Machine Learning Street Talk (MLST)

Sayak Paul

Jul 17, 2020

Sayak Paul, a prominent figure in deep learning and Google Developer Expert, shares insights from his vibrant career in machine learning. He discusses the AI landscape in India and the nuances of unsupervised representation learning. The conversation dives into data augmentation and contrastive learning techniques, emphasizing their importance in performance improvement. Sayak further explores the complexities of explainability and interpretability in AI, suggesting ethical responsibilities for engineers. The talk wraps up with advanced topics on pruning and the lottery ticket hypothesis in neural networks.

01:36:16

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Episode guests

Sayak Paul

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Episode notes

Intro

4min

Navigating Machine Learning Success

19min

Unpacking Data Augmentation in Machine Learning

25min

Innovations in Data Augmentation

10min

Understanding Machine Learning Interpretability

25min

Optimizing Neural Networks: Techniques and Tools

7min

Exploring Fine-Tuning and Parameter Significance in Transfer Learning

2min

Exploring the Lottery Ticket Hypothesis in Neural Networks

2min

Enhancing Knowledge Distillation

3min

This week we spoke with Sayak Paul, who is extremely active in the machine learning community. We discussed the AI landscape in India, unsupervised representation learning, data augmentation and contrastive learning, explainability, abstract scene representations and finally pruning and the recent super positions paper. I really enjoyed this conversation and I hope you folks do too!

00:00:00 Intro to Sayak

00:17:50 AI landscape in India

00:24:20 Unsupervised representation learning

00:26:11 DATA AUGMENTATION/Contrastive learning

00:59:20 EXPLAINABILITY

01:12:10 ABSTRACT SCENE REPRESENTATIONS

01:14:50 PRUNING and super position paper

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