Statistics, Open Source & ML Research | Python for ML | Interview with Sebastian Raschka

Sanyam Bhutani: Hey, this is
Sanyam Bhutani and you’re listening to “Chai Time Data
Science” : a podcast for data science enthusiasts, where I
interview practitioners, researchers, and Kagglers about
their journey, experience, and talk all things about data
science. Hello, and welcome to another
episode of the “Chai Time Data Science” show. In this episode,
I interview Dr. Sebastian Raschka, currently an assistant
professor of statistics at University of Wisconsin, Madison
and the author of Python for machine learning book. Sebastian
has a background in biology and holds a PhD in quantitative
biology, biochemistry, and molecular biology. In this
interview, he talks all about his journey into the
intersection of biology, machine learning, statistics, open
source, and machine learning research. Yes, these are all of
the topics that Sebastian is currently involved in. We also
talk about his journey with writing the book. I’m sure you
all are familiar with the book and the book Python for machine
learning has been rewritten thrice over the past few years,
we discuss how the book has evolved. And the latest
additions to the version that has just come out, the third
edition that is. We also discuss the vast Ian’s current research
interests and his research efforts in the areas that he’s
currently actively contributing to. We talk about another area
that Sebastian’s are also active in, which is open source.
Sebastian is an assistant professor at UW but he shares
advices that apply to any student at university or
otherwise in the field of machine learning looking to
learn anything. So I’m really excited about this interview. A
quick reminder to the non native English speakers. This will have
checked subtitles on YouTube. So if you are watching it on
YouTube, please remember to enable the subtitles. And the
blog version of this interview will later be published on the
links that you can find in the description on this podcast if
you’d like to check out the blog version later. For now, here’s
my interview Dr. Sebastian Raschka. All about machine
learning statistics, open source and research. Please enjoy the
show. Hello, everyone, I’m on the call
with one of my favorite teachers, not from university,
unfortunately, but through his book, Dr. Sebastian Raschka.
Thank you so much for joining me on the “Chai Time Data Science”
podcast. Dr. Sebastian Raschka: Yeah,
thank you for the invitation to be on the Chai Time Data Science
podcast. I haven’t done a podcast for long time, and I’m
maybe a little bit rusty. But I am excited. So yeah, I’m happy
to be here. Sanyam Bhutani: I’m really
excited as well. I’ve held all three versions of your book,
which unfortunately, I didn’t finish properly as my homework
but I’m really excited to be talking to you. Dr. Sebastian Raschka: So you
have a homework assignment, right there right? I mean,
three, so yeah, I appreciate your support there. Hope you
liked them. If you haven’t finished them, I hope it’s not
because you didn’t like them. It’s more like maybe you were
busy. Sanyam Bhutani: Certainly the
latter. We’ll talk definitely a lot about the book but I want to
talk about how you got started. You’re currently an assistant
professor of statistics and you’re doing many interesting
research in the intersection of a few fields that we’ve just
talked about. But I want to talk about how you got started. You
were doing your base in biology. And then you discovered a PhD
program by stumbling into a flyer, I believe, during your
undergrad days. Where did stats and machine learning start to
come into the picture of biology for you? Dr. Sebastian Raschka: Yeah, so
that’s an interesting question. It’s actually a long time ago,
but you’re right, there was a flyer so I was studying in
Dusseldorf where I did my undergrad in biology, and there
was this exchange program with Michigan State University. And
yeah, you know, Germany is a small country. At some point,
you’ve pretty much seen everything I thought. Infact
interesting exciting opportunity to just see something different.
So I went to Michigan State and we could pick our own courses.
So I was back then already dabbling with uh, I was taking
bioinformatics classes or Pearl and stuff like that. Yeah, and
then I took a statistics class was an advanced class. Molecular
evolution class like only computation focused and where
you basically just also use statistical techniques. I’ve
been really interested in statistics and then I joined
Michigan State University of the PhD program later. And during my
first semester, I took statistical data classification
of class in computer science. And that got me hooked
basically. So there was basically a Bayesian statistics.
Mostly, we focus on Bayes Optimal Classifier as my space
and all everything revolving around the base theorem. But
then I based on that, I mean, this is just a small picture of
the machine learning field. I also took a data mining class
and there was also the time when I started writing my blog, got
super excited about everything. And yeah, I was just getting
hooked back then. And I could also see how that applies to my
work and kept studying and that’s, that’s basically how I
ended up in machine learning, I guess. Sanyam Bhutani: And I believe
this is sort of a unique situation because maybe I have
the wrong friends but my friends from biology usually completed
distant from statistics and programming at all. What led you
to become passionate about these things specifically? Philipp Singer: Um, I think it
was more like I always like to create or build things or tinker
with things. It’s maybe a little bit sidetrack here, but I was
always as a kid into computers and video games and stuff like
that. This is like really a long time ago, but there was for
example, on the video game modding scene and stuff like
that. It was before online gaming was, internet was new. It
was super exciting. And you shared your you had a level
editor, you were making levels, and I even had a web server for
a certain computer game back then. I like to tinker around
with things and programming is kind of like that. So you it’s
kind of freeform. It’s like, you have a canvas and you can create
pretty much everything. And that is what I find exciting. I was
never like a person about informatics where I just don’t
know how to use a tool. I usually, maybe it was sometimes
making myself making my life harder. But I was trying to do
things myself if I had to, for example, analyze the DNA
sequence, I would just write my Python script to give me the
statistics of the different amino of the different bases
like adenosine and guanine and stuff like that distribution of
that and stuff like that. But this is a long time ago, I don’t
work with much political data these days anymore. So I’m more
like in statistics focused on machine learning and deep
learning, not just a nice application areas that have some
collaborations with colleagues in that field, so. Sanyam Bhutani: Interesting. And
I believe in your undergrad itself, we’ll talk more about
this, you were even exposed to open source contributions, which
again, you really enjoyed. What made you pick research over
industry because you were also oriented to the industry as well
in terms of coding practices and open source contributions? Why
go ahead with the research? Um that is an awesome,
interesting question. So maybe that is a good question. I would
also say it’s maybe related to why I like coding, I like my
freedom. So in academia, it’s also the way you structure your
day, structure your research, it’s, for me it’s a lot of
responsibility. But I also like being flexible in terms of what
I like to work on and how I like to work on things. But also to
be honest, I mean, it’s not really among other people are
listening. But I was kind of (with teaching), in my last
semester, I was really busy, was getting all the papers out and
writing the thesis and just getting ready for the PhD
defense and I honestly, I was a little bit procrastinating there
regarding job application site. And single one. So I thought my
strategy was maybe I wouldn’t recommend it to anyone, but my
strategy was just let’s finish the PhD first and then I will
take maybe a one two months break and we’ll think about
things what I want to do next because I like you said, I mean,
this is like, there are the pros and cons in both like industry
and academia is, I wouldn’t say one is universally better than
the other. It’s like a trade off and both both fields is a trade
off. So what’s my cave? Let me finish one thing first, and then
I will just focus on making a decision while just think about
things early on, there was there were some small opportunities I
had back then where companies reached out, started the
interviews with Google. But at this point, I really didn’t know
where I want to be at that point. I really wanted to take a
break first. But then my pitch defense was like early December,
and one professor here from my current department sent me an
email, hey, we have openings here, why don’t you apply? He
will be excited to invite you for giving a talk and basically
interview you. And I thought, huh, yeah, why not? So this is
basically was my only job application sent. So I got kind
of lucky that I was invited to give an interview for the
interview here to give a talk. And I really liked my current
colleagues. I liked the department. They liked me
apparently. So that’s one thing actually other basically and no,
this was really exciting because in my first year, I was
designing two new courses or machine learning course a deep
learning course. And right now I’m really doing what I really
love. So I teach and do research both and this is perfect for me,
I would say that’s a good fit. I was lucky in that way. Awesome. I really envy your
students who get to study from you in person rather than us who
have to rely on your book. But we’re fortunate that we have it.
