Hands-On Keras for Machine Learning Engineers

Description

This course is your guide to deep learning in Python with Keras. You will discover the Keras Python library for deep learning and learn how to use it to develop and evaluate deep learning models.

Welcome to hands-on Keras for machine learning engineers. This is a carefully structured course to guide you in your journey to learn deep learning in Python with Keras. Discover the Keras Python library for deep learning and learn the process of developing and evaluating deep learning models using it. There are two top numerical platforms for developing deep learning models; they are Theano, developed by the University of Montreal, and TensorFlow developed at Google. Both were developed for use in Python and both can be leveraged by the super-simple-to-use Keras library. Keras wraps the numerical computing complexity of Theano and TensorFlow, providing a concise API that we will use to develop our own neural network and deep learning models. Keras has become the gold standard in the applied space for rapid prototyping deep learning models. This course is a hands-on guide. It is a playbook and a workbook intended for you to learn by doing and then apply your new understanding to your own deep learning Keras models. All resources and code files for this course are placed here: https://github.com/PacktPublishing/Hands-On-Keras-for-Machine-Learning-Engineers

Develop and evaluate neural network models end-to-end
Build larger models for image and text data
Understand the anatomy of a Keras model
Evaluate the performance of a deep learning Keras model
Build end-to-end regression and classification models in Keras
Learn how to use checkpointing to save the best model run

This course is for developers, machine learning engineers, and data scientists that want to learn how to get the most out of Keras. You do not need to be a machine learning expert, but it would be helpful if you knew how to navigate a small machine learning problem using SciKit-Learn. Basic concepts such as cross-validation and one-hot encoding used in lessons and projects are described, but only briefly. With all of this in mind, this is an entry-level course on the Keras library.

The course follows a hands-on approach towards learning. To get the most out of the course, it is recommended to work through all the examples in each tutorial.

Learn how to use more advanced techniques required to develop state-of-the-art deep learning models * Learn how to use advanced image augmentation techniques in order to lift model performance * Learn how to enhance performance with learning rate schedules

https://github.com/PacktPublishing/Hands-On-Keras-for-Machine-Learning-Engineers

Mike West is the founder of LogikBot. He has worked with databases for over two decades. He has worked for or consulted with over 50 different companies as a full-time employee or consultant. These were Fortune 500 as well as several small to mid-size companies. Some include Georgia Pacific, SunTrust, Reed Construction Data, Building Systems Design, NetCertainty, The Home Shopping Network, SwingVote, Atlanta Gas and Light, and Northrup Grumman. Over the last five years, Mike has transitioned to the exciting world of applied machine learning. He is excited to show you what he has learned and help you move into one of the single-most important fields in this space.

1. Introduction

2. Foundations

3. Going Deeper with Keras

4. Convolutional Neural Networks

5. Recurrent Neural Networks