Improve your deep learning skills with Keras tips, tricks, and techniques
This is the code repository for Keras Tips, Tricks and Techniques[Video], published by Packt. It contains all the supporting project files necessary to work through the video course from start to finish.
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.
- 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
This course is for aspiring data science professionals and machine learning practitioners who are familiar with basic Python programming and Keras.
This course has the following requirements:
Some familiarity with Python and Keras
Machine learning background
Desire to get hands-on and learning
Jupyter Notebook