GIVEAWAY INFORMATION:  

Thanks to O'Reilly and the authors, we are giving away 5 copies of the Practical Natural Language Processing book.   

Giveaway tweet: [TBD at 7:30PM IST]  

To participate in the giveaway, retweet and comment about your favourite part of the conversation with the #practicalnlp hashtag. The winners will be selected and notified on October 1, 2020. To be updated about the results, subscribe to the Youtube channel and follow me on twitter https://twitter.com/mkulkhanna.  

You can also get a 30-day free trial from the O'Reilly website by using the promo code PNLP20 or the link below. 

Link: https://learning.oreilly.com/get-learning/?code=PNLP20  

Episode Introduction:  

This is the 10th episode of the podcast and a really special one. I've got the authors of the Practical Natural Language Processing book. The book is a comprehensive guide to building, iterating and scaling real world NLP Systems. It is for anyone who is involved in any way in building NLP systems in industry - from software engineers to data scientists to ML engineers to product managers and business leaders. The book is already topping the charts on Amazon and has been endorsed by various experts from academia and industry.  

Episode Overview:  

So for this episode, I talk to the authors of the book - Sowmya, Bodhi, Anuj and Harshit.  We talk about the key ideas behind the book - about how it bridges the gap between theory and building practical ML/NLP solutions. We talk about the inspiration behind writing the book, how it stands out, how it has been structured, who can benefit from it and lots more. We also talk about the elephant in the room, GPT-3 and try to make sense of the hype around it and understand it's broader impact and how it positions us, as a community to leverage these systems on a wider scale.  

We also talk about the state of ML and NLP in general, about the many misconceptions and misinformed expectations that surround these fields in the context of the business of AI, and about how they've tried to incorporate this message in the book.  

Practical Natural Language Processing Book:  

Website: http://www.practicalnlp.ai/
Twitter: https://twitter.com/PracticalNLProc  

Authors / Guests:  

Sowmya Vajjala: https://twitter.com/adyantalamadhya
She is a research officer at National Research Council, Canada’s largest federal research and development organization. Her past work experience spans both academia as a faculty at Iowa State University, USA and industry at Microsoft Research. 

Bodhisattwa Majumder: https://twitter.com/mbodhisattwa
He is a Computer Science PhD student working on NLP and ML at UC San Diego. His research interests include Lang Generation and Dialogue & Interactive Systems 

Anuj Gupta: https://twitter.com/anujgupta82
He is currently Head of Machine Learning and Data Science at Vahan Inc. He has built NLP and ML systems at Fortune 100 companies as well as startups as a senior leader.   

Harshit Surana: https://twitter.com/surana_h
He is a co-founder at DeepFlux Inc. He has built and scaled ML systems and engineering pipelines at several Silicon Valley startups as a founder and an advisor.

Connect with me 🙎🏻‍♂️:   

Website: https://mukulkhanna.github.io
Twitter: https://twitter.com/mkulkhanna 

Deep Neural Notebooks podcast 🎙: 

Youtube: www.youtube.com/channel/UC66w1T4oMv66Jn1LR5CW2yg

Anchor: www.anchor.fm/deep-neural-notebooks

Spotify: www.open.spotify.com/show/2eq1jD7V5K19aZUUJnIz5z

Apple Podcasts: www.podcasts.apple.com/in/podcast/deep-neural-notebooks/id1488705711

In this episode, I talk with Shalini De Mello, who is a Principal Research Scientist and Research Lead at NVIDIA. Her research interests are in computer vision and machine learning for human-computer interaction and smart interfaces.

At NVIDIA, she has developed technologies for gaze estimation, 2D and 3D head pose estimation, hand gesture recognition, face detection, video stabilization and GPU-optimized libraries for mobile computer vision. Her research has been focused on human-computer interaction in cars and has led to the development of NVIDIA’s innovative DriveIX product for smart AI-based automotive interfaces for future generations of cars.

Shalini received her Masters and PhD in Electrical and Computer Engineering from the University of Texas at Austin. She received a Bachelor of Engineering degree in Electronics and Electrical Communication Engineering from Punjab Engineering College.

