./episode_41.sh
I would rather die of passion than of boredom.
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Advanced CFD Using the Finite Volume Method (Free Course)
Course on the Implicit Finite Volume Method for CFD using Jupyter Notebooks and Python.
Gartner Hype Cycle 2021 for Data Science and Machine Learning
New Coursera Course - FEM Mesh Convergence
In this hands-on project, you will learn about the Finite Element Method (FEM) and perform a convergence study using the cloud-based simulation tool SimScale - Coursera gives you a certificate at the end of each course taken!
New Coursera Course - CFD Simulation Through a Centrifugal Pump
In this course, you will learn about Computational Fluid Dynamics (CFD) and perform a pump simulation using the cloud-based simulation tool SimScale and also receive a certificate for this one!
Upcoming Podcast
For the upcoming podcast I am very happy to welcome Dr. Sarah Kaiser to my show!
Sarah is currently a technical staff member and quantum community lead at Unitary Fund and has spent much of her career developing new quantum hardware in the lab. From building satellites to hacking quantum cryptography hardware, communicating what is so exciting about quantum is her passion. She loves building new demos, tools, and partnerships to help enable the quantum open-source community to grow. When not at the keyboard she loves kayaking, laser cutting everything (safe), and writing books about engineering for kids and adults alike.
In this podcast, we talked about:
Quantum Computing (QC) ⚛️
Ethics in QC 📙
Q# and Sarah’s book about Q# 📚
“Quantum Supremacy”
and much more…
Course of the Week
In this NVIDIA course, you’ll learn how deep learning works through hands-on exercises in computer vision and natural language processing. You’ll train deep learning models from scratch, learning tools and tricks to achieve highly accurate results. You’ll also learn to leverage freely available, state-of-the-art pre-trained models to save time and get your deep learning application up and running quickly.
Book of the Week
Trust in Machine Learning
Trust in Machine Learning reveals the four principles of trustworthiness that humans and machines both share: accuracy, reliability, openness, and selflessness. You’ll master practical techniques for achieving trustworthiness as you explore real-world use cases drawn from author Kush Varshney’s extensive career, including a peer-to-peer lender and an automated résumé screening service.
You’ll learn how to handle the inevitable distribution shift between training and operating data, measure and mitigate bias and unfairness, and be robust to deliberate sabotage from adversarial attacks. Interpretability techniques demystify how your ML makes its decisions, and transparent reporting mechanisms ensure your whole pipeline is open and explainable. You’ll discover the dark side of AI such as filter bubbles and malicious deepfakes, and learn how to prevent unintended consequences from arising. Finally, you’ll see how machine learning can be used with real benevolence to empower nonprofits and do social good.
Visualize Ideas
"Straight roads do not make skillful drivers."
– Paulo Coelho
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Favourite Animation
Meme of the Week
“Reproducible”.
Closing Remarks
If you have any wishes for video topics, you can submit them in this form. Apart from that you can always reach out to me via Twitter or Instagram or LinkedIn, so let’s connect!
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See you next week and in the meantime, make sure to keep engineering your mind!
Jousef