6 Years of Studying Machine Learning in 26 Minutes

Summary

This video script narrates the journey of a machine learning enthusiast, detailing their six-year transformation from a computer engineering student to a research scientist at a favorite AI startup. The speaker shares their academic and professional experiences, including initial struggles, pivotal courses, research projects, and the process of landing their dream job. The script also highlights the importance of continuous learning, making informed career choices, and avoiding common beginner mistakes in the field of machine learning.

Takeaways

• 😀 The speaker's journey in machine learning started with an interest in physics and math, leading to a Computer Engineering degree at TU Berlin.
• 📚 The initial years of university were challenging, focusing on foundational courses like linear algebra, calculus, and differential equations, which later became crucial for understanding machine learning.
• 🔧 The speaker's first job as a student researcher in an optical physics lab helped build a strong foundation in experimental work and programming, but also highlighted the importance of seeking new challenges to avoid stagnation.
• 💡 The discovery of AI courses in the fifth semester marked the beginning of the speaker's deep dive into machine learning, starting with reinforcement learning.
• 🎓 The bachelor thesis on deep reinforcement learning for autonomous robotic navigation was a significant step, despite the initial struggles and the steep learning curve.
• 📈 The transition to a Computer Science master's program allowed the speaker to focus exclusively on machine learning, taking advanced courses and engaging in more complex projects.
• 🤖 Working on robotics projects and publishing a paper at a top conference like IROS was a major milestone, showcasing the speaker's growth and capabilities in the field.
• 👨‍🏫 The speaker's experience of switching teams within the research institute to work in the AI department highlights the importance of proactively seeking opportunities to learn and grow.
• 📘 Reading papers and learning from them independently became a habit that contributed to the speaker's development as a researcher.
• 🚀 The pursuit of internships and jobs at top companies, including rejections and learning from them, demonstrated resilience and a commitment to continuous improvement.
• 🌟 The speaker's eventual success in joining a favorite AI startup as a research scientist after years of hard work and learning emphasizes the value of persistence and incremental progress.

Q & A

What was the speaker's initial academic background before getting into machine learning?

The speaker initially studied Computer Engineering at TU Berlin, taking courses in linear algebra, calculus, differential equations, and basic programming in C.

How did the speaker's interest in machine learning begin?

The speaker's interest in machine learning began during their fifth semester when they chose their first AI course, which was split into two parts: old school AI and reinforcement learning.

What was the speaker's first experience with a machine learning project?

The speaker's first machine learning project was their bachelor thesis, where they worked on deep reinforcement learning for autonomous robotic navigation.

What was the speaker's first job as a student researcher, and how did it relate to their later work in AI?

The speaker's first job as a student researcher was at an optical physics lab, running experiments with optical fibers. Although not directly related to AI, it provided them with basic programming skills that later became useful in machine learning.

How did the speaker's experience with coding evolve throughout their studies?

The speaker started coding in C during their computer engineering studies, then moved to Java for data structures and algorithms, and later used Python for machine learning projects, including using libraries like PyTorch.

What is Codium, and how did the speaker use it to improve their coding efficiency?

Codium is a free coding assistance tool similar to GitHub Copilot. The speaker used it to refactor and explain existing code and to write appropriate functions using context from their entire project.

What was the speaker's approach to learning new machine learning concepts?

The speaker learned new machine learning concepts through a combination of university courses, self-study using YouTube videos, reading papers, and hands-on projects.

How did the speaker's job change when they switched teams within the research institute?

The speaker transitioned from working in an optical physics lab to joining the AI department, where they started working on data engineering and gained more experience with machine learning.

What was the speaker's experience with internship applications, and what did they learn from it?

The speaker faced rejections from several top ML internships but learned the importance of persistence and self-improvement. They eventually secured an offer from their favorite AI startup.

What is the speaker's advice for beginner ML students to avoid common mistakes?

The speaker suggests that beginner ML students should watch a follow-up video where they share seven common mistakes to avoid, emphasizing continuous learning and improvement.

