Book Image

Python Deep Learning Cookbook

By : Indra den Bakker
Book Image

Python Deep Learning Cookbook

By: Indra den Bakker

Overview of this book

Deep Learning is revolutionizing a wide range of industries. For many applications, deep learning has proven to outperform humans by making faster and more accurate predictions. This book provides a top-down and bottom-up approach to demonstrate deep learning solutions to real-world problems in different areas. These applications include Computer Vision, Natural Language Processing, Time Series, and Robotics. The Python Deep Learning Cookbook presents technical solutions to the issues presented, along with a detailed explanation of the solutions. Furthermore, a discussion on corresponding pros and cons of implementing the proposed solution using one of the popular frameworks like TensorFlow, PyTorch, Keras and CNTK is provided. The book includes recipes that are related to the basic concepts of neural networks. All techniques s, as well as classical networks topologies. The main purpose of this book is to provide Python programmers a detailed list of recipes to apply deep learning to common and not-so-common scenarios.
Table of Contents (21 chapters)
Title Page
About the Author
About the Reviewer
Customer Feedback

Launching an instance on Google Cloud Platform (GCP)

Another popular cloud provider is Google. Its Google Cloud Platform (GCP) is getting more and has as a major benefit—it includes a newer GPU type, NVIDIA P100, with 16 GB of GPU memory. In this recipe, we provide the steps to launch a GPU-enabled compute machine.

Getting ready

Before with this recipe, you should be with GCP and its cost structure.

How to do it...

  1. You need to request an increase in the GPU quota before you launch a compute instance with a GPU for the first time. Go to
  2. First, select the project you want to use and apply the Metric and Region filters accordingly. The GPU instances should show up as follows:

Figure 1.1: Google Cloud Platform dashboard for increasing the GPU quotas

  1. Select the quota you want to change, click on EDIT QUOTAS, and follow the steps.
  2. You will get an e-mail confirmation when your quota has been increased.
  3. Afterwards, you can create a GPU-enabled machine.
  4. When launching a machine, make sure you tick the Allow HTTP traffic and Allow HTTPs traffic boxes if you want to use a Jupyter notebook.