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  • [MUSIC PLAYING]

  • LAURENCE MORONEY: Hi, everybody, and welcome

  • to "TensorFlow Meets."

  • In this episode, I'm chatting with Haohan Wang--

  • HAOHAN WANG: Thank you for having me here.

  • LAURENCE MORONEY: --who's been doing some really,

  • really cool work with neuroscience, and TensorFlow,

  • and machine learning, and all kinds of cool stuff,

  • and even a new course.

  • So you've been working on Dyad X Machina, which

  • is a beautiful name for what looks like a beautiful site.

  • Could you tell us all about it?

  • HAOHAN WANG: Sure.

  • So I will start from the name Dyad X Machina.

  • Dyad means two as one, which is me

  • and my partner, Christian Fanli.

  • LAURENCE MORONEY: And Christian is

  • going to be on a future show, which is great.

  • HAOHAN WANG: Yes.

  • So machine-- things we constantly work with machine.

  • And we're at the intersection of deep learning and affective

  • computing.

  • So as for why we started Dyad X Machina,

  • I think the story really starts four to five years ago

  • when we first met.

  • So I was a student in finance and he was

  • working in human behavior area.

  • And we started to collaborate because we

  • both shared the same strong interest in machine learning.

  • LAURENCE MORONEY: Right.

  • HAOHAN WANG: So one of the first machine learning projects

  • that we worked on together is to create a trading algorithms.

  • LAURENCE MORONEY: OK.

  • HAOHAN WANG: Well, so Christian challenged

  • us to think outside the box instead

  • of using the traditional technical or fundamental

  • analysis into your algorithm, why can't we

  • add some human behavioral elements, like emotions,

  • into your algorithm?

  • LAURENCE MORONEY: OK.

  • HAOHAN WANG: Well, so we end up including

  • sentiment into our algorithm.

  • And surprisingly, sentiment is a great training--

  • a very indicative training signals.

  • LAURENCE MORONEY: I can see that because I'm

  • sure when some people are buying,

  • then everybody jumps on board.

  • And when some people are selling,

  • everybody-- you know, that kind of thing.

  • There's a lot of sentiment.

  • HAOHAN WANG: Yeah, early signal.

  • LAURENCE MORONEY: Yeah, definitely.

  • HAOHAN WANG: Yeah.

  • Well, so after that, I think it really brought emotion

  • into our attention.

  • And then we started to study a lot more

  • into this field, affective neuroscience especially.

  • And we read numerous book in the field.

  • And I think we started to notice a problem, which

  • is how society at large tends to ignore how important emotion

  • is, especially like in our daily decision-making process

  • because people tend to overvalue cognition

  • and rationality over emotion.

  • LAURENCE MORONEY: Interesting.

  • HAOHAN WANG: Yeah.

  • So I think, well, that's really the starting point that we

  • think probably it's our job--

  • it's Dyad X Machina's job to bring this affective layer back

  • into people's daily life.

  • LAURENCE MORONEY: So it's you saw an opportunity

  • to bring emotion into what are traditionally

  • logical decisions?

  • HAOHAN WANG: Yep.

  • There is where Dyad X Machina started, yeah.

  • LAURENCE MORONEY: Interesting.

  • And now, a lot of the work that you've

  • been doing and a lot of the learning

  • that you've been doing, you're turning into a course

  • now, right?

  • HAOHAN WANG: Yes.

  • So start from there actually-- well,

  • here is where deep learning and TensorFlow came in.

  • So we were studying machine learning,

  • but then we discovered deep learning.

  • So I think so here is also the point that we hit a point that

  • we cannot make any more progress because we're both working full

  • time and we'd dedicate all our free time on reading books--

  • LAURENCE MORONEY: Those day jobs just get in the way,

  • don't they?

  • HAOHAN WANG: Yep-- trying to understand machine learning

  • and affective computing.

  • So I think we had a discussion and made a hard decision--

  • I took one year off, focused fully on deep learning

  • and affective computing.

  • So here, you can think of this course--

  • and we end up making this course because this course is really

  • the synthesis of our learning because we really

  • dedicated a lot of time and effort

  • on learning deep learning.

  • And we think this course will be a great start for people

  • who are new to deep learning to get started.

  • But more importantly, I think our main focus

  • is to help people who want to use deep learning to the field

  • that they are passionate about to be able to get started.

  • LAURENCE MORONEY: So your course is about

  • applied deep learning with TensorFlow and Cloud AI?

  • HAOHAN WANG: Yes.

  • LAURENCE MORONEY: There's a lot in there.

  • So what kind of content do you have?

  • HAOHAN WANG: Well, so this is our very first course.

  • And we're a little bit ambitious trying

  • to put everything in there.

  • But the course is really meant to help

  • people who are new to machine learning

  • to be able to build their first deep learning model

  • and to take it all the way to deploy

  • their model as production-level API.

  • LAURENCE MORONEY: OK.

  • HAOHAN WANG: Then we move on to talk

  • about the basics of deep learning

  • and how to design an experiment with some typical neural

  • networks using Keras.

  • LAURENCE MORONEY: Keras, yep.

  • HAOHAN WANG: Yep.

  • And then we move on-- we dove deep into TensorFlow.

  • We start from low-level TensorFlow.

  • We introduce the concept like dataflow graph

  • and a TensorBoard, then we move on to high-level TensorFlow.

  • Then help people to build a model in the cloud,

  • train the model, evaluate the model,

  • and eventually deploy their model

  • as a production-level API.

  • LAURENCE MORONEY: So the deployment part

  • is really fascinating to me because there's

  • lots of great material out there about training models and maybe

  • doing a little bit of a test, but making it real world,

  • making it applied is really cool.

  • HAOHAN WANG: Yeah, that's our intention-- help people

  • to apply it to the field they're interested in.

  • LAURENCE MORONEY: Nice.

  • And we'll put a link to the course in the comments below.

  • So you've started with neuroscience,

  • and then you've moved into taking a year off and creating

  • a course.

  • And you've obviously gotten very deep into machine learning

  • and you've gotten very deep into neuroscience

  • and the intersection between the two of them.

  • What advice would you give to people

  • who are just starting out?

  • HAOHAN WANG: Well, yes.

  • So I'd like to talk a little bit about-- connect it back

  • to our learning experience of a journey of deep learning

  • and possible a little bit about affective neuroscience.

  • So we also summarize this four P's

  • of learning that I'd like to share here.

  • LAURENCE MORONEY: The four P's of learning?

  • HAOHAN WANG: Well, yeah.

  • LAURENCE MORONEY: OK, I'll try to remember them myself.

  • HAOHAN WANG: We published an article on our website

  • so people can check it out.

  • LAURENCE MORONEY: Ah, so we've got a link for that,

  • so I don't need to remember it.

  • HAOHAN WANG: Yeah, and you don't have to remember it.

  • LAURENCE MORONEY: Great.

  • HAOHAN WANG: So the first P-- papers and books.

  • LAURENCE MORONEY: Papers, OK.

  • HAOHAN WANG: Well, I think it's really the foundation

  • because we make sure we read papers

  • and books every single day.

  • And we got up 4:30 AM, the first task is to read paper.

  • LAURENCE MORONEY: Wow.

  • HAOHAN WANG: Yeah, deep learning papers.

  • So at the beginning, we read some more

  • like basic deep learning paper to know

  • what is trending in the field or how people solve problems