What was the speaker's final decision regarding their academic and career path after completing their master's degree?

The speaker decided to join their favorite AI startup as a research scientist instead of pursuing a PhD, although they had an offer for a PhD position.

Outlines

00:00

🎓 Journey to Becoming an AI Research Scientist

The speaker reflects on their six-year journey in machine learning, starting as a Computer Engineering student at TU Berlin with no knowledge of AI. They describe the initial struggle with foundational courses like linear algebra and calculus, which later became crucial for understanding machine learning models. The speaker also shares their first steps into the tech industry through a student researcher role at an optical physics lab, emphasizing the importance of continuous learning and the steep learning curve of new jobs.

05:02

📚 Transitioning from Academics to Practical Machine Learning

The speaker discusses their transition into machine learning during their third year at university, where they took their first AI course, which included reinforcement learning. They recount the challenges of their bachelor thesis on deep reinforcement learning for robotics, which involved self-teaching and overcoming hardware setup difficulties. The paragraph also highlights the speaker's first publication at a top robotics conference, IROS, marking a significant milestone in their academic career.

10:03

🔬 Balancing Theory and Practice in Machine Learning

In this paragraph, the speaker delves into their fourth year, focusing on machine learning courses and projects. They mention taking a classical machine learning course, learning about various algorithms, and gaining practical experience through coding assignments. The speaker also talks about their experience working at a research institute's AI department, where they learned data engineering and gained more experience with PyTorch. They emphasize the steep learning curve and the excitement of working on new ML domains like computer vision for medical image analysis.

15:05

🚀 Pursuing Advanced Studies and Research in Machine Learning

The speaker recounts their graduate studies, where they took advanced courses in deep learning and computer vision, and worked on various projects, including one that was published but never successfully. They express a desire for a more dynamic learning environment, leading to a job change to work on graph neural networks. Despite the initial rejection, they eventually joined the ML department, where they continued to learn and grow in the field of AI.

20:08

🛠️ Overcoming Challenges and Exploring Multimodal Learning

The speaker describes their quest for a more challenging and dynamic work environment, leading to applications for internships at top tech companies and startups. They recount their experiences with interviews and the process of finding a professor for their Master's thesis in multimodal learning. The paragraph highlights the importance of persistence and the pursuit of research interests, even when facing rejections and setbacks.

25:09

🌟 Securing a Position at a Dream AI Startup

In the final paragraph, the speaker shares their decision to apply for research scientist positions despite the risk of rejection. They detail the application process for a favorite AI startup and their surprise at receiving an invitation for an interview. The speaker reflects on their growth and the importance of daily improvement, ending with the announcement of their upcoming role at the AI startup and an invitation to watch a follow-up video on common mistakes for beginner ML students.

Keywords

💡Machine Learning

Machine Learning (ML) is a subset of artificial intelligence that enables systems to learn from and make decisions based on data. In the video, the speaker's journey into ML began with an initial interest in computer engineering and physics, evolving into a deep dive into ML research and application. The term is central to the video's theme, as it encapsulates the speaker's academic and professional progression, from studying basic math and computer science to working as a research scientist in AI startups.

💡Research Scientist

A Research Scientist is a professional who conducts research in a specialized field, often contributing to the advancement of knowledge and technology. In the script, the speaker's goal to become a research scientist in an AI startup is a significant milestone, representing the culmination of years of study and work in ML. The role is integral to the video's narrative, illustrating the speaker's career aspirations and achievements.

💡Computer Engineering

Computer Engineering is a discipline that integrates elements of electrical engineering and computer science to design, develop, and test computer hardware and software. The speaker's initial foray into tech was through studying Computer Engineering at TU Berlin, which provided foundational knowledge in math, coding, and electrical engineering, setting the stage for their later specialization in ML.

💡Mathematics

Mathematics is the abstract science of number, quantity, and space, often used to model and solve real-world problems. In the video, the speaker emphasizes the importance of math as a fundamental skill for understanding ML concepts. Early struggles with math courses like linear algebra and calculus are mentioned, highlighting the necessity of a strong math foundation for advanced ML studies.

