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Activities & events
| Title & Speakers | Event |
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Introduction to programming with Python (Part 1)
2026-01-06 · 17:00
THIS IS A PAID EVENT PLEASE COMPLETE REGISTRATION HERE: https://luma.com/b9wy77ys Who is this for? This workshop is designed for complete beginners and anyone who wants to learn Python from the ground up. Whether you're new to programming, switching careers, or building your confidence before moving into data, AI, or software development, this series will guide you step by step. By the end of the programme, you’ll understand the fundamentals of Python and how real code works inside modern applications. Who is leading the session? The session is led by Dr. Stelios Sotiriadis, CEO of Warestack and Associate Professor & MSc Programme Director at Birkbeck, University of London. Stelios teaches cloud computing, distributed systems, data engineering, and AI engineering, and has extensive experience working with Huawei, IBM, Autodesk, and multiple startups. He holds a PhD from the University of Derby, completed a postdoctoral fellowship at the University of Toronto, and has been teaching at Birkbeck since 2018. In 2021 he founded Warestack, building developer tools and automation software for startups around the world. About the Series Python for Beginners: From Zero to Hero is a live coding workshop split into 5 sessions (10 hours total). You will write code with me in real time, learning Python through hands-on exercises rather than slides. This is a limited-space, interactive workshop, ideal if you prefer learning by doing. What we’ll cover (Part 1) A practical workshop to Python, including:
Each exercise builds directly on the previous one, so by the end you’ll already feel comfortable writing real code. Requirements You just need:
Why Python? Python is one of the most widely used programming languages in the world. It's the foundation of:
This workshop gives you the skills to move confidently toward all of these areas. Format A 2-hour live session including:
This is a beginner-friendly, supportive learning environment. Cost The first session costs only £20, so you can get a flavour of the material and the teaching style. Each session after that is £40, making the total cost for all 5 sessions £180. By the end of the 5 sessions, you will be able to confidently write Python code and build your own programs from scratch. You’ll understand how to work with data, automate tasks, and apply Python to real-world problems — giving you a strong foundation for further study in AI, data science, and software development. Prerequisites No prior programming experience or Python installation required. I’ll guide you through everything from scratch. |
Introduction to programming with Python (Part 1)
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Introduction to Machine Learning with Python (Part 1)
2025-12-15 · 18:00
👉 Register and subscribe to my calendar to join more free sessions. Who is this for? Students, developers, and professionals who want a practical introduction to Machine Learning with Python, without the hype, just practical explanations and hands-on coding. If you’re confused by ML being explained with buzzwords or abstract theory, this session gives you the Python fundamentals you actually need to build and use basic machine learning models from scratch. Who is leading the session? The session is led by Dr. Stelios Sotiriadis, CEO of Warestack, Associate Professor and MSc Programme Director at Birkbeck, University of London, specialising in cloud computing, distributed systems, and AI engineering. Stelios holds a PhD from the University of Derby, completed a postdoctoral fellowship at the University of Toronto, and has worked on industry and research projects with Huawei, IBM, Autodesk, and multiple startups. Since moving to London in 2018, he has been teaching at Birkbeck. In 2021, he founded Warestack, building software for startups around the world. What we’ll cover A practical introduction to core machine learning concepts and how to implement them with Python and scikit-learn:
This session focuses on real code, clear understanding, and practical ML engineering. What are the requirements? Bring a laptop and ensure you have a Gmail account. The session will run entirely on Google Colab, so no local installation is required. Format A 2-hour live hands-on class, structured around:
This is a practical, code-first session, suitable for both beginners and intermediate Python users wanting to level up. In-person or online? The class will run in person, with streaming available for remote attendees. Please note: In-person participation is strongly preferred, as the session includes hands-on coding, live troubleshooting, and personalised support that cannot be fully provided to remote participants. Prerequisites You should be comfortable writing basic Python scripts (variables, loops, functions, imports). No prior machine learning experience is required. A link will be shared to participants after registration. Are there going to be more sessions? Yes, this is the first session in a new series on practical Machine Learning and applied AI with Python. Additional sessions will be scheduled afterwards, covering further machine learning and AI algorithms. What comes after? Participants will receive an optional mini ML assignment and recommended next steps for deeper learning. |
