today uses some open source software in its code. But most people never contribute to open source themselves.


 2017-02-07  48m