Dr. Fabian Schreiber’s Post

This new description of the transformer mechanism used in AI (machine learning) systems is so well done that I am going to post a pointer to it. The person who did this, Grant Sanderson, is a mathematician who has built tools for presenting concepts for his YouTube channel 3blue1brown and other ventures. He uses these tools to augment is excellent understanding of what is important. Anyone who has not fully understood the concepts of the attention mechanism and how it is used to build a transformer for machine learning as demonstrated in the 2017 paper "Attention is All You Need", take a look at this 1 in a million talk released on Nov 20, 2024 on YouTube: https://lnkd.in/gqP4t9W3 Cheers! ☕ Todd B.

Transformers are a foundational architecture in modern deep learning, particularly in natural language processing (NLP) and beyond. This video explains the workings, effectiveness, and applications of Transformers in tasks like text generation, translation, and classification. The presentation provides a clear, technical explanation of Transformers, focusing on their design, operation, and real-world impact. -Overview of Transformers: Introduced in the 2017 paper "Attention is All You Need," Transformers are versatile for NLP tasks and other applications. They rely on attention to process text, images, and audio flexibly. -Tokenization and Embeddings: Text is tokenized into units like words or subwords and converted into numerical vectors through embeddings, encoding meaning and context. -Attention Mechanism: The core component, attention, uses "queries," "keys," and "values" through matrix multiplication to determine relationships between tokens. This updates token embeddings to reflect contextual meaning and dependencies. -Multi-Head Attention and Depth: Multi-head attention enables the model to focus on different input aspects in parallel. Layers of attention blocks and multi-layer perceptrons (MLPs) refine token representations. -Training and Parallelization: Transformers are trained to predict the next token in a sequence using vast data. The architecture is optimized for GPU-based parallel computations, enabling efficient training despite complexity. -Applications and Scalability: Transformers have revolutionized various fields. The tokenization of non-text data, such as images and sounds, highlights their adaptability. -Interpretability and Challenges: The speaker discusses interpretability challenges, like understanding how and where knowledge is stored in models, and highlights intriguing properties of high-dimensional vector spaces. -Future Directions and Q&A: The talk concludes with ongoing research topics like the superposition hypothesis and innovations for handling larger contexts in modern models. https://lnkd.in/e2-bskqd

🚀 Excited to share my latest Medium post on YOLOv9 training with a custom dataset! 🎥🔍 #yolov9 #computervision #ai #deeplearning #machinelearning #letsconnect

Guys, I had announced a PM-related event that we would host last week, and so we did! Even as I was traveling - I held the meeting at a Starbucks in lovely Tuscaloosa, Alabama 💖😎 So, David Wertheimer, a dear Friend of Mr. Simon, gave an excellent presentation on using GenAI in Products that rely on language models. He zoomed in on a very interesting aspect called RAG, which has very clear, high-demand business use cases. Great topic, stellar presentation, and intelligent questions from the audience - I highly recommend watching! Here's the recording:

Do you often encounter challenges with computer vision or Gen AI projects due to images with low brightness or resolution? These issues can impact the quality of annotation and curation processes, and even result in failures of AI models after deployment. The root cause often lies in 👉 false negatives, where objects present in the images are missed due to low brightness. However, identifying which images suffer from low brightness remains unclear, as your curation pipeline fails to provide that crucial information. Have a quick peek into this https://lnkd.in/gzZ7PtQ5

🚀 Exciting times ahead! Just wrapped up training a custom object detection model on a unique dataset! 🖥️🔍 Leveraged the power of YOLOv10 to fine-tune the model. The results are looking promising—high accuracy and fast inference times! 💯 Stay tuned for more updates as we push the boundaries of what's possible with computer vision. 👁️✨ #Pyresearch #AI #MachineLearning #ComputerVision #ObjectDetection #DeepLearning #TechInnovation #CustomModel https://lnkd.in/dzKgewZQ

I recently gave a talk on Generative AI, vector databases, and Retrieval-Augmented Generation—a fascinating approach to enhancing the capabilities of large language models using proprietary data. If you're interested in learning more, be sure to check out the video. Feel free to reach out with any questions! #genai #llm #rag

🚀 Ready to take Object Detection to the next level? Meet YOLOv10! 🖥️⚙️ We’re diving deep with YOLOv10 to train on a custom dataset for ultra-accurate, real-time object detection. From enhancing safety to optimizing logistics, this powerful model is redefining how machines "see" the world around them. 💡 Why YOLOv10? It's faster, smarter, and more precise—perfect for any application that demands high performance and real-time results. Stay tuned as we push the boundaries of AI vision tech and bring innovative solutions to life! 🔥 #Pyresearch #YOLOv10 #ObjectDetection #ComputerVision #MachineLearning #AI https://lnkd.in/d-6SD_p6

🚀 Training YOLOv10 for Next-Level Object Detection! 🎯 I’m thrilled to dive into the future of object detection with **YOLOv10**, the latest evolution in the YOLO family! 🔥 With enhanced speed and accuracy, it’s perfect for detecting objects on **custom datasets** in real-time. Whether it’s complex environments or niche applications, YOLOv10 is set to redefine the standards of **AI-powered detection**. 📊⚡ From dataset preparation to training and fine-tuning, I’m pushing the limits of what's possible with state-of-the-art **computer vision**. Excited to share more insights and breakthroughs soon! 💡 #Pyresearch #YOLOv10 #ObjectDetection #AI #ComputerVision #CustomDataset #DeepLearning #MachineLearning #RealTimeDetection https://lnkd.in/d4Hast25

🚀 **Just trained YOLOv10 for Object Detection on a Custom Dataset!** 🎯 After a deep dive into data preprocessing, annotation, and fine-tuning, I’ve successfully trained YOLOv10 to recognize [insert your objects here]. The accuracy and speed are off the charts! 🔥 Whether you’re into AI, machine learning, or just love tech, this is a game-changer for real-time object detection. Stay tuned for more updates and insights! 💡 #Pyresearch #YOLOv10 #ObjectDetection #MachineLearning #AI #DeepLearning #TechInnovation #CustomDataset https://lnkd.in/dgbznr54