Introduction to Machine Learning with Python (Part 1)
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Introduction to programming with Python (Part 1)
2025-12-12 · 18:00
REGISTER HERE https://luma.com/h1pbvd9o Who is this for? This workshop is designed for complete beginners and anyone who wants to learn Python from the ground up. Whether you're new to programming, switching careers, or building your confidence before moving into data, AI, or software development, this series will guide you step by step. By the end of the programme, you’ll understand the fundamentals of Python and how real code works inside modern applications. Who is leading the session? The session is led by Dr. Stelios Sotiriadis, CEO of Warestack and Associate Professor & MSc Programme Director at Birkbeck, University of London. Stelios teaches cloud computing, distributed systems, data engineering, and AI engineering, and has extensive experience working with Huawei, IBM, Autodesk, and multiple startups. He holds a PhD from the University of Derby, completed a postdoctoral fellowship at the University of Toronto, and has been teaching at Birkbeck since 2018. In 2021 he founded Warestack, building developer tools and automation software for startups around the world. About the Series Python for Beginners: From Zero to Hero is a live coding workshop split into 5 sessions (10 hours total). You will write code with me in real time, learning Python through hands-on exercises rather than slides. This is a limited-space, interactive workshop, ideal if you prefer learning by doing. What we’ll cover (Part 1) A practical workshop to Python, including:
Each exercise builds directly on the previous one, so by the end you’ll already feel comfortable writing real code. Requirements You just need:
Why Python? Python is one of the most widely used programming languages in the world. It's the foundation of:
This workshop gives you the skills to move confidently toward all of these areas. Format A 2-hour live session including:
This is a beginner-friendly, supportive learning environment. Cost The first session costs only £20, so you can get a flavour of the material and the teaching style. Each session after that is £40, making the total cost for all 5 sessions £180. By the end of the 5 sessions, you will be able to confidently write Python code and build your own programs from scratch. You’ll understand how to work with data, automate tasks, and apply Python to real-world problems — giving you a strong foundation for further study in AI, data science, and software development. Prerequisites No prior programming experience or Python installation required. I’ll guide you through everything from scratch. |
Introduction to programming with Python (Part 1)
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WEBINAR "Differentially-Private Synthetic Data for Everyone"
2025-06-04 · 16:00
Pre-registration is REQUIRED. Add to your calendar - https://hubs.li/Q03lF-X-0 In this hands-on session, you'll learn how to generate high-quality synthetic data that preserves privacy using differential privacy techniques. We’ll walk through how to train differentially private generative models with MOSTLY AI’s open-source Synthetic Data SDK and explore how this method compares to traditional anonymization approaches in terms of both utility and risk. You’ll gain practical insights into configuring privacy parameters, understanding the impact of privacy budgets, and evaluating synthetic data output. We’ll also cover how to assess the fidelity of synthetic datasets using predictive and discriminative machine learning models, and how to create hybrid datasets that blend real and synthetic data for improved utility. Through live demonstrations and real-world examples, you’ll develop a strong understanding of the privacy-utility trade-offs and how to confidently apply privacy-safe synthetic data in your own data science workflows. Session Outline: Lesson 1: Introduction to Differential Privacy Get familiar with the core concepts of differential privacy and how it differs from traditional anonymization techniques. By the end of this lesson, you’ll be able to explain what differential privacy is, what a privacy budget (epsilon) means, and why it provides stronger privacy guarantees than pseudonymization or masking. Lesson 2: Setting Up and Using the Synthetic Data SDK Learn how to install and configure MOSTLY AI’s open-source Synthetic Data SDK to generate synthetic datasets with differential privacy enabled. You’ll run the SDK in LOCAL mode using a prepared dataset, explore the configuration options for privacy settings, and review the structure of the synthetic output. Lesson 3: Evaluating Utility vs. Privacy Trade-offs Compare synthetic datasets generated with different privacy settings to understand how utility is impacted by stricter privacy budgets. By the end