Uploaded August 2026 | Updated September 2026, 2 weeks ago
The Season 2 finale of Give ME 5 is now live!
Dr. Andy R. Terrel (he/him) has been working on GPUs since 2009. And his job at NVIDIA is to make sure Python scientists can actually use them.
What does that look like in practice?
→ CUDA Python 1.0: a stable API for GPU-accelerated Python — just released, ready for the community to kick the tires
→ Directly engaging with the open source scientific Python community to surface real use cases and accelerate real science. "Scientists with better tools make better discoveries."
Watch the S2 finale now at youtu.be/FXecrg2rSKM
The Season 2 finale of Give ME 5 is now live!
Dr. Andy R. Terrel (he/him) has been working on GPUs since 2009. And his job at NVIDIA is to make sure Python scientists can actually use them.
What does that look like in practice?
→ CUDA Python 1.0: a stable API for GPU-accelerated Python — just released, ready for the community to kick the tires
→ Directly engaging with the open source scientific Python community to surface real use cases and accelerate real science. "Scientists with better tools make better discoveries."
Watch the S2 finale now at youtu.be/FXecrg2rSKM
![Simulating the World using SimPy: A practical Example [PyCon DE & PyData 2026]](https://i.ytimg.com/vi/zayHuvKVr34/mqdefault.jpg "Simulating the World using SimPy: A practical Example [PyCon DE & PyData 2026]
🔊 Recorded at PyCon DE & PyData 2026, 16.04.2026
https://2026.pycon.de/talks/WDHTQR/
🎓 Watch Cloud Engineer Niklas demonstrate how to use SimPy to build reproducible, event-based simulations for testing complex systems and optimizing load-balancing algorithms.
Speakers:
Niklas
Description:
Explainable AI (XAI) addresses the black box problem in machine learning, where models may rely on spurious correlations—such as identifying a wolf based on snow in the background rather than the animals features—to make predictions. To mitigate this, a structured framework distinguishes between interpretability (direct reading of model logic) and explainability (approximations of model behavior). The framework further categorizes analysis by model access (white box vs. black box), scope (global vs. local), and data type (tabular, image, and text).
For tabular data, global explainability focuses on feature ranking, effects, and interactions. SHAP (Shapley Additive Explanations) uses game theory to rank feature importance and visualize how specific values push predictions higher or lower via beeswarm plots. Feature effects are analyzed using Partial Dependence Plots (PDP) for average effects or Accumulated Local Effects (ALE) plots to handle correlated features. Local explainability identifies why a specific prediction occurred using SHAP waterfall plots or LIME (Local Interpretable Model-agnostic Explanations), which fits a local meta-model to the input. Counterfactual analysis, implemented via the DiCE package, functions as a GPS navigator to determine the minimum input changes required to achieve a different target outcome.
For image data, global analysis employs Testing with Concept Activation Vectors (TCAV) to determine if human-defined concepts, such as stripes, influence predictions using directional derivatives. Additionally, feature visualization reveals what a model learns at different layers, progressing from simple patterns to complex structures and specific objects.
⭐️ About PyCon DE:
PyCon DE is the leading conference on open-source Python applications in AI and data science. It brings together industry professionals, researchers, AI and data science practitioners, and software engineering communities, providing a unique platform for collaboration, knowledge sharing, and innovation.
The PyCon DE & PyData 2026 conference delivered an exceptional experience, fostering stronger connections within the Python community while showcasing the latest advancements in artificial intelligence and data science. Attendees enjoyed a diverse and engaging program of talks, workshops, and networking opportunities, further establishing the conference as a premier event for Python, AI, and data science enthusiasts across Germany.
PyCon DE 2027 will take place in Heidelberg from 19 to 23 April 2027.
Follow us:
• Newsletter: https://2027.pycon.de/newsletter/
• LinkedIn: https://www.linkedin.com/company/pyconde
• X: https://www.x.com/pyconde
Links:
• Conference website: http://pycon.de
• Other sessions: https://2026.pycon.de/talks/
The conference was organized by
• Python Softwareverband e.V.: http://pysv.org
• Pioneers Hub gemeinnützige GmbH: http://pioneershub.org
in collaboration with NumFOCUS Inc.: http://numfocus.org
If you enjoyed this session, please like, and subscribe to our channel for more insightful talks and discussions.
Share this video with your network to spread the knowledge!
Hashtags:
#Python #PyConDE #PyData #OpenSource #AI #DataScience #MachineLearning #SoftwareEngineering #LLMs #Community #Sovereignty
Acknowledgements:
Special thanks to all the volunteers and sponsors who made this event possible.
About:
Python Softwareverband e.V.:
PySV is a non-profit that promotes the use and development of Python in Germany through events, education, and advocacy, fostering an open Python community.
Pioneers Hub gemeinnützige GmbH:
is a non-profit fostering innovation in AI and tech by connecting experts and promoting knowledge exchange through events and collaborative initiatives.
