Technical interviews about software topics.
https://softwareengineeringdaily.com/
Gesamtlänge aller Episoden: 59 days 13 hours 25 minutes
recommended podcasts
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,
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.
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.
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.
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.
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,
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.
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,
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.
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.