Ampersand Biomedicines’ Post

On April 9th I was honored to deliver a lecture at the Faculty of Economic Sciences at the University of Warsaw, where I shared my insights on Vertex AI and the critical role of MLOps in today's AI landscape. A big thank you to the faculty and students for their warm welcome and insightful questions with special thanks to Marcin Chlebus, Ph.D. and Karolina Kuligowska, Ph.D. for the invitation. It's always inspiring to connect with the next generation of data scientists! #VertexAI #MLOps #MachineLearning #AI #ArtificialIntelligence #DataScience #CloudComputing #GoogleCloud #UniversityofWarsaw #Google

Learn how to drive Next-Gen AI applications with our friends at Amazon Web Services (AWS) and Astronomer at tmrw's webinar (5/29 @ 2 p ET/11 a PT) Save your spot here 🏄🏽♀️👉 https://hubs.ly/Q02xmmJx0 Can't tune in live? Register to receive recording🍿👉https://hubs.ly/Q02xmmJx0 See you tmrw! ✨ #genAI #AWS #artificialintelligence #AI

Insightful Introductory Talk on LLM Development by Sebastian Raschka! I enjoyed attending a fantastic introductory talk by Sebastian Raschka, PhD, today as part of the #TechTalks by #ACM series. For those who might not know, Sebastian is a Research Engineer at Lightning AI. During the talk, Sebastian covered a comprehensive overview of the stages involved in developing Large Language Models (LLMs). The talk delved into: 1. Building the Model: Discussing data preparation, attention mechanisms, and LLM architecture. 2. Pretraining: Covering pretraining processes, training loops, and model evaluation. 3. Finetuning: Explaining the importance of finetuning with class labels and instruction datasets to enhance model performance. Important Takeaway: Even though I have completed multiple courses and projects on LLMs, it is crucial to revisit foundational topics and attend such talks, as you always pick up something new. Today, I deepened my understanding of Preference Fine-Tuning and its role in refining models to better align with specific tasks or preferences. Kudos to Sebastian and the ACM Tech Talks series for an enlightening session! #AI #LLM #ACMTechTalks

Last week, I had the incredible opportunity to attend the PyTorch Conference 2024 in San Francisco! The two days were packed with inspiring keynotes, lightning talks, and deep-dive sessions on topics like Generative AI on the Edge, low precision dtypes, quantization, distributed training, parallelism, and scaling LLMs — just to name a few. It was fascinating to see firsthand how the PyTorch ecosystem is evolving and making AI more accessible and performant for developers everywhere. A special thanks to PyTorch and The Linux Foundation for sponsoring my trip and giving me the chance to connect with leading AI researchers, developers, and innovators. The future of AI looks brighter than ever! 🚀 You can explore the details of these sessions, including the slides from each talk at https://lnkd.in/ei2fJqd7 In addition to attending conferences like this, I’ve been quite active in the developer community. Over the years, I’ve answered 100+ questions on StackOverflow, particularly focusing on AI, PyTorch, and ML-related topics, you can read them on https://lnkd.in/epC75DZr #PyTorchConf #PyTorch #ML #AI #MachineLearning #GenerativeAI #LLM

Just finished the TensorFlow Developer Certificate Bootcamp, and I'm thrilled to share my journey into Computer Vision and Convolutional Neural Networks (CNNs)! Here's what fueled my excitement: Building with Conv2D and Pooling Layers: No longer a mystery! I can now confidently construct CNNs using Conv2D layers to extract valuable features from images and pooling layers to manage data size efficiently. Computer Vision Problem Detective: Equipped to diagnose various computer vision challenges, allowing me to build more targeted and effective models. Crafting Visionary Networks: Demystified the core principles of architecting CNNs specifically designed for image recognition tasks. Real-World Data Integration: Learned techniques to bridge the gap between theory and practice by incorporating real-world images into my CNN models, ensuring real-world applicability. I'm eager to leverage these newfound skills to tackle real-world computer vision projects! #TensorFlow #MachineLearning #ComputerVision #AI #NeuralNetworks #DeepLearning #vit

Exciting news for the #Data community 💡 Next week, PyData Berlin is back with another electrifying meetup. This time around, we’re thrilled to host two thought-provoking talks: - “Exploiting Data Structure and Geometry with a Diffusion-based Graph Neural Network” - “Example Sampling Strategies for Large Language Models” Learn more and snag one of the last remaining spots via the link in comments 👇 #insidegetyourguide #getyourguide #datascience

Google's AI cracks elite math Olympiad! AlphaGeometry solves geometry problems like a gold medalist, using a combo of creative leaps and logical reasoning. It even discovered a new theorem! This breakthrough paves the way for AI tackling even tougher problems across various fields. Read more about this exciting development in AI! https://lnkd.in/eDcfFgPm

🚀 Exciting News! 🚀 We are thrilled to share that The Hack Weekly - Data & AI Community Medium Publication has published "Blog 1" Jai Singh 's blog focusses on the fascinating world of Vector Databases in #genai 📊 💯 Vector Databases are the backbone of Gen AI applications 💯 In this post, he dives into the fundamentals of vector databases and one of the popular open-source vector databases Qdrant , exploring how they’re revolutionizing data management and analysis. Whether you’re a seasoned pro or just curious about this emerging tech, it is packed with insights, and future trends that you won’t want to miss. Check it out and let me know your thoughts! Your feedback and discussion would be greatly appreciated. Follow for more tech blogs and insightful posts ! 👉 https://lnkd.in/gwq5HKQT #VectorDatabases #AI #GenAI #NLP #TechBlog #HackWeekly #TheHackWeekly #Innovation #DataScience #TechTrends #Qdrant #GenerativeAI Understanding Vector Databases: Let’s Explore Qdrant — Most popular open-source vector database Share your thoughts what you loved about this blog in the comments section below 👇 ⤵

I'm thrilled to share that I have successfully completed the "Linear Algebra for Machine Learning and Data Science" course by DeepLearning.AI! 🚀 This course has been an amazing exploration of the fundamental ideas of linear algebra, with a focus on applications in data science and machine learning. From understanding matrices and vectors to exploring advanced topics like eigenvalues and eigenvectors, every module is equipped with essential skills to tackle real-world challenges in AI. “Consistency is harder when no one is clapping for you. You must clap for yourself during those times; you should always be your biggest fan.” --Luis Garcia #MachineLearning #DataScience #LinearAlgebra #DeepLearningAI #ContinuousLearning #AI #TechInnovation

"At the frontier of deep learning research, reliable infrastructure must be built with the same care as datasets and learning algorithms. To create Grok, we built a custom training and inference stack based on Kubernetes, Rust, and JAX..." ~ xAI