of this lesson, you’ll be able to evaluate the usefulness of differentially private synthetic data using predictive models and summary statistics. Lesson 4: Creating Hybrid Datasets with Real and Synthetic Data Explore how to combine real and synthetic data to create hybrid datasets that retain utility while improving privacy. You’ll walk through a practical example and learn how to use synthetic data to augment or replace sensitive parts of your dataset. Difficulty: Intermediate Pre-reqs: This tutorial is designed for data engineers, data scientists, ML engineers, and analysts with basic Python skills and familiarity with working in Jupyter Notebooks. Attendees should have a general understanding of machine learning workflows and working with tabular datasets (e.g., CSV files or pandas DataFrames). No prior experience with synthetic data is required. To participate fully in the hands-on exercises, attendees should have the following installed before the session: Python 3.11+, Git Installation and setup of the Synthetic Data SDK will be covered as part of the tutorial. But feel free to get started beforehand by visiting https://github.com/mostly-ai/mostlyai. Speaker: Dr. Michael Platzer, Co-Founder and CTO of MOSTLY AI Dr. Michael Platzer is co-founder and CTO of MOSTLY AI, a leader in privacy-safe synthetic data generation. He earned his degrees in mathematics and in business with distinction, led consumer analytic teams at global technology leaders, before starting his venture to pioneer the field of synthetic data. His company's mission is to democratize data access and data insights in a safe and responsible way for everyone. ODSC Links: • Get free access to more talks/trainings like this at Ai+ Training platform: https://hubs.li/H0Zycsf0 • ODSC blog: https://opendatascience.com/ • Facebook: https://www.facebook.com/OPENDATASCI • Twitter: https://twitter.com/_ODSC & @odsc • LinkedIn: https://www.linkedin.com/company/open-data-science • Slack Channel: https://hubs.li/Q038cQBy0 • Code of conduct: https://odsc.com/code-of-conduct/ |
WEBINAR "Differentially-Private Synthetic Data for Everyone"
|
|
WEBINAR "Differentially-Private Synthetic Data for Everyone"
2025-06-04 · 16:00
Pre-registration is REQUIRED. Add to your calendar - https://hubs.li/Q03lF-X-0 In this hands-on session, you'll learn how to generate high-quality synthetic data that preserves privacy using differential privacy techniques. We’ll walk through how to train differentially private generative models with MOSTLY AI’s open-source Synthetic Data SDK and explore how this method compares to traditional anonymization approaches in terms of both utility and risk. You’ll gain practical insights into configuring privacy parameters, understanding the impact of privacy budgets, and evaluating synthetic data output. We’ll also cover how to assess the fidelity of synthetic datasets using predictive and discriminative machine learning models, and how to create hybrid datasets that blend real and synthetic data for improved utility. Through live demonstrations and real-world examples, you’ll develop a strong understanding of the privacy-utility trade-offs and how to confidently apply privacy-safe synthetic data in your own data science workflows. Session Outline: Lesson 1: Introduction to Differential Privacy Get familiar with the core concepts of differential privacy and how it differs from traditional anonymization techniques. By the end of this lesson, you’ll be able to explain what differential privacy is, what a privacy budget (epsilon) means, and why it provides stronger privacy guarantees than pseudonymization or masking. Lesson 2: Setting Up and Using the Synthetic Data SDK Learn how to install and configure MOSTLY AI’s open-source Synthetic Data SDK to generate synthetic datasets with differential privacy enabled. You’ll run the SDK in LOCAL mode using a prepared dataset, explore the configuration options for privacy settings, and review the structure of the synthetic output. Lesson 3: Evaluating Utility vs. Privacy Trade-offs Compare synthetic datasets generated with different privacy settings to understand how utility is impacted by stricter privacy budgets. By the end of this lesson, you’ll be able to evaluate the usefulness of differentially private synthetic data using predictive models and summary statistics. Lesson 4: Creating Hybrid Datasets with Real and Synthetic Data Explore how to combine real and synthetic data to create hybrid datasets that retain utility while improving privacy. You’ll walk through a practical example and learn how to use synthetic data to augment or replace sensitive parts of your dataset. Difficulty: Intermediate Pre-reqs: This tutorial is designed for data engineers, data scientists, ML engineers, and analysts with basic Python skills and familiarity with working in Jupyter Notebooks. Attendees should have a general understanding of machine learning workflows and working with tabular datasets (e.g., CSV files or pandas DataFrames). No prior experience with synthetic data is required. To participate fully in the hands-on exercises, attendees should have the following installed before the session: Python 