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Machine Learning with Swift

Machine Learning with Swift

By : Alexander Sosnovshchenko , Jojo Moolayil, Oleksandr Baiev
3 (1)
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Machine Learning with Swift

Machine Learning with Swift

3 (1)
By: Alexander Sosnovshchenko , Jojo Moolayil, Oleksandr Baiev

Overview of this book

Machine learning as a field promises to bring increased intelligence to the software by helping us learn and analyse information efficiently and discover certain patterns that humans cannot. This book will be your guide as you embark on an exciting journey in machine learning using the popular Swift language. We’ll start with machine learning basics in the first part of the book to develop a lasting intuition about fundamental machine learning concepts. We explore various supervised and unsupervised statistical learning techniques and how to implement them in Swift, while the third section walks you through deep learning techniques with the help of typical real-world cases. In the last section, we will dive into some hard core topics such as model compression, GPU acceleration and provide some recommendations to avoid common mistakes during machine learning application development. By the end of the book, you'll be able to develop intelligent applications written in Swift that can learn for themselves.
Table of Contents (14 chapters)
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Training the network


Stochastic gradient descent (SGD) is an effective way of training deep neural networks. SGD seeks such parameters Θ of the network, which minimize the loss function .

Where 

 is a training dataset.

Training happens in steps. At every step, we choose a subset of our training set of size m (mini-batch) and use it to approximate loss function gradient with respect to parameters Θ:

Mini-batch training advantages are as follows:

  • Gradient of the loss function over a mini-batch is a better approximation of the gradient over the whole training set then calculated over only one sample
  • Thanks to the GPU you can perform computations in parallel on every sample in the batch, which is faster, then processing them one-by-one

 

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