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

with deep learning.

Then we move on to be more specific about our domain--

how people use deep learning to solve

like neuroscience problems-- affective neuroscience

problems?

LAURENCE MORONEY: So 4:30 AM for the first P?

HAOHAN WANG: Yeah.

LAURENCE MORONEY: I think you've lost me already.

HAOHAN WANG: Yeah, first day.

LAURENCE MORONEY: Just kidding.

So papers.

And what was the second?

HAOHAN WANG: Paper and books.

LAURENCE MORONEY: And books, yeah.

HAOHAN WANG: Well, paper and books.

Second P is practice.

LAURENCE MORONEY: Practice, OK.

HAOHAN WANG: Yeah, so I think practice is very important.

Especially like I think I am very

passionate about like every day, I

will use TensorFlow to build some--

start from the basic neural networks

and understand like dataflow graph.

And like, I'm very interested in using TensorBoard to visualize

my training, and evaluating resource,

and how to tune my model's hyperparameters.

So I think then practice.

And you can find those problems very easily

on Kaggle and Crowd AI.

LAURENCE MORONEY: Kaggle, right.

Yep.

HAOHAN WANG: So the third P, which I think personally

is very helpful for my learning, which is preach.

LAURENCE MORONEY: Preach, OK.

HAOHAN WANG: Yeah, you can think of lecturing or teaching.

So every day, I will give Christian

a mini-lecture about what I learned during the day

about deep learning, some theories,

or even neural network I built that day.

So I think it's mutually beneficial.

It helped me to consolidate my knowledge

and helped him to have an overview about what

I learned and also making some progress from his side.

LAURENCE MORONEY: Excellent.

HAOHAN WANG: Well, last P, we think

is really helping us to get where we are right

now is passion.

LAURENCE MORONEY: Yeah.

They definitely need passion.

HAOHAN WANG: Yeah.

So well, first like I mentioned, we

saw this great potential of deep learning and TensorFlow

that you can leverage to really bring the transformative change

into the domain because this is new technology.

And regularly in the past, at least

based on our study about the effect of neuroscience,

no one has ever leveraged this technology

to build any algorithm yet.

So we set out to make that happen.

LAURENCE MORONEY: So bringing emotion into algorithms.

HAOHAN WANG: Yes.

LAURENCE MORONEY: It sounds fascinating.

So you started this journey as you were

doing financial trading stuff--

HAOHAN WANG: Yep.

LAURENCE MORONEY: --and then you just took a look at emotions

in that.

You know, from out of that has grown Dyad X Machina.

HAOHAN WANG: Yeah.

LAURENCE MORONEY: And now, you have a course.

You have your daily four P's that you're doing.

It's like there's a lot of amazing stuff there.

So what's next for Haohan?

HAOHAN WANG: Yeah, so next for me and Dyad X Machina

is we're right now, still working

on running experiments every day and doing research

in affective computing area.

We're trying to put things together

to be able to eventually present to the-- share

with the community.

So we will be working on this part of the study.

What's more is another thing about the course.

LAURENCE MORONEY: OK.