Rami Krispin's Data Science Channel

PyData Website: www.pydata.org LinkedIn:

Twitter:

Backtesting and error metrics for modern time series forecasting Evaluating time series forecasting models for modern use cases has become incredibly challenging. This is because modern forecasting problems often involve a large number of related time series, often hierarchical, with a diverse set of characteristics such as intermittency, non-normality, and non-stationarity. In this talk we'll discuss all the tips, tricks, and pitfalls in creating model evaluation strategies and error metrics to overcome these challenges. Slides:

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:

Unlock exclusive coding resources: 🚀

🚀 Master Django & Python Mastery & Bonuses:

Don't Forget to Subscribe for More: ➡️

⬅️ Timestamp: 00:00 Intro 01:34 Introduction to Vector Databases 06:18 Vectors - Overview 17:53 Why are vectors used in a Vector database 21:21 Vector Databases Use cases 27:21 Building Vector Databases 28:13 Install Visual Studio Code, Python and OpenAI API 32:55 Hands-on Create a Vector DB Using Chroma 40:08 Hands-on - Code 55:23 End - Thank you!

30 days of Python programming challenge is a step-by-step guide to learn the Python programming language in 30 days. This challenge may take more than100 days, follow your own pace. These videos m...

PyData Website: www.pydata.org LinkedIn:

Twitter:

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:

Learn how to fine-tune Microsoft's Florence-2, a powerful open-source Vision Language Model, for custom object detection tasks. This in-depth tutorial guides you through setting up your environment in Google Colab, preparing datasets, and optimizing the model using LoRA. Chapters: - 00:00 Introduction: Unlock the Power of Florence-2 - 01:09 Getting Started: Prepare for VLM Fine-Tuning - 03:55 Florence-2 in Action: Explore Pre-trained Capabilities - 07:00 Dataset Deep Dive: PyTorch Data Loading for Florence-2 - 13:02 LoRA: Optimize Your VLM Training - 14:21 Fine-Tuning: Unleash Florence-2's Custom Object Detection - 17:30 Model Evaluation: Measure Your VLM's Success - 21:37 Florence-2 vs Other Computer Vision Models - 24:09 Conclusion and Next Steps Resources: - Roboflow:

- 🔴 Community Session July 3th, 2024 at 08:00 AM PST / 11:00 AM EST / 05:00 PM CET:

- ⭐ Notebooks GitHub:

- 📓 Florence notebook:

- 🗞 Florence-2 arXiv paper:

- 🗞 Florence-2 overview blog post:

- 🗞 Florence-2 fine-tuning blog post:

- 🔗 Florence-2 HF Space:

- 🗞 Mean Average Precision (mAP) blog post:

- 🗞 Confusion Matrix blog post:

Stay updated with the projects I'm working on at

and

⭐

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.