Book Details
Title: Machine Learning By Tutorials Ray Wenderlich
ISBN-10: 942123347
Author: By Matthijs Hollemans, Chris LaPollo and Audrey Tam
Publisher: Razeware LLC
Language: English
Subject: Swift / Computers & Technology / Programming / Apple Programming
Format: PDF, EPUB, Full Source code
Recently I bought a set of 10 books advanced IOS and Swift bundle by Ray Wenderlich. As you can see in the image above, which includes Machine Learning By Tutorials Ray Wenderlich Swift 4.2 and IOS 12. And now I want to transfer it to you for $ 60 (10 books), All books are the latest version that supports swift 4.2 and have full source code. I will share it for you for $ 60 Includes PDF, EPUB file and full source code, you can download on Google Drive.
List books:
1, Advanced Apple Debugging
2, Server Side Swift with Vapor
3, Push Notifications by Tutorials
4, Arkit books
5, Data Structures and Algorithms in Swift
6, Realm Building Modern Swift Apps with Realm
7, RxSwift Reactive Programming with Swift
8, Metal by Tutorials
9, Machine Learning by Tutorials
10, Advanced iOS App Architecture
Please contact me by Email: truonghang0207@gmail.com.
You can see the full description 10 books at http://www.prograbooks.com/2018/05/advanced-swift-bundle-by-ray-wenderlich.html
Thank you.
What is machine learning?
Machine learning is hot and exciting — but it's not exactly new. Many companies have been routinely employing machine learning as part of their daily business for several decades already. Google, perhaps the quintessential machine-learning company, was founded in 1998 when Larry Page invented PageRank, now considered to be a classic machine-learning algorithm.
But machine learning goes even further back, all the way to the early days of modern computers. In 1959, Arthur Samuel defined machine learning as the field of study that gives computers the ability to learn without being explicitly programmed.
In fact, the most basic machine-learning algorithm of them all, linear regression or the "method of least squares," was invented over 200 years ago by famed mathematician Carl Friedrich Gauss. That's approximately one-and-a-half centuries before there were computers... even before electricity was common. This simple algorithm is still used today and is the foundation of more complex methods such as logistic regression and even neural networks — all algorithms that you'll learn about in this book.
Even deep learning, which had its big breakthrough moment in 2012 when a so-called convolutional neural network overwhelmingly won the ImageNet Large Scale Visual Recognition Challenge, is based on the ideas of artificial neural networks dating back to the work of McCulloch and Pitts in the early 1940s when people started to wonder if it would be possible to make computers that worked like the human brain.
So, yes, machine learning has been around for a while. But that doesn't mean you've missed the boat. On the contrary, the reason it's become such a hot topic recently is that machine learning works best when there is a lot of data — thanks to the internet and smartphones, there is now more data than ever. Moreover, computing power has become much cheaper. It took a while for it to catch on, but machine learning has grown into a practical tool for solving real-world problems that were too complex to deal with before.
What is new, and why we've written this book, is that mobile devices are now powerful enough to run machine-learning algorithms right in the palm of your hand!
Machine learning is hot and exciting — but it's not exactly new. Many companies have been routinely employing machine learning as part of their daily business for several decades already. Google, perhaps the quintessential machine-learning company, was founded in 1998 when Larry Page invented PageRank, now considered to be a classic machine-learning algorithm.
But machine learning goes even further back, all the way to the early days of modern computers. In 1959, Arthur Samuel defined machine learning as the field of study that gives computers the ability to learn without being explicitly programmed.
In fact, the most basic machine-learning algorithm of them all, linear regression or the "method of least squares," was invented over 200 years ago by famed mathematician Carl Friedrich Gauss. That's approximately one-and-a-half centuries before there were computers... even before electricity was common. This simple algorithm is still used today and is the foundation of more complex methods such as logistic regression and even neural networks — all algorithms that you'll learn about in this book.
Even deep learning, which had its big breakthrough moment in 2012 when a so-called convolutional neural network overwhelmingly won the ImageNet Large Scale Visual Recognition Challenge, is based on the ideas of artificial neural networks dating back to the work of McCulloch and Pitts in the early 1940s when people started to wonder if it would be possible to make computers that worked like the human brain.
So, yes, machine learning has been around for a while. But that doesn't mean you've missed the boat. On the contrary, the reason it's become such a hot topic recently is that machine learning works best when there is a lot of data — thanks to the internet and smartphones, there is now more data than ever. Moreover, computing power has become much cheaper. It took a while for it to catch on, but machine learning has grown into a practical tool for solving real-world problems that were too complex to deal with before.
What is new, and why we've written this book, is that mobile devices are now powerful enough to run machine-learning algorithms right in the palm of your hand!
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