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Bayesian statistical methods provide powerful tools for solving various data science problems. The Bayesian approach yields easy-to-interpret results and automatically accounts for uncertainty in our estimates or predictions. Although computational challenges have historically been an obstacle, especially for new users, there are now mature probabilistic programming tools that are both efficient and easy to learn. We will use the latest release of PyMC (version 5) for this tutorial, but the concepts and techniques taught can be applied to any probabilistic programming framework. This tutorial targets practicing and aspiring data scientists and analysts who seek to incorporate Bayesian statistics and probabilistic programming into their work. It will provide new users with an overview of Bayesian statistical methods and their applicability in various situations. Learners will also gain practical experience in applying these methods using PyMC, including the specification, fitting, and validation of models using a real-world dataset. 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. PyData conferences aim to be accessible and community-driven, with novice to advanced level presentations. PyData tutorials and talks bring attendees the latest project features along with cutting-edge use cases. 00:00 Welcome! 00:10 Help us add time stamps or captions to this video! See the description for details. Want to help add timestamps to our YouTube videos to help with discoverability? Find out more here:

LLM101n: Let's build a Storyteller. Contribute to karpathy/LLM101n development by creating an account on GitHub.

Powering Your Generative AI Workloads with AMD and Open-Source ROCm - Farshad Ghodsian, Sourced Group In the generative AI ecosystem today, there is a strong emphasis on expensive AI hardware and proprietary CUDA implementations. While CUDA has undeniably played a crucial role in the success of generative AI, I’d like to share my experience with running generative AI workloads and applications on cost-effective AMD hardware and the open-source ROCm software stack. This alternative approach aims to provide users with greater flexibility and options, allowing them to apply their generative AI solutions across a wider range of hardware and software choices than ever before. Learn how to run your favourite open source large language and image generation models using ROCm, how far ROCm has come from previous versions and what features are currently supported, including PyTorch support, Optimum-AMD, Flash Attention 2, GPTQ and vLLM and how more affordable workstation class AMD GPUs compare to their Nvidia counterparts in terms of performance and inference speed. You will also see several demos of ROCm in action and some tips and things to watch out for when working with AMD GPUs.

Kyutai, a french AI lab with $300M in funding, just unveiled Moshi, an open-source GPT-4o competitor. Moshi is a real-time multimodal model that can listen, hear, and speak. Code, model, and paper will be release soon. @kyutai_labs

Workshop: Deploy and Monitor ML Pipelines with Open Source and Free Applications - Rami Krispin, Apple The workshop will focus on different deployment designs of machine learning pipelines using open-source applications and free-tier tools. We will use the US hourly demand for electricity data from the EIA API to demonstrate the deployment of a pipeline with GitHub Actions and Docker that fully automates the data refresh process and generates a forecast on a regular basis. This includes the use of open-source tools such as MLflow and YData Profiling to monitor the health of the data and the model's success. Last but not least, we will use Quarto doc to set up the monitoring dashboard and deploy it on GitHub Pages.

I’ve been reading Steven Sinofsky’s Hardcore Software, and particularly enjoyed this quote from a memo discussed in the Zero Defects chapter: You can improve the quality of your code, and if you do, the rewards for yourself and for Microsoft will be immense. The hardest part is to decide that you want to write perfect code. If I wrote that in an internal memo, I imagine the engineering team would mutiny, but software quality is certainly an interesting topic where I continue to refine my thinking.