What does a day in your life currently look like? Because I
believe you juggling multiple roles as well. Do you have Chai
and Code and then you have to teach a class? Or how do you
also maintain this balance of all these areas that you will
come across? Dr. Sebastian Raschka: Yeah, so
I this is a very good point. This is yeah, you have a lot of
things on your plate. Basically, you have to teach, you want to
do your research, but also your students that also help you with
research or you help them with research is kind of symbiosis
basically. But also can be a little bit much sometimes. So
you have to find a balance at some point, for me, I maybe
that’s my German gene, I’m kind of I try to be organized. So I
have a daily to do list. And every Sunday, basically, I do a
review of what I want to do this month and the upcoming week and
then try to find a balance between teaching,research and
spending time with my students but also doing some coding. So
because my classes are very, I mean, they are theory concepts,
but they also involve practical portions. So in that way, I also
want to keep up to date with current technology basic and I
don’t want to be a dinosaur at some point, teaching from the
last decade or something like that. I mean, nothing against my
grad school experience. But I remember too well writing things
in MATLAB and I don’t want to be at that point where, you know,
the similar situation with you. So I try to find a balance of
staying up to date with coding stuff and technology. I’m doing
my teaching basically. Which is two times a week currently. So
it’s basically my Monday is preparing for Tuesday and my
Wednesday is preparing for Thursday. But except that I try
to do as much research as possible and meet with my
students attending seminars and talking to colleagues. It’s like
a, it’s a trade off, you can’t do everything that, the day only
has so many hours. But yeah, it’s also because it’s such a
mix. It’s never boring. So it’s always something new, something
interesting. Sanyam Bhutani: Definitely. Dr. Sebastian Raschka: I
wouldn’t say I have the super great tip for balance or
something like that. But planning ahead definitely helps
like writing things down making a plan where I want to be at the
end of the year, what do I want to accomplish? And then kind of
focusing on the things that are related to your goals basically. Sanyam Bhutani: I think there’s
no secret. It’s just planning and solid discipline, like you
said. Talking about; Dr. Sebastian Raschka: Hm. Sanyam Bhutani: Sorry. Talking
about research. So I haven’t worked in a certain facility,
but I believe research is about asking the right questions that
lead you to working on problem. Can you tell us how does a
research pipeline for you look like? How do you approach new
ideas? And what questions if I may, you ask while working on a
new problem? Dr. Sebastian Raschka: Yeah,
that is also a good question. Also, here, there’s maybe no
golden rule. But I think there are multiple ways you can
identify an interesting problem to work on or find some ideas.
So one would be basically reading papers, and then maybe
noting something that doesn’t make quite sense. Or you get
ideas, for example, for improving something. I mean,
traditionally, scientific progress. I mean, deep learning
is a fast moving field, you have a lot of new things, but
traditionally, also, I mean, science is like a continuous
progress where you take something and improve it over
multiple stages. So in that way, just also seeing what other
people do. I mean, I wouldn’t do that too much, because then you
are kind of kind of a little bit limited on what what your
horizon is basically. So but yeah, definitely reading other
people’s work helps. So for example, when I was working on
this new network architecture for ordinal regression, that was
basically inspired by the fact that there was a collaboration
where we had auditor labeled. So I was thinking what methods can
we use instead of just a classification or regression
method, regression method what is out there for deep learning?
I was looking at literature and then I read a paper basically
where they presented such a method but then while reading
this I noticed okay, there is this method works really well so
there’s no criticism here but it’s um, it could be improved
because it didn’t have a rank consistency basically. So I
don’t want to go into too much detail in there. But then I
thought okay, we can have basically an improvement that
has this rank consistency and that that basically to a
research project, and then also when design research projects,
you always have in the back of your mind your other research
projects. You I mean, you never finish really, a research
project is also a progress of process where you start working
on something and you get some exciting results, and at some
point you write them up and share them. But there’s always
more to be done, always something that you can improve.
I mean, this is like, it’s kind of an endless process. It’s an
infinity in a way. So in that way, this project, for example,
I use the data set of face recognition data set of image
data set of faces, where we had age labels, which can be thought
of an ordinal regression problem if it’s a non stationary
progress. So if you think about it, let’s say the difference
between five and six year old is different. So the H, it’s a
between five and seven. You notice more than that the age
difference between 70 and 72, for example, where maybe only
yours you get maybe a few more wrinkles, but not really
noticeably. So in that way, it’s more like as non stationary
process and that is basically something where you can maybe
apply autoregression and face images were then related to
another research area of mine where we were working on
protecting privacy and face images. So That way always also
try to find when you design a research project, some
connection so that you don’t just do some random thing just
because it’s the cool idea of the day basically. So just to
maintain some focus also, that’s also I think, important not to
get sidetracked when designing the research project. But um,
regarding a question, the research pipeline how to
approach the problem, really, if you have it, I try to write down
as much as possible, brainstorm ideas, but then also talk to
people. Sanyam Bhutani: Okay. Dr. Sebastian Raschka: Uh, to my
students. First of all, the project must be a good fit for
my students also, because otherwise, it’s maybe it’s not
interesting. Student wouldn’t be motivated or it’s maybe not the
direction the student wants to go. So it has to be a mutual
interest that we both want to work on this basically. For
example, I want him to carry specializes more in transformer
architectures, but another student focuses more on graph
neural networks. So in that way, I also try to see that this is
basically this project is aligned to what the student is
interested in and what the student has worked on also
before. I mean, of course, it’s always good to learn something
new. But there should also be a balance that you don’t want to
overwhelm basically students because it’s also not motivating
if you work on something, and you never get results. So you
need you want to also design the research project, it’s also very
important point in a way that you can have a sense of
incremental progress. You don’t want to pick a project that is
very ambitious. And then you maybe never get there, at least.
