Book Image

Hands-On Machine Learning on Google Cloud Platform

By : Giuseppe Ciaburro, V Kishore Ayyadevara, Alexis Perrier
Book Image

Hands-On Machine Learning on Google Cloud Platform

By: Giuseppe Ciaburro, V Kishore Ayyadevara, Alexis Perrier

Overview of this book

Google Cloud Machine Learning Engine combines the services of Google Cloud Platform with the power and flexibility of TensorFlow. With this book, you will not only learn to build and train different complexities of machine learning models at scale but also host them in the cloud to make predictions. This book is focused on making the most of the Google Machine Learning Platform for large datasets and complex problems. You will learn from scratch how to create powerful machine learning based applications for a wide variety of problems by leveraging different data services from the Google Cloud Platform. Applications include NLP, Speech to text, Reinforcement learning, Time series, recommender systems, image classification, video content inference and many other. We will implement a wide variety of deep learning use cases and also make extensive use of data related services comprising the Google Cloud Platform ecosystem such as Firebase, Storage APIs, Datalab and so forth. This will enable you to integrate Machine Learning and data processing features into your web and mobile applications. By the end of this book, you will know the main difficulties that you may encounter and get appropriate strategies to overcome these difficulties and build efficient systems.
Table of Contents (18 chapters)
Creating ML Applications with Firebase

Preventing Overfitting with Regularization

So far, in the previous chapters, we understood about building neural network, evaluating the TensorBoard results, and varying the hyperparameters of the neural network model to improve the accuracy of the model.

While the hyperparameters in general help with improving the accuracy of model, certain configuration of hyperparameters results in the model overfitting to the training data, while not generalizing for testing data is the problem of overfitting to the training data.

A key parameter that can help us in avoiding overfitting while generalizing on an unseen dataset is the regularization technique. Some of the key regularization techniques are as follows:

  • L2 regularization
  • L1 regularization
  • Dropout
  • Scaling
  • Batch normalization
  • Weight initialization

In this chapter, we will go through the following:

  • Intuition of over/under fitting...