Artificial Intelligence - AI5


AI-5: Deep Learning, Development, and Operations (DLOps)

Your journey in Deep Learning has lead you here. You are now and expert at building everything from computer vision to language to generative to reinforcement models. Those awesome models you trained now sit in notebook files right? What if you want to give life 🌱 to this model? Maybe build an App 😁

This brings us to AI5!

Welcome to AI5.

This course aims to review existing Deep Learning flow while applying it to a real-world problem. Then we will build and deploy an application that uses the deep learning model to understand how to operationalize models. This course follows the Univ.AI model of balancing between concept, theory, and implementation.

Split into three parts; the course starts with the review of Deep Learning models and how to apply them to different model tasks, including vision and language tasks. The next part will be Development, where you use the models you trained in part 1 and incorporate them into real-world applications. Finally, you will Deploy the application in Google Cloud Platform (GCP). The three parts will cover in detail topics such as Transfer learning, Containerization using Docker, and Scaling deployments using Kubernetes.

At the end of this module, you will build efficient deep learning models and design, build and deploy applications that scale.

This page introduces you to the team, the basic instructions, the schedule, and various elements of our class.

Interested in joining?

If you would like to apply to this course, please go here.

We also provide this course as part of our Masters and Accelerated program, check this link out to get more information and apply.

The Team

Dr. Pavlos Protopapas

You can read more about him here.

Shivas Jayaram

You can read more about him here.

The Coursework

We have very carefully designed the coursework to give you, the student, a wholesome learning experience.

Each week shall include:

Session - What to expect

Before the session begins, students are expected to complete a pre-class reading assignment and and attempt a quiz based on the same.

A session will have the following pedagogy layout which will be repeated three times:

After the session, students are expected to complete a short post-class quiz based on the principal concepts covered in class.

Lab - What to expect

A lab is a TA driven one hour session that is divided into 3 major parts.

The Class

Welcome Session - Preparing for this class

There will be a Welcome Session scheduled on Monday, 2nd August 2021 at 7:30 PM IST for all registered students. Please check your mail for more information.

High level course schedule

NOTE: Below timings are in IST

Lecture Sessions:

Lab Sessions:

Office hours:

Course Pre-Requisites

Your are expected to have a working knowledge of python, along with a good understanding on the Tensorflow Deep Learning framework.

Install Docker

Install Docker Desktop for your operating system.

Install VSCode

Follow the instructions for your operating system.
If you already have a preferred text editor, skip this step.

TODO: All exercises in this course will be done in …

TODO: Before you begin the course, we have prepared for you a simple exercise to ensure your proficieny of the above libraries.

TODO: This will help you assess your preparedness for the course, and will also help you familiarize yourself with the platform.

Diversity & Inclusion

We actively seek and welcome people of diverse identities, from across the spectrum of disciplines and methods since Artificial Intelligence (AI) increasingly mediates our social, cultural, economic, and political interactions [1].

We believe in creating and maintaining an inclusive learning environment where all members feel safe, respected, and capable of producing their best work.

We commit to an experience for all participants that is free from – Harassment, bullying, and discrimination which includes but is not limited to:

Reference:

[1] K. Stathoulopoulos and J. C. Mateos-Garcia, “Gender Diversity in AI Research,” SSRN Electronic Journal, 2019 [Online]. Available: http://dx.doi.org/10.2139/ssrn.3428240.

Logistics - What you need to begin?

We assume you have a Univ.AI account, created when you signed up at course.univ.ai. If not, email programs@univ.ai.

Education software we use

TODO: All exercises and homeworks in this course will be done in jupyter notebooks. This link will help you setup jupyter lab and get you acquianted with jupyter notebooks.

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