But in the next five years, you want to have something where you
have like checkpoints, because these checkpoints are basically
your conference papers of updates and talks you may give,
because if you work on something, and in you don’t have
anything to present for the next five years, it’s I think, very
frustrating. It can be frustrating. So in that way,
designing a project that you solve it in multiple steps
basis, I think also helpful. Sanyam Bhutani: It’s similar to
how MIT I think the professors at MIT sat down and decided to
solve computer vision over a summer in the 1940s, over
solving maybe object detection over certain objects, for
example. Dr. Sebastian Raschka: Yeah,
this is also a good point, it’s like, summer is the productive
time when you don’t teach. So it’s also, sometimes I really
leave my most ambitious projects for the summer, where I can
really spent a few days uninterrupted, so that it’s also
sometimes helpful to really have a chunk of uninterrupted time,
so. Sanyam Bhutani: So another, on
the flip side is another question that when you have this
uninterrupted time, you might continue exploring, or you might
have to put brakes on the project because at least in
machine learning, nothing works until it does no model works
until it perfectly does. How do you decide whether you want to
continue exploring? Or maybe it’s an idea it’s a it’s a time
to put an end to the experiment? Dr. Sebastian Raschka: I maybe I
have a hard time sometimes when I already spend a lot of hours
on a project to say okay, maybe that’s not going anywhere. But
there were times like that, for example, in the past, when we
designed, we designed a new app for pricing for Facebook,
related to face recognition, but we wanted to protect the privacy
of images. And working on this project, I noticed basically
that there was an imbalance in terms of certain groups that
were not represented well in the data set. And that led to higher
areas for example, for individuals with a darker skin
color, for example. And we worked on that investigating
that for different commercial classifiers and also open source
versions but then I mean, we saw at some point I have this Google
scholar recommendation stone on there was a paper recommended to
me which was basically called Jenna sheets, paper where they
did exactly that. And so then that way, there was no point in
waiting to continue the spacey so that way, we didn’t spend too
much time but I mean, luckily someone I mean, I would say
fortunately, and unfortunately, because fortunately, someone
also cares about this project. So we know more than, you know,
before, because someone already looked into this, unfortunately,
because I had a student working on this in this room was of
course little bit disappointed because some work was basically
for nothing but then the student worked on something else. And it
was still a useful experience by just working on this, you also
learn things and so it’s never completely wasted, because you
always learn something. And it’s kind of part of the training
basically. On the other hand, it’s sometimes important to say,
hey, maybe, maybe we should work on something else, because that
is maybe not the best investment for my time. But yeah, it’s,
it’s hard. Sometimes it’s because if you spend hours, you
also want to somehow finish it because it feels like otherwise
you wasted your time. But it’s not the only thing. Sanyam Bhutani: I think with
intuition, maybe you start to get an idea of what areas to
look for, even in terms of literature and then decide about
the project. Dr. Sebastian Raschka: Yeah, so
literature is a big part of this. It’s like, you also, you
want to work on something no one else is working on but you also
want to have some, some some framework basically, you don’t
want to do something crazy. No one cares about or yeah. Sanyam Bhutani: Yeah. Talking
about training there’s also this, actually the message that
I try to get across to the audience, but there is this
misconception that the researchers always have to have
a lot of graphic cards. Maybe you’re hiding graphic cards on
other end of the camera. Is that true? Can you confirm or deny
that belief? Dr. Sebastian Raschka: So yeah,
GPUs. I think for deep learning work are important, but they are
not everything. But one of my students. I think he just made
this up. But he said, a famous researcher once said you have to
have at least eight GPUs to be productive. Sanyam Bhutani: Hehehe. Dr. Sebastian Raschka: I think
that was just his way of saying, hey, I need my eight GPUs all
the time. Sanyam Bhutani: Hehehe. Dr. Sebastian Raschka: So he has
eight GPU’s he’s doing a lot of product tuning with that. I
think so, I’ve assumed one of my students on- only uses one GPU,
it’s enough because it depends on what you work on. I have also
my own GPUs, I use maybe two to four at a time, both for
teaching and for experimenting with things. But if you, it
really depends on what you’re working on, if it’s something
where you so the way what’s helpful is, is basically that in
deep learning, there’s no really principled way of no finding out
what happened without choices you should use or all the
different architectures you want to explore that may be
candidates for for your new network designer, even just
comparing your method to related methods basically, all that
takes time basically, computing time. So usually, I would say
the more the merrier. But of course, also, at some point it
takes, I mean, you need to also time, you have to write their
own things. You have to write your paper at time. It’s always
a trade off. If you have too many, maybe you finish your
experiments, but you didn’t even have time to think about what
you want to work on. So that way, it happened, you run things
you’ve never used them. So in that way, there’s a balance. So
a certain number is good. So you can run things you want to run.
But at that point, they almost maybe at some point, you may
have too many. So you run thing, you have idle GPU. So it’s also
a balancing act if you have a limited budget, how much you
should invest in new hardware. And so we are fortunate at UW
Madison so we have a system called condo basically, which is
connecting all the computers on campus. Sanyam Bhutani: Okay. Dr. Sebastian Raschka: Um, you
can use that for CPU. So we have basically a giant, general
enormous CPU resource there because all the campus computers
are connected. It’s mostly like a ram cluster, but including not
only the data science machines as well as the data center
machines, but all computers also personal desktop computers, if
they are items so they can also be utilized, but GPUs are I
mean, GPUs, they are just starting to announce, so we
don’t have that many yet. But we last year got a grant approved
to also add more GPUs to this. So we have, I think this year
already, they’ve bought like 32 new GPUs. So in that way, it’s
also becoming easier for students to get resources, of
course, so it will take some time and in my classes, I use a
mix between local GPUs and also using Google color and the
Kaggle kernels, which are great. They’re great tools for
students, which are free can only use maybe one GPU at a
time. But that mean for learning, it’s usually already
or a very helpful to have at least a bunch of you. Sanyam Bhutani: I think, even
for people who just want to maybe get a taste of the field,
they can go ahead and check out collab, Kaggle kernels, even the
free resources that I hope GCP continues to provide and they’ll
get a taste and then maybe decide if they really want to
continue being frustrated or maybe invest some money into an
expensive graphic card. Dr. Sebastian Raschka: Yeah, and
also one thing I have to think about when you’re teaching. So
the class I’m teaching many students are also new to
computing basically. And I mean slurm. And Linux is not super
complicated, but it takes you at least. So I don’t want to
overwhelm my students by Hey, you have to set up a or even a
AWS instance, it’s not trivial to get up if you have never
worked with Linux. So that way Google call up is awesome,
really appreciate it. Because you can just go online and your
web browser have a Jupiter notebook with a GPU connected
and you can just start working, because this really lowers the
friction point people have when you start with deep learning,
because the running already is a lot of stuff to learn about. And
then if you have to worry about computers and how to set up your
Linux stuff, it can easily get frustrating. So in that way,
it’s at least one barrier less if you can use something like
Google call up. Sanyam Bhutani: I like to joke
about it as if once you own the hardware, you have to take the
secret oath of work, oath of walking through fire with
setting up CUDA drivers which are always annoying. You tend to
mess up something and I think that’s also barrier, like you
said. Dr. Sebastian Raschka: Mhm. Sanyam Bhutani: Now, coming back
to your current research, can you tell us more about your
research you’re working on? You still very active in the
intersection of privacy and semi admissible networks? Can you
tell us more about why are these interesting to you and more
about the field? Dr. Sebastian Raschka: So yeah,
the privacy unseen adversarial network. So that was a series of
papers basically, that started with a friend of mine by V
Mirjalili, so he was we were both students at Michigan State
University and he was developing a method with us Professor,
based on face swapping if I remember correctly. So in a way
back then we talked about, so deep learning methods that we
can use to achieve maybe better results. So the challenge is
what what our goal was basically to protect privacy in images in
that context. We started with hiding a certain attribute, face
attribute and so here what we did is basically hiding, trying
to hide the gender. Because, for instance, I mean data collection
on the internet, it’s very prevalent nowadays, you can just
download certain information on people or people that you’re
maybe not supposed to. It’s maybe it’s private. So in that
way, we just also wanted to develop a general method that
you can retain the utility of your data, let’s say the face
image for face recognition, it let’s say, biometric scan,
airport scanners and so forth. But you shouldn’t be allowed to
extract more information than you’re supposed to. For example,
even if you have a security camera that you can have still
the security footage, you had maybe some incident you can
recognize who that is in the picture. But you can’t maybe do
a large scale study of different people who, let’s say the
distribution of gender, visiting your story and things like that.
