arrow_back Hands on Deep Learning for Computer Vision – Techniques for Image Segmentation.(6 hours workshop).
Tensorboard: Almost a one stop shop for Machine Learning Development
Submitted by Tushar Pawar (@tuuushaar) on Sunday, 28 April 2019
Section: Full talk Technical level: Beginner Session type: Lecture
This talk will focus on the areas of Machine Learning development (specifically Computer Vision problems) and will discuss how using certain tools will make your life easier. Also, discuss some areas where developing custom tools is beneficial instead of using open-source tools and the trade-off for making this choice.
We will discuss why we should be using Tensorboard and why it is the only tool you will need to do machine learning development. We will see how it compares to other model development tools such as Visdom, Netron and several other Proprietary(and expensive) tools.
We will also see some small snippets of examples of what is possible with Tensorboard and how to interpret machine learning models, monitor training stats, debug models and compare experiments. This includes using tensorboard as an interactive front-end for monitoring the models and visualising its performance on test set and how we can make it to be framework agnostic and work with Pytorch, Keras and not be dependent on tensorflow.
1. Understand the features available in Tensorboard and why it is the only tool that you will ever need.
2. How to make it work with any machine learning framework.
- Addressing the problems faced while developing machine learning models using just terminal for interface.
- How some companies have leveraged this pain into making a paid service for monitoring model training.
- Why tensorboard is better than any other paid service.
- Showcase of in-house tools built at Infilect.
Tushar Pawar is Machine Learning Engineer at Infilect. He has around 3 years of experience in the field of Deep Learning. Has worked with several computer vision problems such as image classification, object detection, image generation etc.