Book Image

Scala Machine Learning Projects

Book Image

Scala Machine Learning Projects

Overview of this book

Machine learning has had a huge impact on academia and industry by turning data into actionable information. Scala has seen a steady rise in adoption over the past few years, especially in the fields of data science and analytics. This book is for data scientists, data engineers, and deep learning enthusiasts who have a background in complex numerical computing and want to know more hands-on machine learning application development. If you're well versed in machine learning concepts and want to expand your knowledge by delving into the practical implementation of these concepts using the power of Scala, then this book is what you need! Through 11 end-to-end projects, you will be acquainted with popular machine learning libraries such as Spark ML, H2O, DeepLearning4j, and MXNet. At the end, you will be able to use numerical computing and functional programming to carry out complex numerical tasks to develop, build, and deploy research or commercial projects in a production-ready environment.
Table of Contents (17 chapters)
Title Page
Packt Upsell
Contributors
Preface
Index

CNN architecture


In multilayer networks, such as MLP or DBN, the outputs of all neurons of the input layer are connected to each neuron in the hidden layer, so the output will again act as the input to the fully-connected layer. In CNN networks, the connection scheme that defines the convolutional layer is significantly different. The convolutional layer is the main type of layer in CNN, where each neuron is connected to a certain region of the input area called the receptive field.

In a typical CNN architecture, a few convolutional layers are connected in a cascade style, where each layer is followed by a rectified linear unit (ReLU) layer, then a pooling layer, then a few more convolutional layers (+ReLU), then another pooling layer, and so on.

The output from each convolution layer is a set of objects called feature maps that are generated by a single kernel filter. The feature maps can then be used to define a new input to the next layer. Each neuron in a CNN network produces an output...