Indy.Code() Sessions tagged machine-learning

Deep Learning For Folks Without (or With!) a Ph.D.

How does a computer identify pictures of cats? What about translating speech from one language to another? Or drive a car? Creating lifelike photos of people who don’t exist? These are jobs for deep learning. Using neural networks, deep learning is a specialization of machine learning with a predisposition for perceptual tasks requiring lots of data. If this sounds hard, it’s because it is! However, the Python community has pitched in and offered two libraries which are the focus of this talk. Keras is an high level conceptual API which allows developers to focus on the why of deep learning projects instead of the how. For those who need more control, or to invent something new, TensorFlow is a lower-level library which is also a backend for Keras. Between the two there is something for everyone available in the exciting new world of deep learning.

Speaker

Douglas Starnes

Douglas Starnes

Author, Speaker, Independent

Microsoft Azure Makes Machine Learning Affordable and Accessible

It’s one of the hottest topics in software development that is not a buzzword. It’s for real. It’s also really tough. So how can anyone who needs machine learning get started? That’s where Microsoft Azure comes in. Offering a collections of tools that you can get started with for free, Azure makes it possible to perform machine learning tasks with little or no code involved. Microsoft Azure Machine Learning Studio and Workbench give novices and seasoned users a complete toolchain and workflow for a wide variety of machine learning projects. From data ingestion to operationalization via web services, developers and data professionals can leverage the power of predictive engines without a Ph.D. And since it’s opinion-free, supporting current and open source technologies such as Python, R and TensorFlow, you can integrate Azure ML with existing projects. In addition to the free starter tiers, it’s cost effective when you are ready to upgrade because everything is in the Azure cloud and pay for what you use. And you might not need to build anything at all. Azure Cognitive Services provides APIs that let you integrate vision, speech recognition into your app, today! Why buy a supercomputer when you can rent one? You don’t need a data center or a staff of eggheads, just Azure Machine Learning.

Speaker

Douglas Starnes

Douglas Starnes

Author, Speaker, Independent

The AI Engineer: A Foot in Two Worlds

Artificial Intelligence. Machine Learning. Data Science. These have been the hot buzzwords for the past several years. And us developers? We've been following this new technology like we always do. But this time it's not just a new programming language or JavaScript framework. AI is different.

The world of a developer is about code. Code that defines the behavior of some process. But the world of a data scientist is about data. Data that describes the behavior of some process.

An AI Engineer has a foot in each of these worlds.

If you'd like to have a foot in both of these worlds too, this talk will get you started. In it, I'll share what an AI Engineer is and isn't, steps you can take to begin your journey, and some common pitfalls to avoid. When we're done, you'll have a solid idea of exactly how you, as a developer, can fit into the world of data science, how to close the gaps to get there, and what you need to call yourself an AI Engineer.

Speaker

Guy Royse

Guy Royse

Developer Evangelist, DataRobot