Jesper Dramsch

Verified
  • @jesper
  • Machine Learning [PhD & Job] · Data Science [81/100k+] · Creator
  • they
  • Edinburgh, Scotland
 📝 𝗧𝗵𝗲 𝗖𝗹𝗶𝗳𝗳𝗡𝗼𝘁𝗲𝘀:
Moin! I'm Jesper, a recovering geophysicist that ventured into machine learning. I love telling stories with data and challenging assumptions. I'm good at public speaking, writing and figuring out tough stuff and making it accessible from my experience in the field, academia and the industry.

📜 𝗧𝗵𝗲 𝗣𝗿𝗼𝘀𝗲:
When other children played with mud, I put it under a microscope.🔬
I discovered my love for science when I was 9 years old. I asked all my relatives and friends to bring me sand samples from vacation and analyzed them with a sieve and microscope, wondering if I could just take any sand and put it into an hourglass. Today, I hold a Bachelor's degree in Geophysics/Oceanography, a Master's degree in Geophysics, as well as a PhD in Machine Learning for Geophysics from the Technical University of Denmark (#103 QS) in collaboration with Heriot-Watt University (#301 QS) in Edinburgh, Scotland.

Iɴ ᴍʏ Bᴀᴄʜᴇʟᴏʀ, I used multi-parameter stacking for seismic interpolation.
Iɴ ᴍʏ Mᴀsᴛᴇʀ, I compared those stacks to diffraction imaging and the standard workflow.
Iɴ ᴍʏ PʜD, I accomplished a few things: I was the first to publish on transfer learning for automatic seismic interpretation. I was part of the team that first tried and published on GANs for seismic inversion. I reviewed the 70-year history of machine learning in geoscience as a book chapter in Advances in Geophysics. I adapted an MIT algorithm for 4D seismic time shift extraction that does not rely on biased ground truth data.

Tᴏᴅᴀʏ I share my love for data science and ML in my newsletter, blog, and Youtube. I teach data science and Python through Skillshare, Humble Data and speaking gigs. Oh, and I'm a full-time Machine Learning Engineer.

🔓 𝗦𝗼𝗺𝗲 𝗮𝗰𝗵𝗶𝗲𝘃𝗲𝗺𝗲𝗻𝘁𝘀 𝗜 𝗮𝗺 𝗽𝗿𝗼𝘂𝗱 𝗼𝗳:
💌 Weekly Newsletter (dramsch.net/newsletter)
🏫 Two Skillshare Video Courses on Data Science (Over 1000 Students)
📓 Multiple Book Chapters (ML Review in Geoscience)
🎓 PhD in Applied Machine Learning (Full Open Source)
📊 Kaggle Kernels Expert and TPU Star (Top 81/100,000+ Worldwide)
🏆 Subsurface Hackathon - Execution Prize 2017 (Gan-based Inversion) / Winner of Show 2018 (t-SNE App)
ℹ️ Team Leader and Gold Member at Copenhagen Volunteer Corps
🎤 Public Speaker (500+ Audience Members)
🎤 Scientific Conference Presentations (200+ Audience Members, Largest Recorded Audience at EAGE) 
Read more
Positions

Scientist for Machine Learning

  • ECMWF
  • Jul 2021 - Present

Skillshare Instructor

  • Skillshare
  • Aug 2020 - Present

Newsletter Editor

  • Late to the Party
  • Aug 2020 - Present

Youtube Creator

  • YouTube
  • Apr 2019 - Present

Machine Learning Engineer

  • GMV NSL
  • Sep 2020 - Jun 2021

Postdoc

  • Technical University of Denmark
  • Nov 2019 - Feb 2020

PhD Candidate

  • Technical University of Denmark
  • Oct 2016 - Nov 2019

👉 Start here

✨ Awesome McAwesomesauce

11 Highlights

💌 Newsletter

20 Highlights

📢 Soapbox Shenanigans

12 Highlights

🐍 Parselmouth 01100010 01101001 01101110 01100001 01110010 01111001

14 Highlights

🎥 Laterna Magika

6 Highlights

✍️ Digital Scribbles

45 Highlights

👩‍🏫 Edumacation

4 Highlights

🛳️ Atomic Essays

30 Highlights

2022

Jan 14, 2022
Jan 14, 2022
Reached 1000 YouTube subscribers
What a nice way to start 2022! 🎉

https://dramsch.net/youtube
Youtube Creator, YouTube
Jan 13, 2022
Jan 13, 2022
Received a fellowship
I have been selected as a Software Sustainability Institute fellow 🎉

I'm so stoked to finally share this with you!