3.11+, Git Installation and setup of the Synthetic Data SDK will be covered as part of the tutorial. But feel free to get started beforehand by visiting https://github.com/mostly-ai/mostlyai. Speaker: Dr. Michael Platzer, Co-Founder and CTO of MOSTLY AI Dr. Michael Platzer is co-founder and CTO of MOSTLY AI, a leader in privacy-safe synthetic data generation. He earned his degrees in mathematics and in business with distinction, led consumer analytic teams at global technology leaders, before starting his venture to pioneer the field of synthetic data. His company's mission is to democratize data access and data insights in a safe and responsible way for everyone. ODSC Links: • Get free access to more talks/trainings like this at Ai+ Training platform: https://hubs.li/H0Zycsf0 • ODSC blog: https://opendatascience.com/ • Facebook: https://www.facebook.com/OPENDATASCI • Twitter: https://twitter.com/_ODSC & @odsc • LinkedIn: https://www.linkedin.com/company/open-data-science • Slack Channel: https://hubs.li/Q038cQBy0 • Code of conduct: https://odsc.com/code-of-conduct/ |
WEBINAR "Differentially-Private Synthetic Data for Everyone"
|
|
WEBINAR "Differentially-Private Synthetic Data for Everyone"
2025-06-04 · 16:00
Pre-registration is REQUIRED. Add to your calendar - https://hubs.li/Q03lF-X-0 In this hands-on session, you'll learn how to generate high-quality synthetic data that preserves privacy using differential privacy techniques. We’ll walk through how to train differentially private generative models with MOSTLY AI’s open-source Synthetic Data SDK and explore how this method compares to traditional anonymization approaches in terms of both utility and risk. You’ll gain practical insights into configuring privacy parameters, understanding the impact of privacy budgets, and evaluating synthetic data output. We’ll also cover how to assess the fidelity of synthetic datasets using predictive and discriminative machine learning models, and how to create hybrid datasets that blend real and synthetic data for improved utility. Through live demonstrations and real-world examples, you’ll develop a strong understanding of the privacy-utility trade-offs and how to confidently apply privacy-safe synthetic data in your own data science workflows. Session Outline: Lesson 1: Introduction to Differential Privacy Get familiar with the core concepts of differential privacy and how it differs from traditional anonymization techniques. By the end of this lesson, you’ll be able to explain what differential privacy is, what a privacy budget (epsilon) means, and why it provides stronger privacy guarantees than pseudonymization or masking. Lesson 2: Setting Up and Using the Synthetic Data SDK Learn how to install and configure MOSTLY AI’s open-source Synthetic Data SDK to generate synthetic datasets with differential privacy enabled. You’ll run the SDK in LOCAL mode using a prepared dataset, explore the configuration options for privacy settings, and review the structure of the synthetic output. Lesson 3: Evaluating Utility vs. Privacy Trade-offs Compare synthetic datasets generated with different privacy settings to understand how utility is impacted by stricter privacy budgets. By the end of this lesson, you’ll be able to evaluate the usefulness of differentially private synthetic data using predictive models and summary statistics. Lesson 4: Creating Hybrid Datasets with Real and Synthetic Data Explore how to combine real and synthetic data to create hybrid datasets that retain utility while improving privacy. You’ll walk through a practical example and learn how to use synthetic data to augment or replace sensitive parts of your dataset. Difficulty: Intermediate Pre-reqs: This tutorial is designed for data engineers, data scientists, ML engineers, and analysts with basic Python skills and familiarity with working in Jupyter Notebooks. Attendees should have a general understanding of machine learning workflows and working with tabular datasets (e.g., CSV files or pandas DataFrames). No prior experience with synthetic data is required. To participate fully in the hands-on exercises, attendees should have the following installed before the session: Python 3.11+, Git Installation and setup of the Synthetic Data SDK will be covered as part of the tutorial. But feel free to get started beforehand by visiting https://github.com/mostly-ai/mostlyai. Speaker: Dr. Michael Platzer, Co-Founder and CTO of MOSTLY AI Dr. Michael Platzer is co-founder and CTO of MOSTLY AI, a leader in privacy-safe synthetic data generation. He earned his degrees in mathematics and in business with distinction, led consumer analytic teams at global technology leaders, before starting his venture to pioneer the field of synthetic data. His company's mission is to democratize data access and data insights in a safe and responsible way for everyone. ODSC Links: • Get free access to more talks/trainings like this at Ai+ Training platform: https://hubs.li/H0Zycsf0 • ODSC blog: https://opendatascience.com/ • Facebook: https://www.facebook.com/OPENDATASCI • Twitter: https://twitter.com/_ODSC & @odsc • LinkedIn: https://www.linkedin.com/company/open-data-science • Slack Channel: https://hubs.li/Q038cQBy0 • Code of conduct: https://odsc.com/code-of-conduct/ |
