Our Community

Join our Deep Learning Community, where we aim to bring about a change and improve our understanding of DNN.

We'll all be sharing experiences, knowledge, techniques, and support each other with a goal of helping data-scientists alike make sense of neural network development.

Current Development Paradigm

A brief overview of the process of neural network development: how we got to where we're at, how it is today, and how it could be improved to help data scientists build high-performing models they can trust.

Guided Error Analysis

How can we utilize model feature extraction to build meaningful latent spaces and apply advanced explainability techniques to understand model weaknesses and gain insights on how to fix them.

Dataset Architecture

How to design better datasets by removing redundant and irrelevant information yet find which samples are needed to achieve the most practical and generally distributed dataset.

Deep Unit Testing and Collaboration

How do we improve DNN testing using advanced techniques to find scenarios that should be tested, and how it will improve the dev cycle, team collaboration, and responsiveness.