How to track your Machine Learning Experiments Effectively
Sanyam Bhutani (~init27) |
The usual pipeline for working on a machine learning experiment is very different from Software Engineering. This talk will be highlights of Tracking the experiments and the iterative nature of the same effect inside of a Jupyter notebook, how to effectively apply these ideas to Kaggle competitions and make these work with data science teams.
- Familiarity with Python
- Familiarity with Jupyter notebook
- Some Experience with Building Machine Learning models.
I'm happy to record and upload a quick screencast if that is required along with the Jupyter notebooks.
Sanyam is a Machine Learning and Computer Vision practitioner, recognized by media such as inc42 or Economic Times. Sanyam is a Kaggle Triple Expert (ranked top 1% in all categories), He is also an active blogger on Medium, which recognizes him as a "Top Writer in Artificial Intelligence".
Sanyam has done various research and industrial internships based on Deep Learning applications at the Indian Institute of Technology Madras, the Indian Institute of Technology Roorkee, ONGC, and Tech-Mahindra. He has a background in Computer Science and is an active contributor to multiple Machine Learning communities: Fastai, TWIMLAI, DS India, AISaturdays, and Kaggle-Noobs.
Twitter: https://twitter.com/bhutanisanyam1 Kaggle: https://www.kaggle.com/init27/ Blog: https://medium.com/@init_27 Linkedin: linkedin.com/in/sanyambhutani/