So in that way, we designed to simulate your network with two
goals in mind, basically retaining face recognition
accuracy, while hiding the genetic code. It’s basically a
kind of a constraint optimization problem. For that
we started very simple using simple autoencoder. We did. So
that was the idea a little bit earlier before even generative
adversarial networks. But so the first idea when we had that was
a little bit before that. So but later we recognized, which was
very networks could be even more useful in that way. And the last
paper we had was basically using again, with the cycle
consistency, but yeah, going back to this idea, we had this
autoencoder, giving the autoencoder face image. And then
attached to it, we had gender classify and to face
recognition. Both were in that it could be anything but we had
used commercial networks for all of the modules, sub networks.
And when you basically feed the image to the autoencoder, it
will try to reconstruct image but then we add a constraint to
that. The way that the gender classifier prediction should be
flipped. So that’s basically at the circle. And the not
adversary goal is retaining the face matching accuracy, which is
why we call it semi adversarial. So it’s half of the circle,
basically. Sanyam Bhutani: Okay. Dr. Sebastian Raschka: Yeah. And
we have kind of series of papers on that. There’s basically
improving it also incrementally, like we talked about, it’s
basically you have this idea, you get it to work, it works. So
we had kind of proposed a method offers paper, but then we knew
okay, we can actually improve this by many different ways. But
what if you would basically do all of them at once it would be
a multi year project and it would be a 50 page long paper
that was great. So we kind of did this incrementally. So the
first paper was the basic idea basically. Sanyam Bhutani: Okay. Dr. Sebastian Raschka: The
second one, we improve the issues like imbalance, data set
imbalance, because for example, people from certain
subpopulations were over or under represented and the third
one was basically idea where we basically connect on different
modules of these because every module adds a certain number
certain perturbation to the image. And we can basically
control how much we can disrupt image by having multiple these
same effects or networks in a sequence. Because basically you
can, what you get is basically you have to input as a face
image and then your output is a modified face image, you can you
can take this modified face image and give it to another
senior officer network to add some other perturbation on top
of the faces. So you can modify it step by step. And the third
one was basically extending this idea. So not to only hide
general information, but also to hide race information and age
information basically so doing three things at once. And that
was also with a cycle consistency. And it is also
selective framework. So you can say I want to, let’s say hide
the gender, but I don’t want to hide the age or I want to hit
both at the same time and so forth. So it was more like a
little bit more, more complex in that way. So a little bit more
general also in the way that you can target specific attributes
and you had the psychokinesis since we borrowed this idea from
the CycleGAN, paper that made also the results look much
better basically. Sanyam Bhutani: Okay. Dr. Sebastian Raschka: Number
one area of research, right? Yeah, so. Sanyam Bhutani: That was a high
level overview. But in case anyone wants to take those
papers out, I’ll have those linked in the description of
this podcast if you want to read them. Now, another aspect that I
think not many researchers, I wish they were active in is open
source, you are always kind enough to share open source
implementations also for research, and also cause you’ve
been contributing to it outside. And I think you discovered your
passion about this during your undergraduate days, can you tell
us about your open source then you have contributed to
Scikit-Learn TensorFlow and even Pytorch. Dr. Sebastian Raschka: Yeah, so
um, that started basically, all of passion. I like coding at
some point I discovered, hey, I’m using open source software.
And it might be useful if I find some issue with it, if I not
only fix it on my computer, but also share the solution with
others so that everyone can benefit from it. And I have to
say, in the early days when I contribute to the second round,
I really learned a lot because people working on secular and
they were really good coders they had like, what I really
liked about this is oscillators very rigorous style expectation
that you can’t just upload any code, you have to have the
documentation alongside with it and unit tests, and also stylize
it has to be good code. And this was really inspiring, I would
say, it’s like, that inspired me really to do a proper job when
coding pissy. I mean, I mean, there are days when I don’t know
I have to sort some files or rename some parts of my
computer. I don’t read documentation for little things
like that. But if I write research code, I always try to
document it and also have good practices because even though I
may not use it everyday or share it with other people right now,
there may be a time next two months to work on some project.
I can give the student my old code and it’s the readable. And
in that way, open source really encouraged also good habits
because it’s like, even if you if you write personal notes, if
you have, let’s say a diary, you maybe don’t care as much about
your handwriting. But if you put an article online where many
people can see it, you want to make sure that it’s also legible
and the arrows in there, the same way with code, I think open
source, but also what I liked about it is basically it’s
bringing the best of the best it makes you do good job. And it’s
also very satisfying when you hear people will find out what
people who find it useful and give you feedback. That’s also
another aspect of when I contributed or had my own open
source libraries. Other people were using the code, and they
had ideas I didn’t have and they improve the code a lot. And they
pointed maybe arrows and made and I learned so much just by
interacting with people use my code basically. So one recent
example I have this emmonak stem library, which basically just
started about, it was just seriously a loose collection of
things related to machine learning. It’s also why it’s
called ML extended machining extensions. I mean, it wasn’t
maybe the best name, but I just wanted to; Sanyam Bhutani: Hehehehe. Dr. Sebastian Raschka: It was, I
just wanted to have a guitar project basics. So I just picked
the name. And then I just, every time I needed some new
functionality that was not implemented elsewhere, I
implemented it and edit it to that. So, for example, for one
research project, I was needed doing some frequent pattern
mining, Sophie Preqin itemsets, and things like that are using
the a priori algorithm first to find frequent itemsets. And I
couldn’t find a good Python solution, there was this C++
package, but it was not, what I wanted was a command line tool.