💡Coding

Coding, or programming, is the process of writing computer programs to perform specific tasks. The speaker's journey involved learning to code in various languages like C and Java, which is essential for implementing ML algorithms and models. Coding is a key skill in the video, illustrating the practical application of computer science knowledge in ML projects.

💡Reinforcement Learning

Reinforcement Learning (RL) is a type of ML where an agent learns to make decisions by performing actions in an environment to maximize a reward. The speaker's interest in RL began with a university course and culminated in a bachelor thesis on the topic. RL is a significant concept in the video, showcasing the speaker's early foray into a specialized area of ML.

💡Deep Learning

Deep Learning is a subset of ML that uses neural networks with many layers to learn and represent data. The speaker's introduction to deep learning was through a project on autonomous robotic navigation, which required learning about neural networks from scratch. Deep Learning is a pivotal concept in the video, marking a transition from traditional computer science to cutting-edge AI research.

💡Convolutional Neural Networks

Convolutional Neural Networks (CNNs) are a class of deep learning algorithms widely used in computer vision tasks. The speaker's first encounter with CNNs was during a project course on medical image analysis, which involved learning about this technology to detect anomalies in 3D brain images. CNNs are a key term in the video, representing the speaker's expansion into the field of computer vision.

💡Multimodal Learning

Multimodal Learning is an area of AI that involves processing and understanding data from multiple modalities, such as text, images, and video. The speaker's interest in multimodal learning was sparked by the desire to work with AI that can understand and process various types of data. This concept is central to the video's narrative, as it led the speaker to choose a professor and a research direction for their thesis.

💡Technical Papers

Technical Papers are scholarly articles that present research findings, methodologies, and results in a technical field. Throughout the video, the speaker's learning process involved reading and understanding technical papers, which is a common practice in the scientific community for staying updated and advancing knowledge. The term is used to illustrate the speaker's academic development and research contributions.

💡Internship

An Internship is a temporary job opportunity that provides practical experience in a professional setting. The speaker's attempts to secure internships at top ML companies like Amazon and DeepMind, despite facing rejections, demonstrate the competitive nature of the field and the speaker's determination to gain industry experience. The term is used in the video to highlight career development and learning opportunities.

Highlights

Six-year journey in machine learning, starting from an ML student researcher to joining a favorite AI startup as a research scientist.

Initial introduction to technology through a combination of interest in physics, math, and the practicality of engineering.

Foundation in computer engineering at TU Berlin, with early struggles in understanding the relevance of mathematical concepts.

The importance of math skills as a fundamental for later machine learning understanding and intuition.

First job as a student researcher in an optical physics lab, highlighting the initial steep learning curve and eventual plateau.

The transition from computer engineering to focusing on machine learning during the third year of university.

First exposure to AI through a course on reinforcement learning, which sparked interest in machine learning.

Bachelor thesis on deep reinforcement learning for autonomous robotic navigation, marking a significant step into deep learning.

The experience of publishing the first ML paper at a top robotics conference, IROS.

Switching to a pure computer science master's program to delve deeper into ML, facing challenges with engineering and training ML models.

The process of learning to read and understand ML papers, a skill that became essential for research.

Internal job switch within a research institute from physics lab to an AI department, highlighting the importance of persistence.

Exploration of computer vision and medical image analysis, introducing the challenges of learning new domains.

The realization of the need for continuous learning and improvement in ML, as well as the decision to pursue a PhD.

The pursuit of internships and the challenges faced in the competitive tech industry, including rejections and learning from failures.

Finding and working with a professor specializing in multimodal learning, emphasizing the importance of finding the right mentor.

Research on video moment retrieval in the field of multimodal learning, achieving state-of-the-art performance.

The decision to join a favorite AI startup as a research scientist, illustrating the culmination of years of hard work and dedication.

