Following up last post Deep Learning Note 4 — Error Analysis, this post focuses on dealing with mismatched data distributions between training and dev/test sets. Deep learning hungers for a lot of training

Following up last post Deep Learning Note 3 — Comparing to Human Level Performance on the Machine Learning Strategy, the next two posts cover the error analysis and how to handle mismatch training and

Observations We have an ElasticSearch cluster that is running fine most of time. However, occasionally our users can submit heavy queries that add a lot of load to the cluster by accident. They typically

Following up last post Deep Learning Note 3 — Machine Learning Strategy of Setting up the Learning Target on the Machine Learning Strategy, this post covers the last 3 points on how to work on a machine

This week’s course Machine Learning Strategy mainly focuses on how to work on a machine learning project and accelerate the project iteration. Since this course covers quite a lot small topics, I’ll break

In my last post Deep Learning Note 2 — Part 1 Regularization I summarized some key points about regularization methods to improve deep neural networks. In this week I will summarize another two topics:

In my last post Deep Learning Note1--Neural Networks and Deep Learning I summarized some key points that I found useful from the Coursera course Neural Networks and Deep Learning. In this week I started

Introduction I recently signed up for the Deep Learning Specialization on Cousera and have just completed the first course Neural Networks and Deep Learning. Although it is recommended for 4 weeks of study,

Hello World! Or Hello Steemit :) My name is Jason. I'm a software engineer, a photographer and a not-so-good-yet cook. Self introduction is never easy to me as I don't know what and how much I should share.