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  5. Getting Started With Deep Learning: Improving Performance

Exercise 2

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Catalit
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Overview
DifficultyBeginner
Duration1h 2m
Students42

Description

Move on from what you learned from studying the principles of recurrent neural networks, and how they can solve problems involving sequencing, with this cohesive course on Improving Performace. Learn to improve the performance of your neural networks by starting with learning curves that allow you to answer the right questions. This could be needing more data or, even, building a better model to improve your performance.

Further into the course, you will explore the fundamentals around bash normalization, drop-out, and regularization.

This course also touches on data augmentation and its ability to allow you to build new data from your starting training data, culminating in hyper-parameter optimization. This is a tool to that aids in helping you to decide how to tune the external parameters of your network.

This course is made up of 13 lectures and three accompanying exercises. This Cloud Academy course is in collaboration with Catalit.

 Learning Objectives

  • Learn how to improve the performance of your neural networks.
  • Learn the skills necessary to make executive decisions when working with neural networks.

Intended Audience

Transcript

Hey guys, welcome back. In this video, we're going to review exercise two in section nine. In exercise two, you are asked to reload the digits as we did before, and define a function that is repeated training regularization dropout that adds the regularization and dropout to a fully connected network. So basically, like we did for batch normalization in class, we're going to do for regularization and dropout, comparing the performance with and without regularization with and without dropout. And the question is, do we get a better performance? Pretty simple, again, it's a minor modification of the code that it's about. So good luck and thank you for watching.

About the Author

Students736
Courses8
Learning paths3

I am a Data Science consultant and trainer. With Catalit I help companies acquire skills and knowledge in data science and harness machine learning and deep learning to reach their goals. With Data Weekends I train people in machine learning, deep learning and big data analytics. I served as lead instructor in Data Science at General Assembly and The Data Incubator and I was Chief Data Officer and co-­founder at Spire, a Y-Combinator-­backed startup that invented the first consumer wearable device capable of continuously tracking respiration and activity. I earned a joint PhD in biophysics at University of Padua and Université de Paris VI and graduated from Singularity University summer program of 2011.