This is the code repository for Automated Machine Learning with AutoKeras, published by Packt.
Deep learning made accessible for everyone with just few lines of coding
AutoKeras is a very simple and popular open source AutoML framework that provides easy access to deep learning models. This book will help you to explore the basics of automated machine learning using practical examples, enabling you to create and use your own models in your company or project.
This book covers the following exciting features:
- Set up a deep learning workstation with TensorFlow and AutoKeras
- Automate a machine learning pipeline with AutoKeras
- Create and implement image and text classifiers and regressors using AutoKeras
- Use AutoKeras to perform sentiment analysis of a text, classifying it as negative or positive
- Leverage AutoKeras to classify documents by topics If you feel this book is for you, get your copy today!
All of the code is organized into folders. For example, Chapter02.
The code will look like the following:
import autokeras as ak
import matplotlib.pyplot as plt
import numpy as np
import tensorflow as tf
from tensorflow.keras.datasets import mnist
Following is what you need for this book: AutoKeras is an AutoML open-source software library that provides easy access to deep learning models. If you are looking to build deep learning model architectures and perform parameter tuning automatically using AutoKeras, then this book is for you.
With the following software and hardware list you can run all code files present in the book (Chapter 1-10).
Chapter | Software required | OS required |
---|---|---|
1 - 10 | Web Browser | Windows, Mac OS X, and Linux (Any) |
We also provide a PDF file that has color images of the screenshots/diagrams used in this book. Click here to download it.
Luis Sobrecueva is a senior software engineer and ML/DL practitioner currently working at Cabify. He has been a contributor to the OpenAI project as well as one of the contributors to the AutoKeras project.
If you have already purchased a print or Kindle version of this book, you can get a DRM-free PDF version at no cost.
Simply click on the link to claim your free PDF.