Tag: machine learning
Episode 444: Tug Grall on Redis
Tug Grall of Redis Labs discusses Redis, its evolution over the years, and emerging use cases today. Host Akshay Manchale spoke with Tug about Redis’ fundamental data structures and their common uses, its module based ecosystem and Redis’ applicability in a wide range of applications beyond being a layer for caching data such as search, […]
Episode 395: Katharine Jarmul on Security and Privacy in Machine Learning
Katharine Jarmul of DropoutLabs discusses security and privacy concerns as they relate to Machine Learning. Host Justin Beyer spoke with Jarmul about attacks that can be leveraged against data pipelines and machine learning models; attack types – adversarial example, model inference, deanonymization; and how they can be utilized to manipulate model outcomes; the dangers of […]
Episode 391: Jeremy Howard on Deep Learning and fast.ai
Jeremy Howard from fast.ai explains deep learning from concept to implementation. With transfer learning, individuals and small organizations can quickly get to work on machine learning problems using the open source fastai library and desktop graphics hardware. Jeremy and host Nate Black discuss neural network architecture and deep learning models, using pre-trained models from a […]
SE Radio Episode 318: Veronika Cheplygina on Image Recognition
Felienne interviews Veronika Cheplygina about image recognition. The discussion covers: what exactly constitutes image recognition, including categorizing and segmentation problems; fields where image recognition are currently being applied, including medicine, self-driving cars and security, and future applications. The host and guest also cover how to obtain enough and good datasets, and some of the common […]
SE-Radio Episode 315: Jeroen Janssens on Tools for Data Science
Felienne interviews Jeroen Janssens on Data Science. They examine what data science is exactly, particularly exploring how it differs from machine learning and statistics. The episode further considers what skills people need to be great data scientists – skills that are related but not always equal to the skills that programming and software engineering require. […]
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