How to Demonstrate Your Basic Skills with Deep Learning
Last Updated on August 6, 2019
Skills in deep learning are in great demand, although these skills can be challenging to identify and to demonstrate.
Explaining that you are familiar with a technique or type of problem is very different to being able to use it effectively with open source APIs on real datasets.
Perhaps the most effective way of demonstrating skill as a deep learning practitioner is by developing models. A practitioner can practice on standard publicly available machine learning datasets and build up a portfolio of completed projects to both leverage on future projects and to demonstrate competence.
In this post, you will discover how you can use small projects to demonstrate basic competence for using deep learning for predictive modeling.
After reading this post, you will know:
- Explaining deep learning math, theory, and methods is not sufficient to demonstrate competence.
- Developing a portfolio of completed small projects allows you to demonstrate your ability to develop and deliver skillful models.
- Using a systematic five-step project template to execute projects and a nine-step template for presenting results allows you to both methodically complete projects and clearly communicate findings.
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