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

Understanding convolutional neural networks

In this section, we will take a look at a variant of DNNs, where the structure of the network is modified for image recognition tasks.

In the neural networks that we've discussed so far in this chapter, we've seen that all the input layers are one-dimensional. However, images are two-dimensional. To capture how images are fed to a neural network for training, we have to modify the structure of the input layer. Traditional algorithms require humans to label the edges of the objects in the image. Convolutional neural networks (CNNs) can automatically detect the objects in the image with enough training and, based on the labels of the image, they can learn how to identify objects in the images without explicitly labeling the edges in the image.

CNNs require a preprocessing phase, where the image has to be prepared into a specific...