NumFOCUS Inc.
supports open-source scientific computing by providing financial and logistical support to key projects like NumPy and Jupyter, promoting sustainable development and collaboration.
www.pydata.org
PyData is an educational program of NumFOCUS, a 501(c)3 non-profit organization in the United States. PyData provides a forum for the international community of users and developers of data analysis tools to share ideas and learn from each other. The global PyData network promotes discussion of best practices, new approaches, and emerging technologies for data management, processing, analytics, and visualization. PyData communities approach data science using many languages, including (but not limited to) Python, Julia, and R.")
![Array-Oriented Programming in Python: Libraries, Techniques, and Trade-offs [PyCon DE & PyData 2026]](https://i.ytimg.com/vi/zgIj2oCFF1M/mqdefault.jpg "Array-Oriented Programming in Python: Libraries, Techniques, and Trade-offs [PyCon DE & PyData 2026]
🔊 Recorded at PyCon DE & PyData 2026, 15.04.2026
https://2026.pycon.de/talks/AGYLTV/
🎓 Watch Iason Krommydas break down the modern Python array ecosystem to discover how to choose between NumPy, JAX, Numba, and Awkward Array for maximum performance in your scientific computing workflows.
Speakers:
Iason Krommydas
Description:
Array-oriented programming in Python shifts the focus from individual element manipulation to operations on entire data structures. This paradigm addresses the performance bottlenecks of the Python interpreter by offloading heavy computations to pre-compiled C or C++ libraries. While imperative programming relies on explicit loops and functional programming uses mapping functions, array-oriented programming utilizes implicit loops through vectorization, which aligns with hardware-level SIMD (Single Instruction, Multiple Data) operations common in CPUs and GPUs.
Key tools for this approach include NumPy, JAX, and Numba. NumPy provides the foundational array object and vectorized operations, though it often creates costly intermediate arrays in memory for complex expressions. To mitigate this, NumExpr fuses operations to reduce memory overhead, while Numba uses LLVM to compile Python bytecode into machine code, allowing imperative loops to run at C-like speeds. JAX further optimizes this process by tracing functions to create an intermediate representation (HLO), which is then compiled via XLA (Accelerated Linear Algebra) for efficient execution on CPUs, GPUs, or TPUs.
A primary trade-off in array-oriented programming is the handling of conditional logic. Standard Python if-statements cannot be applied to arrays because the truth value of multiple elements is ambiguous. This is resolved using boolean masking—applying a filter of true/false values to update only specific elements. In many cases, it is computationally faster to perform redundant calculations on all elements than to manage the memory overhead of masking. For datasets exceeding available RAM, libraries like Dask extend these capabilities by implementing chunking and parallelized computation graphs.
⭐️ About PyCon DE:
PyCon DE is the leading conference on open-source Python applications in AI and data science. It brings together industry professionals, researchers, AI and data science practitioners, and software engineering communities, providing a unique platform for collaboration, knowledge sharing, and innovation.
The PyCon DE & PyData 2026 conference delivered an exceptional experience, fostering stronger connections within the Python community while showcasing the latest advancements in artificial intelligence and data science. Attendees enjoyed a diverse and engaging program of talks, workshops, and networking opportunities, further establishing the conference as a premier event for Python, AI, and data science enthusiasts across Germany.
PyCon DE 2027 will take place in Heidelberg from 19 to 23 April 2027.
Follow us:
• Newsletter: https://2027.pycon.de/newsletter/
• LinkedIn: https://www.linkedin.com/company/pyconde
• X: https://www.x.com/pyconde
Links:
• Conference website: http://pycon.de
• Other sessions: https://2026.pycon.de/talks/
The conference was organized by
• Python Softwareverband e.V.: http://pysv.org
• Pioneers Hub gemeinnützige GmbH: http://pioneershub.org
in collaboration with NumFOCUS Inc.: http://numfocus.org
If you enjoyed this session, please like, and subscribe to our channel for more insightful talks and discussions.
Share this video with your network to spread the knowledge!
Hashtags:
#Python #PyConDE #PyData #OpenSource #AI #DataScience #MachineLearning #SoftwareEngineering #LLMs #Community #Sovereignty
Acknowledgements:
Special thanks to all the volunteers and sponsors who made this event possible.
About:
Python Softwareverband e.V.:
PySV is a non-profit that promotes the use and development of Python in Germany through events, education, and advocacy, fostering an open Python community.
Pioneers Hub gemeinnützige GmbH:
is a non-profit fostering innovation in AI and tech by connecting experts and promoting knowledge exchange through events and collaborative initiatives.
NumFOCUS Inc.
supports open-source scientific computing by providing financial and logistical support to key projects like NumPy and Jupyter, promoting sustainable development and collaboration.
www.pydata.org
PyData is an educational program of NumFOCUS, a 501(c)3 non-profit organization in the United States. PyData provides a forum for the international community of users and developers of data analysis tools to share ideas and learn from each other. The global PyData network promotes discussion of best practices, new approaches, and emerging technologies for data management, processing, analytics, and visualization. PyData communities approach data science using many languages, including (but not limited to) Python, Julia, and R.")