WEBINAR "Differentially-Private Synthetic Data for Everyone"
|
|
WEBINAR "Differentially-Private Synthetic Data for Everyone"
2025-06-04 · 16:00
Pre-registration is REQUIRED. Add to your calendar - https://hubs.li/Q03lF-X-0 In this hands-on session, you'll learn how to generate high-quality synthetic data that preserves privacy using differential privacy techniques. We’ll walk through how to train differentially private generative models with MOSTLY AI’s open-source Synthetic Data SDK and explore how this method compares to traditional anonymization approaches in terms of both utility and risk. You’ll gain practical insights into configuring privacy parameters, understanding the impact of privacy budgets, and evaluating synthetic data output. We’ll also cover how to assess the fidelity of synthetic datasets using predictive and discriminative machine learning models, and how to create hybrid datasets that blend real and synthetic data for improved utility. Through live demonstrations and real-world examples, you’ll develop a strong understanding of the privacy-utility trade-offs and how to confidently apply privacy-safe synthetic data in your own data science workflows. Session Outline: Lesson 1: Introduction to Differential Privacy Get familiar with the core concepts of differential privacy and how it differs from traditional anonymization techniques. By the end of this lesson, you’ll be able to explain what differential privacy is, what a privacy budget (epsilon) means, and why it provides stronger privacy guarantees than pseudonymization or masking. Lesson 2: Setting Up and Using the Synthetic Data SDK Learn how to install and configure MOSTLY AI’s open-source Synthetic Data SDK to generate synthetic datasets with differential privacy enabled. You’ll run the SDK in LOCAL mode using a prepared dataset, explore the configuration options for privacy settings, and review the structure of the synthetic output. Lesson 3: Evaluating Utility vs. Privacy Trade-offs Compare synthetic datasets generated with different privacy settings to understand how utility is impacted by stricter privacy budgets. By the end of this lesson, you’ll be able to evaluate the usefulness of differentially private synthetic data using predictive models and summary statistics. Lesson 4: Creating Hybrid Datasets with Real and Synthetic Data Explore how to combine real and synthetic data to create hybrid datasets that retain utility while improving privacy. You’ll walk through a practical example and learn how to use synthetic data to augment or replace sensitive parts of your dataset. Difficulty: Intermediate Pre-reqs: This tutorial is designed for data engineers, data scientists, ML engineers, and analysts with basic Python skills and familiarity with working in Jupyter Notebooks. Attendees should have a general understanding of machine learning workflows and working with tabular datasets (e.g., CSV files or pandas DataFrames). No prior experience with synthetic data is required. To participate fully in the hands-on exercises, attendees should have the following installed before the session: Python 3.11+, Git Installation and setup of the Synthetic Data SDK will be covered as part of the tutorial. But feel free to get started beforehand by visiting https://github.com/mostly-ai/mostlyai. Speaker: Dr. Michael Platzer, Co-Founder and CTO of MOSTLY AI Dr. Michael Platzer is co-founder and CTO of MOSTLY AI, a leader in privacy-safe synthetic data generation. He earned his degrees in mathematics and in business with distinction, led consumer analytic teams at global technology leaders, before starting his venture to pioneer the field of synthetic data. His company's mission is to democratize data access and data insights in a safe and responsible way for everyone. ODSC Links: • Get free access to more talks/trainings like this at Ai+ Training platform: https://hubs.li/H0Zycsf0 • ODSC blog: https://opendatascience.com/ • Facebook: https://www.facebook.com/OPENDATASCI • Twitter: https://twitter.com/_ODSC & @odsc • LinkedIn: https://www.linkedin.com/company/open-data-science • Slack Channel: https://hubs.li/Q038cQBy0 • Code of conduct: https://odsc.com/code-of-conduct/ |
WEBINAR "Differentially-Private Synthetic Data for Everyone"
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Getting Started with FiftyOne Workshop
2024-12-04 · 17:00
Harpreet Sahota
– data science leader
@ Voxel51
90-minute hands-on workshop led by Harpreet Sahota, Hacker-in-Residence and Machine Learning Engineer at Voxel51. Part 1 covers FiftyOne basics (terms, architecture, installation, general usage), an overview of useful workflows to explore, understand, and curate data, and how FiftyOne represents and semantically slices unstructured computer vision data. Part 2 provides a hands-on introduction to FiftyOne: loading datasets from the FiftyOne Dataset Zoo, navigating the FiftyOne App, programmatically inspecting attributes, adding new samples and custom attributes, generating and evaluating model predictions, and saving insightful views into the data. Prerequisites: working knowledge of Python. |
