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Lab Overview

By Charles Frye. Published July 25, 2022.

What are these labs for?

In the lab portion of Full Stack Deep Learning 2022, we will incrementally develop a complete codebase to train a deep neural network to recognize characters in hand-written paragraphs and deploy it inside a simple web application.

These labs act as an opportunity to work through the nitty-gritty details that come up when implementing some of the recommendations given in the lectures in a concrete system. It's also a chance for you to gain familiarity with some of the tools we recommend in the lectures.

This lab reviews the overall architecture of the system.

Architecture of the Text Recognizer

Software architectures are inherently about trade-offs: decisions that make for better scaling might make for worse security or tools that encourage faster iteration might reduce transparency.

We design our architecture with agility and simplicity as the prime directives. We choose simplicity in order to empower individuals to understand the "full stack" of the application, from GPUs crunching tensors in model development up to serverless cloud functions acting on requests in production. And we choose agility so that individual is able to quickly iterate on the application, especially in response to user feedback.

Architecture Diagram

We put together a handy architecture diagram summarizing the application here:

For a guided tour of this architecture, watch the video at the top of the page or click the badge below to open an interactive Jupyter notebook on Google Colab:

Running the labs

One-click setup on Colab

To make it as easy as possible to run the labs, we've made them compatible with Google Colab.

Wherever you see an "Open in Colab" badge, like the one below, just click on it and you'll be dropped into a hosted notebook environment for the lab, complete with free GPU. The badge below opens the first main-track lab, Lab 4 on experiment management.

You can read more here.

Setup on your own Linux machine

If you have a Linux machine with an NVIDIA GPU and drivers, either locally or in the cloud, you can also run the labs there. The video above and text instructions here should be enough to get you going.

Don't get stuck on setup!

Remember that Google Colab is always there as an option if you run into issues while setting up.

Rather than getting frustrated with some obnoxious library linking or driver issue that's irrelevant to the material you are really trying to learn and getting stuck in an installation quagmire, just run the labs on Colab so you can get back to learning about machine learning!

We are excited to share this course with you for free.

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