Book Image

Large Scale Machine Learning with Python

By : Bastiaan Sjardin, Alberto Boschetti
Book Image

Large Scale Machine Learning with Python

By: Bastiaan Sjardin, Alberto Boschetti

Overview of this book

Large Python machine learning projects involve new problems associated with specialized machine learning architectures and designs that many data scientists have yet to tackle. But finding algorithms and designing and building platforms that deal with large sets of data is a growing need. Data scientists have to manage and maintain increasingly complex data projects, and with the rise of big data comes an increasing demand for computational and algorithmic efficiency. Large Scale Machine Learning with Python uncovers a new wave of machine learning algorithms that meet scalability demands together with a high predictive accuracy. Dive into scalable machine learning and the three forms of scalability. Speed up algorithms that can be used on a desktop computer with tips on parallelization and memory allocation. Get to grips with new algorithms that are specifically designed for large projects and can handle bigger files, and learn about machine learning in big data environments. We will also cover the most effective machine learning techniques on a map reduce framework in Hadoop and Spark in Python.
Table of Contents (17 chapters)
Large Scale Machine Learning with Python
About the Authors
About the Reviewer

CNN's with an incremental approach

Now that we have a decent understanding of the architectures of CNNs, let's get our hands dirty in Keras and apply a CNN.

For this example, we will use the famous CIFAR-10 face image dataset, which is conveniently available within the Keras domain. The dataset consists of 60,000, 32 x 32 color images with 10 target classes consisting of an airplane, automobile, bird, cat, deer, dog, frog, horse, ship, and truck. This is a smaller dataset than the one that was used for the AlexNet example. For more information, you can refer to

In this CNN, we will use the following architecture to classify the image according to the 10 classes that we specified:

input->convolution 1 (32,3,3)->convolution 2(32,3,3)->pooling->dropout -> Output (Fully connected layer and softmax)