Essential Python Recipes for Deep Learning
Aakash N S
This coding-focused talk offers some practical tips and advice that can help beginners be more efficient, organized and productive while working on deep learning projects and participating in data science competitions. The topics covered include working with the file system, effecting processing CSV files, doing exploratory data analysis, creating a baseline model for evaluation, and easily working with image data.
- How to work with the filesystem
- How to work with CSV files
- How to do exploratory data analysis (EDA)
- How to create a baseline model for evaluation
- How to display images in a grid
- How to create a Kaggle submission file
- Basic knowledge of Python programming
- Basic knowledge of Machine Learning and Kaggle
I’m the co-founder and CEO of swiftace.ai , an early stage startup building productivity and collaboration tools for machine learning. Our team has won 8 data science competitions and hackathons in India in the past 2 years. Prior to starting SwiftAce, I have worked as software engineer at Twitter in Ireland & San Francisco. He graduated from IIT Bombay in 2013. He’s also an avid blogger and open source contributor.