What this book covers
Chapter 1, First Steps to Scalability, sets the problem of scalable machine learning under the right perspective and familiarizes you with the tools that we will be using in this book.
Chapter 2, Scalable Learning in Scikit-learn, discusses strategies for stochastic gradient descent (SGD) where we mitigate memory consumption; it is based on the theme of out-of-core learning. We will also deal with data preparation techniques that can deal with a variety of data, such as the hashing trick.
Chapter 3, Fast-Learning SVMs, covers streaming algorithms that are capable of discovering non-linearity in the form of support vector machines. We will present alternatives to Scikit-learn, such as LIBLINEAR and Vowpal Wabbit, which, although operating as external shell commands, can be easily wrapped and directed by Python scripts.
Chapter 4, Neural Networks and Deep Learning, provides useful tactics for applying deep neural networks within the Theano framework together with large-scale applications with H2O. Even though it is a hot topic, it can be quite a challenge to apply it successfully, let alone provide scalable solutions. We will also resort to unsupervised pre-training with autoencoders with the theanets package.
Chapter 5, Deep Learning with TensorFlow, covers interesting deep learning techniques together with an online method for neural networks. Although TensorFlow is only in its infancy, the framework provides elegant machine learning solutions. We will also utilize Keras Convolutional Neural Networks capabilities within the TensorFlow environment.
Chapter 6, Classification and Regression Trees at Scale, explains scalable solutions for random forest, gradient boosting, and XGboost. CART, an acronym for classification and regression trees, is a machine learning method usually applied in the framework of ensemble methods. We will also provide examples of a large-scale application using H2O.
Chapter 7, Unsupervised Learning at Scale, dives into unsupervised learning, as we will cover PCA, cluster analysis, and topic modeling using the right approach for scaling them up.
Chapter 8, Distributed Environments – Hadoop and Spark, teaches us how to set up Spark within a virtual machine environment, shifting from a single machine to a computational network paradigm. As Python can easily glue and power up our efforts on a cluster of machines, it becomes a piece of cake to leverage the power of a Hadoop cluster.
Chapter 9, Practical Machine Learning with Spark, gets into action with Spark, teaching all the essentials for starting immediately to manipulate data and build predictive models on large datasets.
Appendix, Introduction to GPUs and Theano, will cover the basics of Theano and GPU-computation. It will help you install and prepare your environment for using Theano on the GPU, if your system allows it.
