Ian GoodFellow Interview
Contents
The following are the takeaways from Ian GoodFellow Interview.
- Inventor of GAN
- Coded up GAN in one night
- Learn the basic math needed for Deep Learning - Linear Algebra + Probability
- Wrote a book with PhD co-advisors
- GAN can be useful in many different fields
- Got kicked after using Deep Belief networks from Geoff Hinton
- Do you want to make unsupervised work same as deep learning algos
- Do you want to make reinforcement learning work same as deep learning algos
- Do you want to work on the bias ?
- There are ton of ways you can contribute to AI ?
- PhD is not required
- Put your code on github
- Writing papers and put in on arxiv is also important
- Always choose a project to work with - Along with the learnings from the book
- Exercise basic skills - Always work on project
- Adverserial examples - Machine Learning security
- Spends 40% of the time on improving the stability of GANs
- Ian Good Fellow’s book is good - All the needed math is there in the first few chapters of the book
Obviously the first thing that comes to my mind - Why have I not being working on Deep Learning book ? Why am I shying away from going through the math and working out stuff ?
I should also learning about GAN’s and application in finance? There are a ton of applications of GAN. Need to be creating a demo using GAN