Deep Brain AI

"Neural networks are nature's way of implementing optimization algorithms. It's nothing but maximizing information gain in terms of compressing what's similar and contrasting what's different, while minimizing resources, say (or coding length). Hence a game." --- Yi Ma, UC Berkeley professor

In any data science project or research, don't get over exited about tech or jump into coding part too early, before understanding the problem and requirements well.

Do the following (your steps maybe different depending on requirements):

In research:

-- Understand the problem well.
-- Formulate research questions and hypotheses
-- Get the data ready, do necessary ETL, feature engineering, etc.
-- Implement the ideas to solve the problems
-- Experiment to validate hypotheses
-- Disseminate your findings (conference/journals/workshops).

In business:

-- Understand the business context very well and requirements (in a collaborative setting with client) thoroughly
-- Analyse customers requirements and communicate them to cross-check
-- Get the data ready, do necessary ETL, feature engineering, etc.
-- Dive into coding or implementation, predictive modelling, tuning, evaluation
-- Build the first prototype, send it to customers, and ask for their feedback
-- Address their comments and improve the prototype
-- Get feedback on the improved version
-- Iterate the process until they're happy
-- Communicate the models and business insights in the form of customer-centric story telling.
-- Take the prototype into production, scale up, deploy/deliver.