Jeremy Connell-Waite’s Post

"Building High-Performing Computer Vision Models" discusses Encord Active and neptune.ai, platforms enabling teams to curate visual datasets and track machine learning experiments, covering end-to-end computer vision modeling. It emphasizes the importance of data quality, iterative model building, and challenges faced in collaborative environments and rapidly growing organizations. Read full article at https://ift.tt/ipOoj7u.

I recently started using 𝗜𝗻𝘀𝘁𝗿𝘂𝗰𝘁𝗼𝗿 to build LLM-based applications. Four chapters are currently available: 𝐃𝐚𝐭𝐚 𝐄𝐱𝐭𝐫𝐚𝐜𝐭𝐢𝐨𝐧, 𝐒𝐲𝐧𝐭𝐡𝐞𝐭𝐢𝐜 𝐃𝐚𝐭𝐚 𝐆𝐞𝐧𝐞𝐫𝐚𝐭𝐢𝐨𝐧, 𝐂𝐥𝐚𝐬𝐬𝐢𝐟𝐢𝐜𝐚𝐭𝐢𝐨𝐧, 𝐚𝐧𝐝 𝐀𝐈 𝐀𝐠𝐞𝐧𝐭𝐬 𝐮𝐬𝐢𝐧𝐠 𝐈𝐧𝐬𝐭𝐫𝐮𝐜𝐭𝐨𝐫. More chapters are in the works to teach you how to 𝐜𝐫𝐞𝐚𝐭𝐞 𝐚𝐧𝐝 𝐞𝐱𝐩𝐥𝐨𝐢𝐭 𝐤𝐧𝐨𝐰𝐥𝐞𝐝𝐠𝐞 𝐠𝐫𝐚𝐩𝐡𝐬 𝐮𝐬𝐢𝐧𝐠 𝐈𝐧𝐬𝐭𝐫𝐮𝐜𝐭𝐨𝐫 𝐚𝐧𝐝 𝐍𝐞𝐨𝟒𝐣, 𝐡𝐨𝐰 𝐭𝐨 𝐛𝐮𝐢𝐥𝐝 𝐚𝐮𝐭𝐨𝐧𝐨𝐦𝐨𝐮𝐬 𝐀𝐈 𝐚𝐠𝐞𝐧𝐭𝐬, 𝐡𝐨𝐰 𝐭𝐨 𝐛𝐮𝐢𝐥𝐝 𝐟𝐢𝐧𝐚𝐧𝐜𝐢𝐚𝐥 𝐚𝐧𝐚𝐥𝐲𝐬𝐭𝐬, 𝐡𝐨𝐰 𝐭𝐨 𝐛𝐮𝐢𝐥𝐝 𝐠𝐞𝐧𝐀𝐈 𝐝𝐞𝐦𝐨𝐬 𝐮𝐬𝐢𝐧𝐠 𝐈𝐧𝐬𝐭𝐫𝐮𝐜𝐭𝐨𝐫 𝐚𝐧𝐝 𝐒𝐭𝐫𝐞𝐚𝐦𝐥𝐢𝐭, 𝐚𝐧𝐝 𝐦𝐨𝐫𝐞. Join Lycee AI's mailing list to receive updates on new chapters, access exclusive content, and connect with a community of like-minded enthusiasts and professionals. Stay informed about the latest developments and how you can leverage them to enhance your skills and projects. Sign up today and take your understanding of AI to the next level! https://lnkd.in/eAdcpWXh

"🔍✨ Exciting News! Dive into the world of interpretability and get interesting visual insights by applying the 'Logit Lens' to image data using Vision Transformers. 🚀 Discover a fresh perspective on understanding vision models. Check out the blog post now! This is a part of my work during the hackathon on AI safety organized at the end of the Turing Seminar course of the master MVA, which took place last weekend at École normale supérieure. Thanks to Charbel-Raphaël Segerie for your helpful comments and remarks! #interpretability #aisafety #visiontransformers #largelanguagemodels #innovation

GPT-4o and its problems with Spatial Intelligence: We put GPT-4o to the test with a challenging spatial intelligence puzzle https://lnkd.in/dgH8cu3n

The Toronto Machine Learning Society (TMLS) will host Dr. Yizhi Yin from Neo4j for an immersive workshop on Enabling GenAI Breakthroughs with Knowledge Graphs. 🌟 We'll explore the integration of Large Language Models (LLMs) with knowledge graphs using Neo4j, a leader in graph database and graph analytics, through a Retrieval Augmented Generation (RAG) approach. 🛠 Workshop Highlights: ✅ How to overcome LLM limitations with Retrieval Augmented Generation (RAG) ✅ How to build a personalized messenger app with RAG patterns for product recommendations ✅ Analyzing Vector Search Results with Graph Patterns ✅ Enriching Search Results Using Graph Data Science Methods You can see the full abstract here: https://lnkd.in/gwSRjPjz #GenAI #KnowledgeGraphs #tmls #neo4j

☀ Convergence 2024 is on the horizon and we're looking for speakers! 🔥 This May 8-9, the Convergence Conference will focus on the development of scalable #ML platforms, #AIGovernance, #LLMs, #FederatedML, and Vision Transformers. 👩🏫 The conference will feature expert-led talks, panels, and workshops designed to address both the technical nuances and the broader implications of #AI regulation for enterprises. 🎤 Applications are now open for speakers: #MachineLearning #GenAI Comet

Unlocking the Power of Supervised Learning: Navigating the Landscape of Intelligent Algorithms 🚀 Excited to share insights on how this cornerstone of machine learning is shaping the future! Join the conversation and dive into the world of supervised learning with me. #MachineLearning #SupervisedLearning #AIAdvancements #TechInnovation #DataScience

Completed a course on Artificial Intelligence

Our new White Paper, Lessons Learned 2.0 is now available to download. This White Paper sets out a bold new vision as to how the discipline of Lessons Learned management, specifically in the context of major engineering/construction build, can be reimagined and essentially presents a blueprint for Lessons Learned 2.0. Within our Resource Hub, you will also find an overview of Project LEAP and recordings of our GRIDtalk webinars that have previously touched on the subjects of Lessons Learned practices and AI. Access these resources here: https://lnkd.in/erRrfmyD

Are you curious about how to detect an object present in a video? 📹💡 Imagine the possibilities of enhancing video analysis and improving automation through advanced object detection techniques. Here's a glimpse of what you need to know: * Utilizing machine learning algorithms for accurate object identification * Implementing deep learning models for real detection * Enhancing video surveillance systems with object recognition technology Join the conversation and share your insights on the future of video analysis! What are your thoughts on the impact of object detection in various industries? #ObjectDetection #VideoAnalysis #MachineLearning #DeepLearning #TechInnovation 🔍💻 Feel free to like, comment, and share to engage with like-minded professionals in this exciting field!