Reflection on the importance of daily improvement, enjoying the work, and being proud of achievements in the ML field.

Transcripts

I've been studying machine learning for

the past 6 years in which I worked as an

ml student researcher for over 2 years

and have written my first three papers

my journey started studying Computer

Engineering not knowing what ml was or

even that it existed to where I am now

soon joining my favorite AI startup as a

research scientist in this video I'll be

sharing my experience over these six

years and explain what I did each year

to get where I am now things like what

to expect for the first few years what I

did to get my first ml student roles and

most importantly what you should be

avoiding and trust me there's a

lot okay before we get to years 1 and

two how did I get into Tech well young

Boris liked physics and math in high

school and thought hm with physics you

can't really make money so I need to do

engineering

I.E applied physics building a robot

would be really cool but then I also

need to know how to program the robot to

make it do the stuff I wanted to do at

that time I didn't know Ai and ml

existed but those were my thoughts those

led me to studying Computer Engineering

at the TU Berlin the first two years

were really tough of course I had to

take the standard courses like linear

algebra 1 Calculus 1 and two and a

course on differential

equations luckily I genuinely enjoy

learning math but that doesn't mean it

was easy for me in the beginning it all

doesn't make much sense and you don't

know why you are learning all these

mathematical formulas and Abstract

Concepts but I promise you at some point

most of them will make sense and you

will learn to appreciate and make use of

them especially when learning ml the

basics of these math skills will be the

fundamentals you will later need for ML

and give you an intuition for how to

look at ml models in a mathematical

sense but back then again I didn't even

know AI existed I had a lot of

electrical engineering and even some

Physics courses those were more tough

but I also had my first computer science

courses and learned how to code in C yes

in C that's right remember I was a

computer engineering major so my program

was designed for low-level coding and

electrical

engineering but I still had the standard

courses on data structures and

algorithms in Java and also a course on

theoretical computer science all that is

pretty much the standard things you

learn when getting into computer science

related programs some CS Theory and a

lot of coding luckily coding is much

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but now let's get back to what I did

