Keras Tips, Tricks, and Techniques | Packt


Keras Tips, Tricks, and Techniques | Packt
English | Size: 3.01 GB
Genre: eLearning

Learn
Run deep learning models with Keras and a TensorFlow backend
Use image augmentation to improve training accuracy for your Keras models
Learn how to generate articles with Recurrent Neural Networks in Keras
Use Keras for Natural Language Processing
Deploy Keras models into production
Use the Keras Functional API for deep learning
About
Machine learning and deep learning allow us to interpret data structures and fit that data into models to identify patterns and make predictions. Keras makes this easier with its huge set of libraries that can be easily used for machine learning.

Designed for those with some existing Python and Keras skills and familiarity with machine learning principles, this course will enable you to enrich your skills by covering a number of more advanced applications. In this course, you will get hands-on experience in solving real problems by implementing cutting-edge techniques to significantly boost your Keras skills and, as a consequence, expand your ability to apply Keras to real-world problems. Throughout the course, you will work on real datasets to increase your expertise and keep adding new tools to your Keras toolbox.

By the end of this course, you will learn various tips, tricks, and techniques to upgrade your machine learning and deep learning algorithm knowledge, as well as how to build efficient models with Keras.

By the end of this course, you will have learned various tips, tricks, and techniques to upgrade your machine learning and deep learning algorithm knowledge; you will also be able to build efficient models with Keras.

All the code and supporting files for this course are available on GitHub at

https://github.com/PacktPublishing/Keras-Tips-Tricks-and-Techniques

Features
Explore how Keras can help with complex real-world challenges
Hands-guide to building an end-to-end deep learning solution using Keras
Tips and tricks to get the most out of using Keras for various machine learning problems

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