So I wanted something where I can have the output in the
pandas data frame for the (the project). So I brought my own
apriori algorithm and put it into an extent. And this is a
more I would say now the most vital used things and accept the
frequent data mining things. But I only uploaded the very
rudimentary, rudimentary, but it was a working version, the
apiori version. And then other people use the code and made it
much faster. They saw, okay, why are you doing this? You can
generate the subsets more like the combinatorial search more
efficiently using this and that. And so they kind of improved the
code. And it was amazing. I mean, it was like growing now,
there are so many different I mean, not that many but at least
three different frequent item set mining algorithms like FP
roles, FP Max and stuff like that, and it’s much faster than
before and it’s only because other people contributed I don’t
I wouldn’t be able to do that and may not stop engineering
that sense that I, I mean, I, it would take a considerable amount
of effort. And this way I really learned not just by having other
people doing the contributions and pointing things out. And not
it’s kind of amazing. It’s also it helps you learn basically
because these are things you don’t learn from textbook
basically. I mean, piano, there’s not a textbook who will
explain to you how to take this algorithm and make it faster in
general. It’s like it’s very useful to be I think involved in
open source of course, there’s always a balance you also have
other responsibilities but it’s like a nice freetime activity I
think. Sanyam Bhutani: You’re still
actively contributing to ML extend another project of yours
bio pandas, I believe that you still active on. Can you tell us
why are you still active on this? And maybe a little bit
more about bio pandas? Dr. Sebastian Raschka: Bio
pandas, right. There, I’m not that active anymore, because it
is, um, I would say, I wouldn’t say it’s finished, but it was
maybe a smaller idea. So the idea was basically so I did back
then a lot of small molecule screening that is basically a
relative practice covery of a small molecule. And you want to
find similar molecules and do some screening and compare
molecules and compute statistics about the molecules. And there
were so many different tools that you can use to compute
certain things about molecule, a molecule is basically a, you can
think of it as a graph. It’s basically a atoms that are
connected by bonds to come. However, when you don’t know
what these files are, there’s, for example, one firm called mo
to. And for proteins, proteins, a larger molecule, you can think
of it as a large molecule peptide peptide. That started to
major a or file for proteins as a PDB, the protein databank
file, and which is basically on a text file. It was basically
developed for Fortran back in the day, so it’s kind of based
also. So you have the coordinates of each item and the
item type. That one, you don’t even have one information, just
the coordinates and the item types. So it’s just a list of
these things. And it would be so there are many, many different
tools that let you read that in and have their special API to
analyze and parse that. And honestly, font is a little bit
cumbersome to learn all these different API’s of the
specialized tools. So I thought maybe convenient to just have a
pen a state of it because it’s just the table. It’s just the
coordinates. And there’s item type. So why not having a pen a
stable and then I don’t have to learn an API. If I want to count
the number of certain items, I just use some in tennis, for
example, or I can compute other statistics with it. Basically,
just using Excel, tennis or doing a selection, basically,
the powerful selections in text. So bio pandas was really just
inspired by the fact making my life easier to develop
basically, it was basically a tool that takes this data file
and converts into a data frame, in a panda’s data frame, which
is why it’s called bio pandas, hehe. Sanyam Bhutani: Makes sense. Dr. Sebastian Raschka: So again,
the same same thing with a small molecules for the more two
files. And I used this pretty heavily in one project screener
where we screened 15 million molecules. And I also added
multi processing to it and things like that. But bio pandas
was basically the core engine for reading in these files,
basically. So in that way, I would, I wouldn’t say it’s
finished. Nothing’s really ever finished. But it is basically
you can add more functionality. But the goal was really just
being able to use pandas API on molecule data basically. Sanyam Bhutani: Awesome. I’ll
again, have both the GitHub links in the description of the
podcast in case anyone’s anyone wants to check them out or maybe
add any functions. But talking more about open source, you’ve
also contributed to TensorFlow, also in a book format. Now, the
book is also updated to TensorFlow 2.0. Can you tell us
more about the book? How did that journey start for you? And
what led you to writing the book? Dr. Sebastian Raschka: Yeah, so
that is just so, good question. The TensorFlow part came a
little bit later. So the first edition, it didn’t have any
TensorFlow, when I remember correctly, it’s already four
years ago, I had a very brief section on psionic then, which I
use, um, but yeah, so the book was mainly, it started as a
Python machine learning. And also back then I think it was
even called Python machine learning essentials. So I added
250 pages. So I had to keep everything very short, I can
remember I was writing chapters and it was like 30 pages and and
send them to the publisher. And they say ahuh. just if you have 12 chapters,
you can’t have 30 pages per chapter. So I had to keep
everything pretty short. But then the reviewers really liked
the book, and also the publisher really liked the book. So they
dropped the essentials from the title, and then I could really
expand it. And then, but then, of course, the there was not
much time left for the first edition. So the first edition
was focusing on machine learning and Scikit-learn. And then
later, we added basically, deep learning and TensorFlow and this
is where V Mirjalili also helped me a lot. So because I was
already, that was 2017. It was kind of late in my PhD, I was
also working on other projects. So we both worked on it
together, basically on the deep learning chapters on the
TensorFlow chapters, basically. Sanyam Bhutani: Okay. Dr. Sebastian Raschka: And then
the last year, last summer, we started working on the third
edition, which was basically taking to the TensorFlow 2.0.
Basically, just it was a large rewrite. All the chapters that
were related to expanding TensorFlow had to be rewritten.
