Overview of Image Segmentation with AI
Explore AI-powered image segmentation techniques, from basic concepts to advanced deep learning structures, and learn steps to create effective models for object identification and edge detection.
Design and Development of Autonomous Racecar
Explore autonomous racecar development: perception, localization, mapping, path planning, and vehicle control. Gain insights into creating self-driving vehicles using advanced technologies.
Feeding ML Models with Real-Time Database Data
Explore real-time data ingestion from databases to ML models using Change Data Capture. Learn about Debezium, challenges, and future improvements with a live TensorFlow demo.
Introduction to Artificial Intelligence: Deep Neural Networks - Lecture 19
Explore deep neural networks, edge detection, and reinforcement learning in AI, with applications to games and image processing.
Is C++23 std::mdspan a Zero-overhead Abstraction? - Lecture
Explore C++23 std::mdspan's efficiency as a zero-overhead abstraction for multi-dimensional arrays through matrix and image operations, comparing assembly output to low-level implementations.
Leveraging the Power of C++ for Efficient Machine Learning on Embedded Devices
Explore practical C++ and TensorFlow Lite integration for efficient machine learning on embedded devices. Includes image classification and hand gesture recognition demonstrations on Raspberry Pi.
Energy-Efficient ML Vision App Development with Ethos-U65 - NXP and Arcturus
Leverage Ethos-U65 microNPU and Cortex-A55 CPUs on NXP's i.MX 93 for energy-efficient ML vision apps. Explore NXP's eIQ ML environment and Arcturus solutions for smart occupancy sensing and edge vision applications.
How Well Do Sparse Models Transfer? - Exploring Transfer Performance in Computer Vision and NLP
Explore sparse models' transfer performance and efficiency gains in image and language tasks, outperforming dense models even at high sparsities.
Applying Second-Order Pruning Algorithms for SOTA Model Compression
Explore second-order pruning algorithms for model compression, achieving higher sparsity while maintaining accuracy. Learn to apply these techniques to your ML projects for improved efficiency.
Pruning and Quantizing ML Models With One Shot Without Retraining
Adapt advanced pruning and quantization methods for ML models, achieving 4X speedup with 99% accuracy in minutes, using Computer Vision and NLP examples.
Deploy Fast and Accurate YOLOv8 Object Detection Models on CPUs
Optimize YOLOv8 models for 10x smaller, 8x faster performance on CPUs. Learn sparsification techniques, quantization, and deployment strategies for efficient computer vision applications.
Hugging Face and Ray AIR Integration for Scalable Machine Learning
Explore Hugging Face and Ray AIR integration for scalable model training and data loading. Learn to create end-to-end workflows from data ingest to inference and serving.
Photographic Image Priors in the Era of Machine Learning
Explore image processing advancements using machine learning, focusing on implicit priors in neural networks for tasks like denoising, deblurring, and super-resolution.
The New Era of Image Denoising
Explore revolutionary image denoising techniques, their applications in inverse problems, and the concept of diverse solutions in image processing.
Sketching Semidefinite Programs for Super-Resolution Problems
Explore super-resolution problems, focusing on measure recovery from Fourier coefficients. Learn about sketching approaches to reduce semidefinite program size and algorithmic developments for integral bounds.
A Path Towards Autonomous Machine Intelligence
Explore Yann LeCun's vision for autonomous machine intelligence, delving into cutting-edge AI concepts and potential future developments in the field.
Advanced Computational Imaging - Chassez le Naturel, Il Revient au Galop
Explorez les avancées en imagerie computationnelle, en mettant l'accent sur la stabilité et la fiabilité des réseaux neuronaux profonds pour la reconstruction d'images biomédicales.
The Promise and Challenge of Harnessing Collateral Information from Medical Images Using AI
Explore AI's potential in extracting collateral information from medical images, discussing promises and challenges in this field.
Validation of AI Techniques in MRI for Clinical Studies
Explore AI validation techniques for MRI in clinical studies, focusing on enhancing accuracy and reliability in medical imaging analysis.
Elucidating the Design Space of Diffusion-Based Generative Models
Explore advancements in diffusion-based generative models, including improved sampling, training, and score network preconditioning. Learn about state-of-the-art results and modular design changes for enhanced efficiency and quality.
Diffusion Models for Inverse Problems
Explore advanced techniques for solving inverse problems using diffusion models, focusing on posterior sampling and manifold constraints.