Dec 4 - Workshop: Getting Started with Computer Vision and FiftyOne
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PyLadies Berlin: May Members Talk evening
2024-05-14 · 17:00
This Meetup is presented by our friends from PyLadies. For more information and to help us keep track, please register via the event page of PyLadies Berlin here! About the evening Join us for PyLadies Berlin's upcoming talk evening event. Don't miss out on this chance to be part of our community of women developers empowering each other in the world of technology startups. V (she/her) - The Nesting Doll Effect: Scope in Python In this talk, I’ll give an overview of what scope is, why it’s important (and actually quite cool) and go over the order in which Python looks up variable names. I’ll show you a couple of examples, including a code snippet which we’ll play around with together (interactively and anonymously). All while using the multi-layered image of a Nesting doll (or Russian or Matryoshka doll) who has been a helpful partner on my way to understanding this topic. Level: Beginner friendly 👩💻 V is a trained historian, turned self-employed English teacher, turned community-taught techie. She is currently working as a data engineer at SAP Signavio. Adrin (he/him) - Revenue based scoring in `GridSearchCV`: a case for the new metadata routing in scikit-learn Passing metadata such as `sample_weight` and `groups` through a scikit-learn `cross_validate`, `GridSearchCV`, or a `Pipeline` to the right estimators, scorers, and CV splitters has been either cumbersome, hacky, or impossible. The new metadata routing mechanism in scikit-learn enables you to pass metadata through these objects. As a use-case, we study how you can implement a revenue sensitive scoring while doing a hyperparameter search within a `GridSearchCV` object. Level: Intemediate Python developer 👩💻 Adrin is a scikit-learn maintainer and works on a few other open source projects. He has a PhD in Bioinformatics, has worked as a consultant, and in an algorithmic privacy and fairness team. He is now a cofounder at probabl.ai, where they work on enabling people to do statistically sane machine learning. ---------- On the Agenda 19h00 Community Announcements & sponsor introduction 19h20 "The Nesting Doll Effect: Scope in Python" with V 20h00 "Revenue based scoring in `GridSearchCV`: a case for the new metadata routing in scikit-learn" with Adrin 21h30 See You Next Time! ---------- Code of Conduct By attending our online event, you agree to the PyLadies Code of Conduct: https://www.pyladies.com/CodeOfConduct/Contact Accessibility The Location is accessible for wheelchair users. This includes the entrance (no steps to get into the location), toilets and the stage. Can men attend? Everyone is welcome. If you identify as someone well-represented in open source and in tech, please be mindful of the space and privileges you have, and use it to support others. Speaking Opportunities Interested in speaking at one of our events? Have a good idea for a Meetup? Get in touch with us at [email protected] Find us on the PyLadies Global workspace:
Important note: To help us keep track, please register via the event page of PyLadies Berlin here! |
PyLadies Berlin: May Members Talk evening
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PyData Heidelberg #12: Convex Optimization with DSP & xAI with SHAP + Lime
2023-12-05 · 17:00
DataScience and AI: in person in Heidelberg and live on PyData.TV on YouTube Agenda 18:00 Doors open 18:30 Welcome 18:45 Philipp Schiele - Introducing Disciplined Saddle Programming (DSP): A New Paradigm in Convex Optimization 19:15 Break: Networking with snacks and beverages 20:00 Christophe Krech - Unveiling the Black Box: Exploring Explainable AI with SHAP and Lime for Tabular Data in Python 20:30 Lightning Talks 20:45 Networking with snacks and beverages 21:30 End Lightning Talks Join us by contributing a five-minute lightning talk! Fill out this form. How to sign up for on site It's important for us to make this meet up happen in a responsible way. We have limited seats available only. No limits to sign up remotely! How to join remotely Join the live stream on YouTube. Q&A Ask via Slido This event will be in English. ---- Talk #1 Philipp Schiele (Ludwig Maximilian University of Munich) Introducing Disciplined Saddle Programming (DSP): A New Paradigm in Convex Optimization Disciplined Saddle Programming (DSP), a new Python-based domain-specific language, significantly enhances the approach to convex-concave saddle problems, crucial in fields like game theory, machine learning, and finance. Hosted on GitHub, DSP extends the CVXPY framework, streamlining the dualization process in optimization. DSP focuses on robust optimization problems, providing an intuitive interface for problem specification and resolution. It builds upon the conic-representable saddle programs by Juditsky and Nemirovski, applying disciplined convex programming to saddle problems. DSP's introduction is a call to the broader scientific and engineering communities to explore its diverse applications. It simplifies complex optimization