besides normal college courses Landing

my first student researcher job at an

optical physics lab about 6 months into

my first year I wanted to somehow boost

my resume earn some money to survive

college and also just learn more stuff I

then saw this listing at a research

institute directly next to my uni and

applied it was honestly quite surprising

that they invited me to an interview

because I literally had not much to

offer except basic programming skills

but I guess for the job I was supposed

to do it was enough I was responsible

for running a lot of experiments with

Optical fibers and doing measurements

when starting a new job or project the

learning curve will likely be very steep

which is amazing I learned a lot but if

you do the same measurements for over

one and a half years the learning curve

plateaus and the job becomes boring in

total I stayed at this job for 3 years

and this learning curve was completely

flat after perhaps 8 to 9 months if not

less and this was a big mistake I really

should have at least changed to a

different team at this Research

Institute after a year but I was quite

exhausted for these first two years I

slept 6 hours at night didn't do much

Sports and just worked a lot which is

normal I don't want to PL I had a lot of

fun in fact I am happy and proud I did

all that but yeah all of this happened

in my first two years of uni most

importantly I learned the basics of math

and computer science and worked as a

student researcher all of which helped

me with studying machine learning

without even knowing ml

existed I finally got into the third

year semesters five and six where I

could choose some of my courses myself

this fifth semester is where I saw that

AI courses existed at my uni and is

where I chose my very first AI course

this is where the ml Journey really

started that said this AI course was

split into two parts the first one was

about old school AI not machine learning

yes AI does not necessarily mean ml if

you have an algorithm with a set of

rules to make decisions it's effectively

AI I learned about things like the

strips method looking back it's not that

exciting honestly but that is where I

started and back then I thought it was

decently cool but the second half of

this course was really cool the second

half was about reinforcement learning

which in retrospect is a weird start

into ml learning about RL before even

knowing what a newal network was but

maybe this is a good way to show you

that it does not really matter how you

start if you keep going you will learn

all the fundamentals anyway just in a

different order perhaps but I would

still not recommend it if you have the

option to choose but you know you you

get the point anyway I learned about

things like Bandit Theory Monte Carlo

research marof decision processes and

finally RL algorithms like Q learning so

in my fifth semester 2 and a half years

into college there was still not that

much ml but these RL lectures really got

me interested in ml and especially RL

that's why I wanted to do my bachelor

thesis in RL which is what I did in my

sixth semester I worked on deep

reinforcement learning for autonomous

robotic

navigation this was a complete cold

start into deep learning I didn't even

know what a new network was I had to

learn all of that on my own through

YouTube videos even worse in the

beginning I struggled a lot to even get

the hardware set up and when I reached

out to my supervisor for help he said he

thought I might not be ready for this

thesis and I had two weeks to prove him

otherwise and if I failed he would have

to drop the thesis with me which would

have been so bad the semester had

already started and then I would have to

look for a completely new one but I

pushed through

and made it this thesis project was a

lot of work a lot of engineering work

and no real training itself since the

thesis was more on the deployment side

of DRL agents than the training side

nevertheless I still learned a lot of

core coding skills like debugging and

did get to learn pyo for the first time

so my final Bachelor year was still a

slow step into the world of ml but a

very firm one one that set the path to

going all in on ML which is why I then

switched to a pure computer science

master so my fourth year began and I

went all in on ML I selected only ml

courses and projects but this of course

came with a lot of challenges in my

first graduate semester I pretty much

had one big course and one big project

for the project I continued to work on

the same team for autonomous robotic

navigation that I worked with during my

bachelor thesis the project was still

more of an engineering effort because we

built a benchmarking suite for

autonomous robots which again came with

a lot of failing and debugging but this

time I could focus a lot more on

training our own agents using pytorch

and had to start reading papers to learn

things like Po of course the beginning

of reading papers is always a bit tough

because you have to get used to the

lingo but I felt so cool I felt like a

real scientist the really cool thing was

that later that year we actually

published this work to one of the two

best robotics conferences iros that was

so huge for me it was my first ml paper

and it was even published at a top

conference now alongside this project I

had my first real ml course I learned

all the basics of classical machine

learning for example what is supervised

learning unsupervised learning what is

the bias variance trade-off what are

methods like linear regression decision

trees support Vector machines K means

PCA boosting and Ensemble learning and I

learned about all the basics of new

networks like what loss functions grade

and descent back propagation and

regularization are alongside each

lecture there were of course practical

homework assignments to implement the

ideas we learned during the lecture and

those were again using py do now besides

uni I still had this boring physics lab

job at this point I was working there

for two to two and a half years already

but the cool thing was the research

institute I worked at also had an AI