In this episode, we talk about her journey - about how she got started with Computer Vision and Machine Learning, from her Bahelor's to her Master's in Biomedical Imaging to her PhD work on Human Face Recognition - about how her research interests shaped over the years. We also talk about Machine Learning inside the car, about her vision of using Machine Learning & Deep Learning for building smart assistive interfaces for inside the car, and about how that manifested into the DriveIX product that NVIDIA recently launched. Among other things, we talk about the importance of open-sourcing technology, about the future of autonomous and semi-autonomous vehicles, about the joys of learning something new everyday, about how to keep track of the every growing amount of research and much more. It was an absolute pleasure to talk with Shalini and learn from her research insights. I hope you like the conversation.

Shalini De Mello:

Twitter: https://twitter.com/shalinidemello

Website: https://research.nvidia.com/person/shalini-gupta

Links:

NVIDIA Drive IX: https://www.nvidia.com/en-us/self-driving-cars/drive-ix/, https://developer.nvidia.com/drive/drive-ix

Self-Supervised Viewpoint Learning From Image Collections: https://research.nvidia.com/publication/2020-03_Self-Supervised-Viewpoint-Learning

Multi-sensor System for Driver’s Hand-Gesture Recognition: https://research.nvidia.com/publication/hand-gesture-recognition-3d-convolutional-neural-networks

AI Co-Pilot: RNNs for Dynamic Facial Analysis: https://developer.nvidia.com/blog/ai-co-pilot-rnn-dynamic-facial-analysis/

Podcast links:

Spotify: https://tinyurl.com/yb6sn2rv

Apple Podcasts: https://tinyurl.com/y9hu7lzq

Google Podcasts: https://tinyurl.com/ybb8gxd5

Anchor.fm: https://tinyurl.com/ya98vk7b

Youtube: https://youtu.be/Hfz965mLuvM

Connect with me 🙎🏻‍♂️: 

Twitter: twitter.com/mkulkhanna

Instagram: instagram.com/mkulkhanna/

Deep Neural Notebooks podcast 🎙:

Youtube: www.youtube.com/channel/UC66w1T4oMv66Jn1LR5CW2yg

Anchor: www.anchor.fm/deep-neural-notebooks

Spotify: www.open.spotify.com/show/2eq1jD7V5K19aZUUJnIz5z

Apple Podcasts: www.podcasts.apple.com/in/podcast/deep-neural-notebooks/id1488705711

In this episode, I interview Varun Jampani, who is a Research Scientist at Google Research. You might recognise him from the renowned Super SloMo paper. His work lies at the intersection of Computer Vision and Machine Learning. His main focus is to leverage machine learning techniques for better inference in computer vision models.

Prior to joining Google, he was a research scientist at NVIDIA. He completed his PhD at the Max-Planck Institute (MPI) for Intelligent Systems. He is also a IIIT Hyderabad alum, where he did his Bachelor's and Master's.  

In this episode, we talk about his journey — from his Bachelor's and Masters at IIIT Hyderabad to his PhD at MPI, about how his research has shaped over the years, about his focus on always asking good research questions and tackling fundamental problems in Computer Vision as a whole.  We also talk about the SuperSloMo paper, about how it started, the key design decisions that were taken and the challenges faced in the process. If there's one thing that you are likely to take away from this conversation, it is the importance of asking good research questions and letting that drive your learning and research.  

Guest:  

Varun Jampani: https://varunjampani.github.io/  

Links:  

CVPR 2020 Novel View Synthesis Tutorial: https://www.youtube.com/watch?v=OEUHalxanuc

Episode links:

Spotify: https://tinyurl.com/y7u98d7m
Apple Podcasts: https://tinyurl.com/y8w7tkhf
Google Podcasts: https://tinyurl.com/y8aas5le
Anchor.fm: https://tinyurl.com/ycudgfse

Connect with me 🙎🏻‍♂️:   

Twitter: twitter.com/mkulkhanna
Instagram: instagram.com/mkulkhanna/  

Deep Neural Notebooks podcast 🎙:

Youtube: www.youtube.com/channel/UC66w1T4oMv66Jn1LR5CW2yg
Anchor: www.anchor.fm/deep-neural-notebooks
Spotify: www.open.spotify.com/show/2eq1jD7V5K19aZUUJnIz5z
Apple Podcasts: www.podcasts.apple.com/in/podcast/deep-neural-notebooks/id1488705711