tasks, making them more accessible and manageable, and holds the potential to significantly impact various optimization-reliant fields. Philipp Schiele's educational background is in finance and economics and he is currently pursuing a PhD in financial econometrics at the Ludwig Maximilian University of Munich, where he taught various courses in statistics. He is a CVXPY maintainer and has presented a tutorial at SciPy 2022. Generally, he is enthusiastic about finance, optimization, and technology, especially open-source projects. Apart from that, he also conducts workshops at SciPy US on "Controlling Self-Landing Rockets Using CVXPY" 🚀 Talk #2 Christophe Krech - Unveiling the Black Box: Exploring Explainable AI with SHAP and Lime for Tabular Data in Python In the era of complex machine learning models, understanding and interpreting their decisions is crucial for fostering trust and transparency; and will become a regulatory requirement for many applications with the implementation of the EU AI act. Explainable AI (XAI) specifically tailored for tabular data can demystify the black box of machine learning. SHAP (SHapley Additive exPlanations) and Lime (Local Interpretable Model-agnostic Explanations) are to very powerful model-agnostic tools to enhance the understanding of model predictions, troubleshoot biases, and communicate machine learning insights effectively. Thanks to great open-source implementations, they can also be seamlessly integrated into existing Python workflows in many real-world applications. Christophe Krech is a senior data scientist at Experian. During his studies in Mannheim and Darmstadt, he already focused on the explainability of machine learning methods and the associated regulatory challenges. After completing his master’s degree in data science, he joined the global information service provider Experian in 2019. Since then, he has been supporting FinTechs, e-commerce retailers and banks in the successful use of machine learning for risk management. Explainability of machine learning models and their implementation in Python are an integral part of his work there. ---- Lightning Talks: 1. Your spot! Submit a talk here 2. Your spot! Submit a talk here 3. Your spot! Submit a talk here Acknowledgements Also a big thank you to our sponsors:
Contact If you have any questions or suggestions, please feel free to contact us via:
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PyData Heidelberg #12: Convex Optimization with DSP & xAI with SHAP + Lime
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Oct 2023 - Getting Started with FiftyOne Computer Vision Workshop
2023-10-25 · 17:00
When: October 25, 2023 at 10 AM PDT (1 PM EDT / 17:00 UTC) for 90 minutes Zoom: https://voxel51.com/computer-vision-events/fiftyone-workshop-oct-25/ Want greater visibility into the quality of your computer vision datasets and models? Then join Dan Gural, machine learning engineer at Voxel51, for this free 90 minute, hands-on workshop to learn how to leverage the open source FiftyOne computer vision toolset. In the first part of the workshop we’ll cover:
The second half will be a hands-on introduction to FiftyOne, where you will learn how to:
Prerequisites are a working knowledge of python and basic computer vision. All attendees will get access to the tutorials, videos, and code examples used in the workshop. |
Oct 2023 - Getting Started with FiftyOne Computer Vision Workshop
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Oct 2023 - Getting Started with FiftyOne Computer Vision Workshop
2023-10-25 · 17:00
When: October 25, 2023 at 10 AM PDT (1 PM EDT / 17:00 UTC) for 90 minutes Zoom: https://voxel51.com/computer-vision-events/fiftyone-workshop-oct-25/ Want greater visibility into the quality of your computer vision datasets and models? Then join Dan Gural, machine learning engineer at Voxel51, for this free 90 minute, hands-on workshop to learn how to leverage the open source FiftyOne computer vision toolset. In the first part of the workshop we’ll cover:
The second half will be a hands-on introduction to FiftyOne, where you will learn how to:
Prerequisites are a working knowledge of python and basic computer vision. All attendees will get access to the tutorials, videos, and code examples used in the workshop. |
Oct 2023 - Getting Started with FiftyOne Computer Vision Workshop
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PyData Montreal Meetup #24 (in-person | en personne)
2023-08-31 · 21:30