Then we had the CNN and RNN in chapters, we could still use the
general explanations but TensorFlow 2 changed a lot. I
mean, there are a lot of little things that changed. But also
just the whole paradigm using a dynamic graphs now make
everything a little bit different. And then, of course,
the new chapters that we had about cans and reinforcement
learning also, which was pretty excited. Because yeah,
reinforcement learning is a hot research field right now, we
didn’t ever come out reinforcement learning. And
people always said, hey, you mentioned reinforcement learning
in the introduction, but you never talked about it in the
book. So finally wehad a chance to also expand it. Like I said
before, it was a little bit due to page and time constraints
that we couldn’t do everything. But doing this over the year was
been really cool that we can could finally go where we wanted
to go with that. So basically having a chapter on
reinforcement learning and GANs also. Sanyam Bhutani: I think this was
at the point when you were already an active blogger. And
you were really enjoying blogging. Did you see a gap
maybe in the resources, which led you to writing this book? Or
is there a story? Dr. Sebastian Raschka: That’s a
very good point, I think one led to the other. So I started with
basically blogging. But you may also notice that I’m not
blogging that much anymore. Because the book is basically an
extension of the blog. Now it’s like, I think that’s maybe why
the publisher contacted me they liked maybe maybe my blog
articles and asked me to write a book. And for me, I thought I
was thinking about this, but I thought it might be a good
opportunity to have to basically do what you do in the blog, but
to have a book where you can reference I mean, each chapter
can be it can be a sequence for the blog, you have an article
here and an article there. And they are more like this joint,
you can maybe write a series of blog posts, but then it is
basically a book basically. So it was basically an opportunity
for me to write about everything in a more structured way. And
having a sequence instead of doing a random blog post here
and there to have this book. Some more complete source
basically, this is also where I initially wanted to go with the
blog. I basically had a I think I had an article like
introduction to a single layer neural networks, which ended up
being the second chapter of my book, then the perceptron. And
adaptive linear neuron basically. And also the PCH
chapter in the book is inspired by the blog post, basically so
it’s all kind of connected in that way. Sanyam Bhutani: Did you expect
the book to become this famous if I may famous, or is it still
behind on your vision? Dr. Sebastian Raschka: Honestly,
I didn’t expect that at all. I was right. I wrote a book on
heatmaps in R that was in 2013. And I don’t know maybe 100
people or 12 people on it. So that was not a very popular
book. And I expected something about the same for this book,
too. So it was I didn’t write this because I wanted I wanted
it to be popular, I was more like, as a student it was, I was
just excited to write and this was a good opportunity I thought
because having someone also reviewing your work and helping
you putting it together this in one framework, basically, I
thought that might might be a good opportunity, but I never
expected that people would like it that much. And it’s really
nice to see. Sanyam Bhutani: Now, the book
has been rewritten thrice, I think, which also represents the
pace at which our field grows really fast. It also took a lot
of efforts. Can you tell us what’s latest in the third
edition? And what are the exciting updates in the; Dr. Sebastian Raschka: I hinted
at this a little bit, like a few moments ago, but let me start
again, because I thought I was going a little bit on tangents
here. So yeah, what we basically did is we took all the first 11
chapter, chapter 10, was plus clustering, the one after until
the one until the first chapter into TensorFlow, not much to
change there, but there was a lot of reader feedback, a lot of
questions I got by email, little things that I maybe didn’t
explain well, so I went back and kind of updated all the
explanations where things were unclear. So you won’t maybe
notice a big change, but it is like a little bit polished, I
would say like making it smoother, and also adjusting
everything to the latest version of Scikit-Learn. Make sure
everything still works. But then the other real change happened
after in the first TensorFlow chapter where we basically
rewrote the first two chapters on TensorFlow, it was TensorFlow
2.0 in mind. So the first one was more like a general
introduction to deep learning or, or deep learning libraries.
And the second one was on TensorFlow was more like the
mechanics like a little bit more in detail. So that had to be
almost 90% rewritten basically, I think we may be kept some of
the sub sub header had us but except that all the content and
code had to change. So that was basically I would say, most
significant update, and then the scene and an art and chapters
also good an update because we have now the different version
of sentence about which changed a lot of things, especially
around the data loading. And then the next chapter was the
GaNS chapter where we basically brought a new chapter from
scratch also highlighting GaNS. Now we also over the last GaNS,
Wasserstein Gan. But we didn’t go into too much detail in terms
of all the different ganic, ganic architecture because this,
I think, a GitHub repository that has like 200 or 300
different GaNS, so we only focus on the fully connected one,
conditional GaNS and the Wasserstein GaNS. And then in
the next chapter of the reinforcement learning base,
which is maybe the most exciting one because it’s really new.
It’s something I felt was missing in all the previous
versions and people really wanted they said, hey, you have
as an introduction, why, why is there no chapter on
reinforcement learning? Personally, I my research is not
on reinforcement learning. So that was also asked for the
majority of the work, a lot of work to get good examples to
run. So it is not easy to try and reinforcement or lettering
agents. But I hope we did a good job there. And yeah, that’s
maybe my highlight, I would say. Sanyam Bhutani: How did how did
you decide on the topics that you wanted to include? Because
I’m sure there’s this huge number of things happening in
machine learning. How did you narrow down your focus on maybe
skipping a few things, deciding what makes it to the book? Dr. Sebastian Raschka: Yeah,
there is. It’s really hard to say no to certain topics. So but
I thought, I mean, our ends and scene ends at least having a
deep learning chapter on images for one on analyzing text. The
GaNS is very interesting, because like I mentioned these
privacy related projects were involving autoencoders and then
GaNS. So that is also something I wanted to have in there. And
also a lot of people work nowadays with GaNS, it’s across
different application and research areas. So that is a, I
thought something, even if you may not, as a reader may use
GaNS yourself, I think it’s good to know about it. And then yeah,
the reinforcement learning was basically first I was excited
about myself, because I’ve never really worked with it before,
like an application. And then also because people were also
really interested in that. So I thought it’s just doing both of
us a favor. Basically, I get to do something new. And also, the
readers basically get what they wanted, so. Sanyam Bhutani: Okay. Now,
coming to focus of the book, which is on TensorFlow 2.0. And
I noticed the recent I believe, research papers are in the
Pytorch area. And many, especially students struggle
with this question, should I learn Pytorch? Should I learn
TensorFlow, should I learn statistics? Do you have any
insights on what package should they use? Do you punish your
students if they don’t use TensorFlow 2.0? Dr. Sebastian Raschka: So, yeah,
that’s a very good interesting and a really sensitive question.
Yeah, so my research students, they use a little of both. I
think, knowing both and being able to use both is really
helpful. Because if you read research papers, today, maybe a
50-50% chance that it’s either one. And if you want to compare
your methods with other methods, you need to run both basically.
So and usually it’s not as easy as downloading a package and
then running it, usually you have to change a little bit of
make some tweaks, modifications, maybe even part of the
architecture to your language. So in that way, you have to be
able to kind of understand what’s going on. And also what’s
interesting, so yeah, like you mentioned, the book is
TensorFlow 2.0. Sanyam Bhutani: Yes. Dr. Sebastian Raschka: My
research, I also use Pytorch. In my classes, I currently use
Pytorch. But it’s like, I mean, there’s advantages and
disadvantages on both sides. But what I noticed especially both
are kind of converging. So it’s kind of interesting to see is
that TensorFlow started with a study graph paradigm and then
people wanted more like the Dynamic Loss because it’s easier
to the back easier to work with and more familiar with you come
from NumPy So, they added this TF Eager mode which is now
making it also more dynamic graph like PyTorch. In PyTorch
however, people would like Pytorch they were saying okay
this is not so good for production, how do I export my
thing into C++? So, I can put it on my mobile device and whatever
people do. So, that way they can have also the the basic numbers
because they end up know the quantization to make things more
efficient the mobile stuff and also touch script which can kind
of convert if I get this right, the Pytorch code into a kind of
intermediate representation and then that can be exported to C++
basically. Sanyam Bhutani: Yeah. Dr. Sebastian Raschka: So now
Caffe 2 was also known as now part of Pytorch basically under
the hood. So in that way, Pytorch got also static graph
capabilities so that you can export a model to static
representation. And TensorFlow got this eager mode basically so
they kind of converge to the same thing. So right now, I
think it’s more like a matter of preference. They have both
slightly different API’s. But they’re also both user friendly
now. So for TensorFlow yourself, the TF.Keras() now, and
TensorFlow 2.0, before it was compact module nodes, like an
official API and TensorFlow, so also that is easier to use. So
right now, I wouldn’t say there’s really a protocol on
both sides. And because this is like our versus Python is like a
hot debate always. And I think using or knowing both was
useful, because you can see both are kind of equally used in
research. And you have to kind of then the opportunity to make
the best of both worlds basically, Sanyam Bhutani: I think one
thing that also most students miss out on this and I’m sorry,
I haven’t completed the book, but in your book and not taking
aim at any other books, but you try to teach the concepts and
then shows how it’s done in TF rather than here’s how you use
an API. So if you really knew the concept it’s also really
easy to pick up another framework, assuming they’re well
documented. Dr. Sebastian Raschka: No, this
is absolutely right. I think because also PyTorch, it’s kind
of like NumPy, you can, you have basically this obtained support
to you have like this template library under the hood, it just
add an API on top of it, which makes it convenient. But it’s
helpful to understand what’s going on under the hood. And
then you can learn a little bit specific API characters a little
bit different than Pytorch. But under the hood, if you know,
basically all the building blocks, how convolution layers
work and things like that, it’s super helpful to just understand
the whole concept, basically. Sanyam Bhutani: Yeah. Now coming
to another topic that I personally am always interested
in learning about learning. I believe your book is and you are
definitely an example of a person who’s putting out updated
resources on learning, which isn’t the case with most books.
