Chi / Larissa Face Detection #9 – Cutting Cloud Costs with Infrastructure Automation (Part IV – Preparing and Training Code on AWS with Drop Out – MNIST)

I said in the last post that I was ready to try this on our face. I’m totally not ready – I’m just missing one last thing I wanted to try. TFlearn gives us the option of easily including drop out in our fully connected layers of the NN, and I wanted to explore that […]

Read More Chi / Larissa Face Detection #9 – Cutting Cloud Costs with Infrastructure Automation (Part IV – Preparing and Training Code on AWS with Drop Out – MNIST)

Chi / Larissa Face Detection #8 – Cutting Cloud Costs with Infrastructure Automation (Part III – Preparing and Training Code on AWS – MNIST)

Review At this point, we have a working EC2 instance loaded with the AWS Deep Learning AMI, with the jupyter notebook tested and ready to go. The last step I want to take to automate this analysis as much as I can is to just get a notebook ready so I can just import it […]

Read More Chi / Larissa Face Detection #8 – Cutting Cloud Costs with Infrastructure Automation (Part III – Preparing and Training Code on AWS – MNIST)

Chi / Larissa Face Detection #7 – Cutting Cloud Costs with Infrastructure Automation (Part II – EC2 Configuration Automation)

Review In the last post, to put it short, I spun up and environment that could’ve taken up to a half a year to procure, set up, and configure in my past life. We worked with networking, security, computing, and storage components all in one script! At the end of the day, I have an […]

Read More Chi / Larissa Face Detection #7 – Cutting Cloud Costs with Infrastructure Automation (Part II – EC2 Configuration Automation)