department so I wanted to internally

switch teams I applied got an interview

and was rejected I mean I get it I was

just starting my first real ml course

and had no theoretical knowledge of any

of the ml fundamentals so I tried again

half a year later after completing the

ml course and having gathered more basic

pyto experience and then actually did

get the job what an amazing start to my

second graduate semester my second half

of my fourth year I started my work as

an applied scientist student researcher

in the ml Department I again had a steep

learning curve and was so excited to get

to work these first six months I started

working on a lot of data engineering

mainly using pandas which I have never

used before I learned a lot there and at

Uni I also focused on Purely practical

learning I took two project courses I

again continueed to work on this

robotics project but at this point I

felt a bit more of a fatigue working on

the project it wasn't that exciting

anymore but it still was a lot of work

and my learning curve PL toed but I

contined to work on it because I hoped

for another paper nevertheless I started

to look at other cool ml domains and

took another project course a project on

computer vision for medical image

analysis this was my first computer

vision project and I had to detect

anisms in 3D images of the brain it was

really cool but I have never dealt with

computer vision before and had never

learned what a convolutional new network

was so the learning curve was again very

steep I had to learn all of that

knowledge Myself by watching YouTube

videos and reading more papers in the

end the final project was not the worst

but also not the best either at least

looking back at it now and I think this

is a good thing if you are looking back

at Old projects and think they are bad

and and perhaps even cringing because

you would have done things differently

with your current knowledge then you

have gotten

better so yeah this year was packed with

all the ml I could fit in most of it was

actually working on ML projects and only

taking one ml lecture but a really

important one so far it was quite

straightforward but in my next year I

had to make some important decisions

now uni continued as usual but

career-wise I had to make those

important decisions in my third graduate

semester I again took one lecture course

and two more projects I took my first

actual deep learning course which had a

decent overlap with my first ml course I

again learned about the same

fundamentals of new networks but now

also had lectures on cnns recurrent new

networks Auto encoders and a bit of

explainable AI so nothing too crazy

right at this point I am really into AI

myself and I started watching paper

review videos on YouTube and reading

random papers on my own perhaps because

this course didn't have too much new

stuff and my job didn't teach me much

theoretical content as well but anyway

this habit of reading papers and

learning stuff on my own are things I

still do to this day and that I I

genuinely enjoy so besides this deep

learning lecture I once again worked on

this robotics project and I have to say

working on it this semester just wasn't

necessary it was really not that

interesting anymore and I really just

wanted to learn new stuff but I was

still hoping for a paper which in the

end was never successfully published now

my second project course this semester

on the other hand was again about

reinforcement learning but was amazing I

had to thoroughly read a paper and

actually reimplement it and reproduce

its results which was a lot of fun I

often say it and I'll say it again

reimplementing a paper and recreating

its results is one of my favorite

projects to recommend I even wrote a

blog post about it and submitted it to a

top ml conferences blog post track but I

didn't really know how the process

worked back then and

I did get my reviews but never received

an email telling me that they were

released so when I randomly checked I

saw the reviews and that I never

responded to them thus the article was

rejected from the icr blog post track

nevertheless the project taught me a lot

and at this point I was pretty confident

I wanted to become a top ml researcher

and this goal meant for me I needed to

strive for the best companies my job at

that time as a student researcher was

not completely plateauing but also not

the best anymore we started doing

research on graph newal networks but for

over a year now we were still stuck with

a lot of the same boring data

engineering and feature engineering I

effectively didn't really learn anything

new that's why I wanted to find a new

job and not make the same mistake as

before where I stayed for three years at

the same job so I applied to dozens of

top ml internships and I actually got

invited to an interview for a applied

science internship at Amazon that was my

first real Tech interview it was really

exciting except that I failed miserably

the more frustrating part was that the

questions were really not that hard it

was a rapid fire basic ml questions

interview they were literally asking

about the content of my first ml course

I mentioned before the one I completed

not even a year ago but well life goes

on and I got another interview at a cool

startup called nuro this time it was for

an ml engineering internship and the

first interview round was a coding

interview again something completely new

to me I prepared using lead code but

when I saw a blank code in canvas and no

pre-existing code where just just had to

fill in an algorithm I was so scared I

failed miserably again well the

applications weren't going so well I

simply didn't get many more interviews

so I changed my Approach I directly

reached out to a Google Deep Mind

researcher I found interesting and asked

for an internship and he got back to me

we had an interview call where I felt it

went decently well but I got rejected I

was done with looking for internships

and focused on finding a new job as a

student researcher where I could also do

my Master's thesis I decided I had

enough of reinforcement learning and