But you’re also an active teacher at UW, what are your
suggestions for future students who maybe aren’t fortunate
enough to take a course at a university and using the, using
your book or online courses to fill the gaps? Dr. Sebastian Raschka: I think,
this is interesting question. I think the online courses have
come a long way. There are a lot of different online courses but
also on documentation has become much better. There are so many
tutorials now out there, back then you had a textbook, maybe
that does explain something and you had to wait maybe a few
years until they update the things like that. Nowadays, I
wouldn’t even go so far and say, prefer maybe even the official
documentation of something if you want to learn about a
specific tool. So my book is more like about learning the
concepts, but there are applications in a certain
language to give you examples, but I would say the examples are
not the core of this. It’s more like learning the concept. If
you want to, for example, learn a specific tool. I think
learning on a high level by book diagram, where you get an idea
what it is about is useful. But then also going to the website
and look what official documentation is out there.
Because in Pytorch for example, they have a lot of tutorials and
TensorFlow that is 2. So these are more in depth, basically,
they help you do certain things. And then what’s very important,
I think, also for learning is to have collaborations with
someone, either open source, but also, they have like this forum,
let’s say the fight partnership discussion forum, or even Stack
Overflow for TensorFlow, where you get feedback basic and see
what questions people have. You kind of communicate because now
so many little things, you may not know that you’re not doing
them right, or something like that, because things are
complicated and having always a second pair of eyes looking over
your code or you maybe help other people. It’s always a good
thing to do to kind of learn. I think it’s a good learning
experience to have also discussions about things why do
you do this and like that and not the other way? It’s like
very helpful, I think. But it also in general, just working
together with people, and even even writing a book basically.
So you learned about something new, and then you write about
it. And there is, I think, also partly how I learned by writing
that down, because then you know, what you don’t know. And
you will automatically be motivated to learn more than you
already know. So that you can basically expand as well.
Because if you only know something on a surface level,
it’s usually not sufficient to write about as well. So it kind
of writing about it encourages you to dig a little deeper, and
then also publish. It’s like a blog post. People have ideas,
additional ideas, like, hey, I noticed you suggest doing this,
but have you thought about doing it this way. And this way, you
kind of stumbled upon new things way more efficiently than
reading everything out there because if you start reading all
the material about the topic, you spend a lot of time reading
the same thing over and over again, because the basics are
kind of covered in the same way in different ways, or different
penetrant offers, but there’s maybe only a very small chunk
that is new. So by reading multiple resources, you spend a
lot of time just reading the same thing. And if you write
about something, and this may be an expert that just points
exactly what you need, and you get them more efficiently,
basically. Sanyam Bhutani: And people
usually get afraid, especially like I can speak at a personal
level, I was afraid that it’s a scary world out there. And it’s
generally the opposite, the machine learning community, even
on Twitter or otherwise is very warm and welcoming. They provide
useful feedback and not criticisive, uh criticizing
feedback most of the times. Dr. Sebastian Raschka: Except
for the Reddit question. Sanyam Bhutani: Hehehe. Dr. Sebastian Raschka: I would
stay away from that maybe, or maybe just reading it, not
commenting unnecessarily. Sanyam Bhutani: I’ve been banned
from it because I keep sharing Chai Time Data Science and
apparently they don’t like; Dr. Sebastian Raschka: Yeah,
it’s I also I don’t think I comment much. It’s just more
like I read the news spacey sometimes, but yeah, it’s a
little bit not so welcoming. Maybe but except as I guess,
yeah, Twitter is generally very welcoming and that is my main
source also for new information. There’s also some newsletters I
subscribe to which are great to just stay up to date on next two
podcasts is so, for newsletters there’s, let me see if I get
this right. The batch I think, this is why and Andrew Ng-he was
doing the Coursera course and he has also newsletter he covers on
some of the recently running stuff. And by Jack Clark from
opening ideas; Sanyam Bhutani: Yes. Dr. Sebastian Raschka: Also is a
good newsletter. And this is always helpful to see like a
brief summary of what’s new and also for staying up to date. Sanyam Bhutani: I’ll try to find
those and have those linked in the description in case anyone
wants to check them out. Now coming to general tips, do you
have any tips for beginners who are just maybe, who just
purchase your book and are listening to this podcast or who
are just getting started in deep learning? Dr. Sebastian Raschka: Oh, yeah,
what I would say is, you know getting started is always a good
thing not getting hung up in details. I think it’s always you
have to find the balance between doing things right the or doing
things like perfect and just getting started. Because if you
I mean, for example, one example is, for example, learning math
is very important. But if you just learn, if you buy five
textbooks right now, one linear algebra, one in calculus, and
maybe someone based in statistics and so forth, and you
read all these books, you will spend like five years without
doing anything may be exciting. I mean, some people will say,
reading these books is already exciting. But what I mean is
doing things alongside is, I think, very motivating. It’s
like picking some project or problem to solve and then
working on it. And that keeps you motivated. Because otherwise
I mean, it’s may be beneficial to just learn all the math up
front, it’s maybe efficient, because then you don’t have to
look things up when you learn deep learning. But I think that
you will have a hard time maintaining your focus because
at some point, you get bored or something like this. So you will
think why am I doing this uh; Sanyam Bhutani: Hehe. Dr. Sebastian Raschka: is
exciting, right? So one thing how I also learned a lot of that
same recording practices with Pandas is that picking a project
that is kind of interesting or cool that I liked. It was fun.
So in my case, I was doing fantasy sports predictions. So
there was, I don’t want to say those disappear website, but it
was a website where you could, on weekends submit, basically
your roster for Premier League Soccer, basically. So you left
it on 11 players for your team. And it was basically a
constraint optimization problem, basically. So you had a certain
budget and you could buy any certain players and they got
scores based on how well they did in the real world. And you
also had injuries and things like that and you wanted to
basically maximize the number of points given your salary. So in
that way, there was a lot of data set, is was kind of
exciting because I like soccer. It was interesting to watch
soccer and observe and then tinkering with the data.