📣 Join us for an exciting "Back to School" meetup at the Ubisoft Montreal office! 🎉🏢 Discover the power of Python in Data Journalism and Reinforcement Learning through insightful talks by our expert speakers, Roberto Rocha and Yann Bouteiller. IMPORTANT : * The access point is on Saint Dominique side (5480 rue saint Dominique) and NOT on Boulevard Saint laurent * Get your proof of attendance with you (available on the meetup event page or received per email), for us to check your status (waiting list or not) at the venue 📊🤖 Don't miss this opportunity to explore cutting-edge applications and ignite your curiosity. Save the date and secure your spot now! 👉🗓️ See you there! 👋😄 Agenda 5:30 pm - Doors open 6:00 pm - Introduction 6:10 pm - How we used Python to see how many lobbyists attended Liberal fundraisers by Roberto Rocha (Investigative Journalism Foundation) 6:50 pm - Break, pizza and networking 7:10 pm - Using Video Games for Autonomous driving by Yann Bouteiller (Polytechnique Montréal) 7:50 pm - Final notes, break and networking Talks How we used Python to see how many lobbyists attended Liberal fundraisers Abstract: There's nothing illegal about lobbyists supporting political parties, but it's certainly suspicious when lobbyists give money to politicians they are trying to influence. We set out to understand just how many active lobbyists attended Liberal fundraising events, which can cost up to $1,700 to attend. To do this, we crossed the guest lists of fundraisers released by the Liberal Party and Elections Canada and the federal lobby registry, which we scrape daily. This involved a complicated mix of PDF parsing, fuzzy matching, pandas wrangling, and good old-fashioned gumshoe sleuthing. About Roberto: He is an investigative data journalist who applies data science and storytelling methods to uncover news that speaks truth to power. He currently works for the nonprofit startup Investigative Journalism Foundation, and before that worked for the CBC News investigative unit and the Montreal Gazette. When he's not geeking out with new data-focused Python packages, he likes playing Latin jazz guitar, making pizza, shaking cocktails, and doing AcroYoga in the park. Using Video Games for Autonomous driving Abstract: What if a recreational video game transformed into a valuable simulator for the autonomous car industry? As companies like Tesla opt out of traditional sensors and increasingly rely on vision-based Machine Learning, this idea becomes unexpectedly relevant. Being highly optimized for visual rendering, recent editions of popular games like Forza and Need For Speed offer unparalleled trade-offs between computational complexity and visual fidelity. In this presentation, we will explore the process of training a vision-based, real-time agent to drive in TrackMania 2020 (Ubisoft Nadeo). Despite being a visually less serious game, TrackMania's low-resource, free-to-play and creative nature makes it a well-suited proxy for this task. The final part of the talk will then open the discussion about the potential future of AI-driven bots and their capabilities. About Yann: He is a machine learning engineer specializing in Deep Reinforcement Learning (RL) for Robotics. Currently holding the position of Research Associate at Polytechnique Montreal, Yann has made significant contributions to the field through a diverse array of publications and open-source projects, spanning from the theoretical underpinnings of Deep RL to advancements in cognitive neuroscience. He leads the development of TMRL, an open-source framework tailored to facilitate the secure and optimized implementation of ad-hoc Deep RL pipelines for complex real-time applications. This includes autonomous driving, high-frequency trading, and other high-frequency control scenarios. Notably, TMRL garners recognition for its illustrative pipeline in the TrackMania videogame. |
PyData Montreal Meetup #24 (in-person | en personne)
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Aug 2023 - Getting Started with FiftyOne Computer Vision Workshop
2023-08-30 · 17:00
When: August 30, 2023 at 10 AM PDT (1:00 PM EDT / 17:00 UTC) for 90 minutes Zoom: https://voxel51.com/computer-vision-events/fiftyone-workshop-8-30-2023/ Want greater visibility into the quality of your computer vision datasets and models? Then join Leila Kaneda, machine learning engineer at Voxel51, for this free 90 minute, hands-on workshop to learn how to leverage the open source FiftyOne computer vision toolset. In the first part of the workshop we’ll cover:
The second half will be a hands-on introduction to FiftyOne, where you will learn how to:
Prerequisites are a working knowledge of python and basic computer vision. All attendees will get access to the tutorials, videos, and code examples used in the workshop. |
Aug 2023 - Getting Started with FiftyOne Computer Vision Workshop
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Aug 2023 - Getting Started with FiftyOne Computer Vision Workshop
2023-08-30 · 17:00
When: August 30, 2023 at 10 AM PDT (1:00 PM EDT / 17:00 UTC) for 90 minutes Zoom: https://voxel51.com/computer-vision-events/fiftyone-workshop-8-30-2023/ Want greater visibility into the quality of your computer vision datasets and models? Then join Leila Kaneda, machine learning engineer at Voxel51, for this free 90 minute, hands-on workshop to learn how to leverage the open source FiftyOne computer vision toolset. In the first part of the workshop we’ll cover:
The second half will be a hands-on introduction to FiftyOne, where you will learn how to:
Prerequisites are a working knowledge of python and basic computer vision. All attendees will get access to the tutorials, videos, and code examples used in the workshop. |
Aug 2023 - Getting Started with FiftyOne Computer Vision Workshop
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