Understanding the Building Blocks of Deep Learning using PyTorch

The popularity of AI and Deep Learning has risen several folds in the last few years. PyTorch has played a very important role for deep learning engineers and Data scientists in adopting and coming up with innovations in the space of AI. In the hack session we will go through some of the key components of PyTorch that power you to build modern DL projects.

Overview of the hack Session:
  1. High level view of different phases of a Deep learning project.

  2. Detailed walk through of each of the building blocks.

  3. Build a simple image classifier.

  4. Show how to use transfer learning with modern architectures.

  5. Tips and tricks for training neural network faster.

  6. Modify existing architectures to integrate techniques from some recent research papers.

Key Takeaways
  1. How to use PyTorch for your deep learning projects.

  2. Transfer learning with modern network architectures like ResNet.

  3. Implement Squeeze and Excitation networks.

  4. Write neural networks in a clean and readable manner.


Vishnu Subramanian

Vishnu Subramanian works as a Director of AI at Jiva Adventures, Principal Data scientist at Affine, author of book titled “Deep learning using PyTorch”. He has experience in leading, architecting and implementing several Big Data analytical projects (AI, ML and Deep Learning). He recently achieved the 21st position in the TGS Salt Identification challenge on Kaggle.

Duration of Hack-Session: 1 hour

Buy Ticket

Leave a Reply

Your email address will not be published. Required fields are marked *

Social media & sharing icons powered by UltimatelySocial
Download Brochure

Download Brochure