found computer vision really cool but

then I thought how cool would it be if

you could talk to an AI about images or

even

videos well that's where I decided

multimodal learning was really cool but

at my University there was a problem

there were no professors working on

multimode learning and pretty much all

of the professors were how do I say it a

bit more old school and not that much

into the new stuff there definitely were

one or two don't get me wrong but they

just weren't into something really

similar to multimod learning so I looked

outside of my uni tiim I wanted to look

for a professor that was a bit more

active and ambitious I read multimode

learning papers and looked at the

authors I then Googled them to see if

they could be an option as an adviser

for my research and thesis and then I

found the perfect Professor he was young

was just about to start as a professor

and before that he was a postor at UC

Berkeley and a researcher at meta and he

worked on multimodal learning he was

everything I was looking for long story

short I am so happy to have gotten the

job and started to work with him later

in my final year I still had my goal of

getting to Big Tech but there are these

nice sayings Rome wasn't built in a day

and all roads lead to Rome I.E

everything takes time and there are

multiple ways to get where you want to

get so all in all this semester besides

this career hassle I just did a lot of

coding at my job for the robotics

project and for this RL paper

reimplementation but this was still just

the first half of my fifth year my

second half was not that eventful since

I failed all my applications for summer

internships I was still doing my best to

learn stuff at my at the time current

job otherwise not much interesting stuff

happening there and at Uni I really

focused on computer vision I took a

course on automatic image analysis and

another seminar course on deep learning

for computer vision where I had to read

several papers on self-supervised

learning and presented them to the group

that was so much fun I just really enjoy

reading papers I even made my

presentation into a mini YouTube series

on self-supervised learning but besides

those two courses I took my second

General deep learning course this one

was finally a bit more advanced I

learned about things like representation

learning self-supervised learning

Transformers Gans diffusion models graph

new networks and even neural ordinary

differential

equations and finally I also did another

computer vision project course where I

wrote a paper/ techical report on so

there was way more theoretical content

this semester but still a practical

project now you might have noticed that

this semester usually should have been

my final semester usually the Masters

would end after 2 years but I had

actively decided to give myself one more

year mainly to have one semester for an

internship and one more for my thesis so

this semester was my last one with

courses and since I didn't get an

internship I had one more entire year to

focus on doing research with my new

professor and then completing my

Master's

thesis and that is what I did in my

final year

I was finally done with uni at least it

felt like that because I had no more

exams I started working as a student

researcher with this cool professor and

started doing research on multimodal

learning specifically video moment

retrieval I read a lot of papers

developed a model that achieved new

state-of-the-art performance on the

benchmarks I evaluated on and wrote a

paper on it in a very short time I even

submitted the paper to a top conference

and I'm telling you those were some

stressful weeks but it still recently

got rejected and to be honest I probably

understand why I rushed it because we

chose a deadline that was simply way too

close I should have taken more time and

just submitted it to a later conference

so that the paper was overall more solid

although it was annoying I will continue

to improve this work and soon submitted

to another conference then I remembered

that I'm still in my final year I still

need to actually complete my degree LOL

that's why I'm currently still in the

process of finishing to write my thesis

and handing it in but since this is my

final year I also had to think of what

comes next I thought to myself either I

skip the PHD and become a researcher at

a top lab or I do my PhD D I mean How

likely was it to skip the PHD the cool

thing was I already had an offer from my

professor for the PHD position and I was

very happy to accept it nevertheless I

still want to try out applying to two

companies as a research scientist one

was deep mind and although I thought my

chances were in fact decent because I

had exactly the combination of different

experiences that they were looking for I

got rejected but besides deep mind I

applied for another really cool AI

startup my favorite one to be precise I

knew I wouldn't even get invited to an

interview but one evening I was like why

not they won't invite me

anyway but you probably already know

where I'm going with this they did

invite me and I was shocked the

application process was quite tough and

I wanted to really give it my all and

see if I am good enough for them and

well long story short I did get an offer

and will work for them starting in a few

months at the time of this recording

once I start my work I will announce

which company it is don't worry I just

want to make it cool because for me it

is a big thing but yeah anyway

throughout all these years there was a

lot of struggling but also some

occasional successes I quickly learned

that the important thing is to keep

moving think some people get to where I

am now in less time and some in more but

that doesn't matter what matters is that

you try to improve every day by 1%

overall enjoy what you do and that you

are proud of what you do nevertheless

there are many mistakes you can avoid

and not waste any time on if you simply

know what they are that's why you might

want to watch this video next I there

share seven common mistakes beginner ml

students make every year happy learning

and bye-bye