Collecting real data from the web, basically, and writing my
scripts to automate everything and using machine learning to
make the predictions. That was basically very exciting. I had
some project where when I learned about something, I kind
of implemented this into my framework there and experimented
with this. I thought that was, what keeping, uh kept me
motivated basically. Sanyam Bhutani: I think finding
your passion project is also a secret to not getting frustrated
while your model isn’t converging is important,
especially in this field. Dr. Sebastian Raschka: Yeah,
it’s, I think, very important, because otherwise, yeah, it’s I
would say it’s hard to maintain the focus. Yeah, so finding
something you’re excited about is always a good idea. Sanyam Bhutani: Yeah. Now, this
has been a great interview. My final question to you is
generally speaking about the field, I believe you were also
one of the moderators on archive, and you probably would
definitely, I think you’d be seeing a lot of people’s
everyday. Do you have any insights of where the field is
headed, and how can we stay up to date with this huge overflow
of trends and literature of the Sesame Street and things that
keeps shifting every year? Dr. Sebastian Raschka: Yeah, so
yeah, the moderation process is pretty interesting. So, once I
think I should say about this is we don’t review papers, we just
check that they have the right category because you know, if
you categorize something as machine learning or computer
vision and so forth, they also actually to machine learning
categories. One is the computer science, the machine learning
and the statistics machine learning, but they are cross
reference if some article gets published in or uploaded in one
category, the other category is automatically assigned. So when
so we are basically three moderators for the machine
learning, one in computer science, from Dietrich, with lab
and I and we kind of share the work we don’t so I don’t do this
every day, but two to three times a week. And every day,
there are at least one to 200 new papers uploaded. So this is
like a huge amount of information going on archive
just machine learning. And yeah, so it’s impossible to keep up
with everything. So basically, you read the headlines. But what
I can tell you your question was basically, what’s maybe
currently the trend what I would say. So you see a little bit of
everything. But certain things come up more frequently. Now, I
would say, topics related to self supervised learning. So
self supervised learning, I think one of the hottest trends
recently. So self supervised learning is basically you do
supervised learning, but you use information that is in the image
or in the data already. For example, if you have images,
what you can do is this jigsaw example where you chop up images
and two different sub images, you shuffle them, and you have
the network, predict the right order of limiters to reassemble
it. And I mean, if you think about it is kind of a supervised
learning problem. But you can do this with unlimited data because
you can just generate your label, label specific order the
pieces are supposed to be in. So I think it’s very powerful for
pre training your networks basically. So the other one,
it’s kind of also one student of mine is working in that area,
that is very popular is graph neural network. So recently
we’ve seen a lot of new projects on graph, neural networks, first
method development, but also a lot of applications to social
network graphs, but also computational biology,
everything related to molecule, it’s basically rep discovery. So
yeah, last week, we, soon when we worked on a review article,
where we were, what was the executive it was basically on
machine AI and machine learning based methods for ligand,
bioactive ligand discovery and GPR ligand recognition, can
maybe also put this in the show not if people are interested in
that but also the main thing that came out of it when we were
reviewing recently learning methods were they are more all
mostly graph based. So that is, it’s actually very interesting
because traditionally, machine learning was only good for text
and images but I mean except text and images, there are so
many other types of problems. Special edition biology. So
extending our toolkit of deep learning towards autographs,
it’s I think it’s very cool to see. Yeah, so these are, I would
say, most, I would say, the hottest ones. But also, I see a
lot of papers. This is a very important topic about fairness
in AI. So there are many, many different tools about
interpretability and fairness in AI and how to diagnose problems
and how to visualize even what the network is doing, basically,
that’s also very, very important and hot topic right now. Sanyam Bhutani: What best
advices do you have for someone who wants to keep up with this,
use overflow people do? Should they go to archive and see every
paper that’s being uploaded every day? Or how should they go
about it? Dr. Sebastian Raschka: Yeah,
there is a good point I saw you keep up. I think the two
newsletters I mentioned they are kind of sub-filtering things. So
that is helpful. There’s the archive, sanity server. So but
yeah, this is if you have a specific research area,
periodically, maybe checking related articles basically, to
see basically what what is company related to, there are
some new articles. The Google on Google Scholar, there’s also
this recommendation alert feature, which is one one little
tool in your toolbox that also can help you stay up to date.
Yeah, and the one where I were, we were just talking about where
we wouldn’t recommend commenting in it, because it may be a
little frustrating. But reading the Reddit machine learning read
is maybe also just reading what new research articles are.
There’s also may be nice because people pre select by voting nice
research articles, like mostly the general ones. So all these
resources are very helpful for keeping up to date. But yeah,
it’s more like a general section of the field. If you are more
specific than that, I think you need to still use search engines
like Google scholar to find related work. Sanyam Bhutani: I think the
Twitter community is also great and the recommender engine also
as on top of it. If you follow your favorite researchers and
they like a certain post, it always pops up at the top and
maybe in the first three or four tweets you can find your paper
to read for the day. Dr. Sebastian Raschka: Yeah, no,
Twitter is I think, maybe also the social website where are
media website. The only one I’m really using and my favorite one
by far because yeah, there you can really use what I like about
this, you know, the people your work. I mean, it’s not like
anonymously, you at some point, you know, different interests by
people and you have your audience and so basically, you
can retail it to your interest base, which is nice. Sanyam Bhutani: Yeah. Now, so
last thing before we end the call, what should be the best
platforms for the listeners who want to follow you and follow
your work? Dr. Sebastian Raschka: Um, yeah,
I think that would be Twitter. That is an easy one. Sanyam Bhutani: Could you spell
out your Twitter handle? I’ll have it linked. But in case
anyone who’s lazy to scroll you into the bottom, Dr. Sebastian Raschka: So yeah,
my Twitter handle is uh, RASPT. So ‘raspt’ – RASPT. So yeah, the
name is a little bit weird, but I think I wanted to do something
that was related to my name, but all the short ones were gone
back then I think like that, but back then it was really cutting
into your character limits. I want to do something short. It’s
just for rational Sebastian. So that’s where it’s coming from,
anyway. Sanyam Bhutani: Okay, awesome.
Thank you so much for joining me on the podcast. And thank you so
much for your contributions, even to the open source, the
research and even for creating the book. Dr. Sebastian Raschka: Yeah,
thanks for the interview. That was actually really fun. I
really enjoyed it. And it’s right now on Monday, so that was
a great start. I wish I can start every week like that. Sanyam Bhutani: It was an honor
to have you on the show. Thanks so much. Dr. Sebastian Raschka: Thank
you. Bye bye. Sanyam Bhutani: Thank you so
much for listening to this episode. If you enjoyed the
show, please be sure to give it a review or feel free to shoot
me a message you can find all of the social media links in the
description. If you like the show, please subscribe and tune
in each week to “Chai Time Data Science.”

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