About the 2019 edition:
The schedule for the 2019 edition is published here: https://hasgeek.com/anthillinside/2019/schedule
The conference has three tracks:
- Talks in the main conference hall track
- Poster sessions featuring novel ideas and projects in the poster session track
- Birds of Feather (BOF) sessions for practitioners who want to use the Anthill Inside forum to discuss:
- Myths and realities of labelling datasets for Deep Learning.
- Practical experience with using Knowledge Graphs for different use cases.
- Interpretability and its application in different contexts; challenges with GDPR and intepreting datasets.
- Pros and cons of using custom and open source tooling for AI/DL/ML.
Who should attend Anthill Inside:
Anthill Inside is a platform for:
- Data scientists
- AI, DL and ML engineers
- Cloud providers
- Companies which make tooling for AI, ML and Deep Learning
- Companies working with NLP and Computer Vision who want to share their work and learnings with the community
For inquiries about tickets and sponsorships, call Anthill Inside on 7676332020 or write to email@example.com
Sponsorship slots for Anthill Inside 2019 are open. Click here to view the sponsorship deck.
Anthill Inside 2019 sponsors:
Tensorboard: Almost a one stop shop for Machine Learning Development
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.
- What are the free options.
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.