Book Image

Mastering Machine Learning on AWS

By : Dr. Saket S.R. Mengle, Maximo Gurmendez
Book Image

Mastering Machine Learning on AWS

By: Dr. Saket S.R. Mengle, Maximo Gurmendez

Overview of this book

Amazon Web Services (AWS) is constantly driving new innovations that empower data scientists to explore a variety of machine learning (ML) cloud services. This book is your comprehensive reference for learning and implementing advanced ML algorithms in AWS cloud. As you go through the chapters, you’ll gain insights into how these algorithms can be trained, tuned, and deployed in AWS using Apache Spark on Elastic Map Reduce (EMR), SageMaker, and TensorFlow. While you focus on algorithms such as XGBoost, linear models, factorization machines, and deep nets, the book will also provide you with an overview of AWS as well as detailed practical applications that will help you solve real-world problems. Every application includes a series of companion notebooks with all the necessary code to run on AWS. In the next few chapters, you will learn to use SageMaker and EMR Notebooks to perform a range of tasks, right from smart analytics and predictive modeling through to sentiment analysis. By the end of this book, you will be equipped with the skills you need to effectively handle machine learning projects and implement and evaluate algorithms on AWS.
Table of Contents (24 chapters)
Free Chapter
1
Section 1: Machine Learning on AWS
3
Section 2: Implementing Machine Learning Algorithms at Scale on AWS
9
Section 3: Deep Learning
13
Section 4: Integrating Ready-Made AWS Machine Learning Services
17
Section 5: Optimizing and Deploying Models through AWS
Appendix: Getting Started with AWS

Creating a custom neural net with TensorFlow

In the previous section, Training and serving the TensorFlow model through SageMaker, we used the high-level library of TensorFlow to construct a regression model using LinearRegressor. In this section, we will demonstrate how we can construct an actual neural network using the Keras library from TensorFlow. Keras facilitates the design of neural networks by hiding some of the complexity behind the core (that is, low-level) TensorFlow library.

In this chapter, we will use the ubiquitous MNIST dataset that consists of a series of images of handwritten digits along with their real labels (that is, values between 0 and 1). The MNIST dataset can be downloaded from https://www.kaggle.com/c/digit-recognizer/data.

The dataset comes as a CSV file with 784 columns corresponding to each of the pixels in the 28 x 28 image. The values for each...