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Machine Learning in iOS - Live Tutorial Session - RWDevCon 2017

677 views · Published 17 October 2018 · 1:24:15 · Indexed 1 October 2026

Channel: Kodeco · 2018 · Education

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Machine Learning. Convolutional Neural Networks. Deep Learning Neural Networks. What is all the hype about? What are these technologies, what are they good for, and can we use them for anything useful right now? This session requires no background in any of these areas, and will introduce you to machine learning on iOS with a worked example.

Download course materials here:

https://store.raywenderlich.com/downloads/812

Watch the full course here:

https://store.raywenderlich.com/products/rwdevcon-2017-vault-bundle

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https://www.raywenderlich.com/384-reactive-programming-with-rxandroid-in-kotlin-an-introduction

raywenderlich.com is a website focused on developing high quality programming tutorials. Our goal is to take the coolest and most challenging topics and make them easy for everyone to learn – so we can all make amazing apps.

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From Wikipedia:
https://en.wikipedia.org/wiki/Machine_learning

Machine learning is a field of artificial intelligence that uses statistical techniques to give computer systems the ability to "learn" (e.g., progressively improve performance on a specific task) from data, without being explicitly programmed.

The name machine learning was coined in 1959 by Arthur Samuel. Machine learning explores the study and construction of algorithms that can learn from and make predictions on data – such algorithms overcome following strictly static program instructions by making data-driven predictions or decisions,:2 through building a model from sample inputs. Machine learning is employed in a range of computing tasks where designing and programming explicit algorithms with good performance is difficult or infeasible; example applications include email filtering, detection of network intruders, and computer vision.

Machine learning is closely related to (and often overlaps with) computational statistics, which also focuses on prediction-making through the use of computers. It has strong ties to mathematical optimization, which delivers methods, theory and application domains to the field. Machine learning is sometimes conflated with data mining,[5] where the latter subfield focuses more on exploratory data analysis and is known as unsupervised learning.

Within the field of data analytics, machine learning is a method used to devise complex models and algorithms that lend themselves to prediction; in commercial use, this is known as predictive analytics. These analytical models allow researchers, data scientists, engineers, and analysts to "produce reliable, repeatable decisions and results" and uncover "hidden insights" through learning from historical relationships and trends in the data

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