With the help of this fellowship, I will create content around 🤖 sustainable machine learning applications in research!

Link

2021

Dec 20, 2021
Dec 20, 2021
Published a Skillshare Course
The best way to learn data science and machine learning is by building an applied project. 📚

No surprise that when hiring, recruiters will often look through your personal portfolio.

In my newest class, I take you through an applied project on finance data. 🤖

https://skl.sh/3yxLhCq

Learn how to retrieve data for stocks and crypto, do exploratory data analysis and do proper validation on this tricky data. Ever wonder how these scammy "AI crypto trading bots" work? There are no real secrets really. It's just these simple models.

Apply multiple models to Bitcoin data to predict future prices.

When you finish this class, you have a valuable project, maybe your first, under your belt to get you started in data science.

Building a finance data science project with Python on Stocks, Crypto, and Bitcoin now on Skillshare!

Build your portfolio with me!

All the best and happy holidays.

Skillshare Instructor, Skillshare
Nov 23, 2021
Nov 23, 2021
Reposted by Jesper Dramsch
Hosted a podcast
On the season finale of the Software World, I welcome Jesper Dramsch, Scientist for Machine Learning in the European Center for Medium-Range Weather Forecasts (ECMRWF) and Geophysicist.

In our conversation, Jesper talks about the differences between Machine Learning and Data Science, how to enter the field, how life scientists shift their focus to Data Science.

We talk about how businesses should approach data as the processes and methodologies are completely different from software engineering.

Listen to the episode here 👇.

#26: Machine Learning and Data Science with Jesper Dramsch

Nov 08, 2021
Nov 08, 2021
Shipped an Atomic Essay
 Many people think Kaggle is just a competition platform.

This could not be further from the truth. The platform has grown to include amazing learning resources no Machine Learning practitioner should miss out on.

Don't be fooled.

↓ 🧵

🧠 Courses and Certificates on Kaggle Learn

Kaggle has an entire notebook-based course platform, including certificates!

Whether it's #SQL, Feature Engineering, or #MachineLearning Explainability, you can use the integrated compute platform to learn for free.

💾 Find Datasets with Examples

Early steps in data science include obtaining datasets.

Kaggle has gamified uploading data sets to the platform. They have licenses and descriptions of how a dataset was obtained.

Usually, a great starting point to get a free and easy data set.

🏆 Explore Old Competitions to Learn from

I love searching old competitions for new tricks!

Finished competitions very often have a showcase and explanation from the winners detailing how they approached the problem and how they placed in the top ranks!

💻 Find Fantastic Code When it's Hidden from You

The standard sort of Kaggle Code is "Hotness", which makes sense in active competitions.

When you search older code examples, click top right and switch to Most Votes to find the hidden gems from Experts to Grandmasters!

🙊 Don't miss out on Discussions

Especially Grandmasters pushing for that quadruple title
will often info-dump in the discussion section to farm upvotes.

You can essentially get an entire paper review in a single discussion comment.

❌ Don't Miss out on an Invaluable Resource

I have yet to even get close to winning any competition.

Yet, Kaggle has benefited me in more than one way in my professional life.

📚 TL;DR

👉 Get Certificates on @Kaggle Learn
👉 Find Datasets with Examples
👉 Explore old Competitions
👉 Sort Code the Right Way
👉 Don't skip the Discussions

#kaggle #kagglecompetition #education #python #machinelearning #code #deeplearning #learning #datascience #training #career 
Nov 08, 2021
Nov 08, 2021
Wrote Python
Answered @cassidoo's newsletter question.

Given an array of integers, return the index of each local peak in the array. A “peak” element is an element that is greater than its neighbors.


Example:

$ localPeaks([1,2,3,1])
$ [2]

$ localPeaks([1,3,2,3,5,6,4])
$ [1, 5]


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