Lesson series
Problem Definition in AI
A dream of Deep Learning is to solve complex multi-stage problems with a single architecture. While possible, it may often not make sense to do so. We show why & present ways of slicing & dicing a problem into smaller ones for solving and combining.
Prerequisite
The Hero Methods Course (Free)
Type
Self-paced
Starts at
Your convenience
Format
Discussion-based
Content Duration
90 minutes
Price
$
199
Problem Definition is your weapon.
Better understand your domain, relate to your audience and lead to consensus.
Ignore it at your peril. We show you how to do it right!
Ignore it at your peril. We show you how to do it right!
#1 Hero vs. Non Hero
Unlike classic software builds, for problems in AI, the solution is not always known. Much experimentation is needed to surface the answer.
#2 Common Ground
Regardless of which, you are never going to solve it right, if you don't know what you're solving. Problem Definition is the first thing you'll need to do.
#3 Maven of Modelling
Problem, not solution. You'll learn about it from stakeholders. You then decompose it into sub-problems - in different ways. The end goal - Modelling.
#4 Shepherd Stakeholders
Then, teach stakeholders the many dimensions of an AI problem. Show them how and why you decomposed yours, and get their feedback. Earn buy-in & consensus.
Not the usual Machine Learning course!
Write your awesome label here.
Design Thinking
Ponder different ways in which a top-level problem can be structured, and solutions woven together in an architecture
Write your awesome label here.
Hands-free
No hands-on exercises, Jupyter Notebooks or datasets here
Write your awesome label here.
Discussion-based
Open-ended questions, discussions and deep thinking are best for learning, and are our primary method
Write your awesome label here.
Peer Learning
Interacting with peers and learning from their experience is better than exams, quizzes or certificates.
Meet the INSTRUCTOR
Jagannath Rajagopal
I teach Design Thinking & Architecture for a whole universe of methods of which Machine Learning is but a part. Between high level use case content and hands-on coding, there exists a knowledge gap today. At Kado, my team of overachievers, and I have melted our brains and put down everything we know about Hero Methods towards a knowledge offering that is different from the usual run-of-the-mill Machine Learning course. Our goal is to bridge this gap.
I've wrestled Tensorflow to the ground, learned Python from scratch in the process, crashed and burned in 2 start-ups, launched a successful YouTube channel, failed at selling Scuba gear, taught R to many, and succeeded several times in implementing large scale Supply Chain Planning systems for big companies.
I've wrestled Tensorflow to the ground, learned Python from scratch in the process, crashed and burned in 2 start-ups, launched a successful YouTube channel, failed at selling Scuba gear, taught R to many, and succeeded several times in implementing large scale Supply Chain Planning systems for big companies.
Write your awesome label here.
Created with
