Embeddings Continued
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 lesson 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 lesson, you will explore the fundamentals around bash normalization, drop-out, and regularization.
This lesson 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 lesson is made up of 13 lectures and three accompanying exercises. This Cloud Academy lesson 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
- It is recommended to complete the Introduction to Data and Machine Learning lesson before starting.
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.