
Explore deep learning for computer vision, covering CNNs, object detection, and image segmentation. Learn about architectures, applications, and impacts in various fields.

Learn object segmentation using StarDist in Python for star-convex shapes in 2D and 3D. Covers installation, implementation, and HD image segmentation with practical examples.

Explora la aplicación de redes neuronales convolucionales en automatización industrial, incluyendo simulación de selección de objetos con actuadores y aspectos teórico-prácticos de implementación.

Proyecto Centinela: sistema de seguridad personalizado con Raspberry Pi y TensorFlow. Reconocimiento facial, almacenamiento local, alertas en tiempo real y integración con dispositivos inteligentes para mayor control y privacidad.

Desarrolla una aplicación de detección de objetos en C# utilizando Fast RCNN, ONNX y CPU para inferencia eficiente.

Train object detection models with PyTorch on Jetson Nano, collect custom datasets, and create personalized models for real-time detection using SSD MobileNet and Open Images dataset.

Explore cutting-edge AI deployment for robotics and edge computing, covering tools, techniques, and recent developments in deep learning and NVIDIA's Jetson platform.

Learn to train and implement custom object detection on Jetson Nano, from image capture and labeling to using a Single Shot Detection Network for real-time robot recognition in video streams.

Learn to print, assemble, and program a SMARS Quad robot using Raspberry Pi Zero and Python. Explore advanced features like computer vision, voice synthesis, and voice recognition in this hands-on series.

Learn to install NVIDIA object detection and inference tools on Jetson Nano, enabling sophisticated pretrained networks for object identification and detection in AI applications.

Explore cutting-edge algorithms for robust geometric perception in robotics, enhancing safety and reliability in autonomous systems across various applications.

Train an ESP32-CAM for object detection using Edge Impulse, without coding. Learn to capture images, create a model, and deploy it on microcontrollers for efficient and cost-effective object recognition.

Explore machine learning on ESP32: object detection, image recognition, keyword spotting, time series data, and LLMs. Learn to implement AI using EdgeImpulse and Google TensorFlow Lite for Microcontrollers.

Explore Earth observation using large vision models for infrastructure planning and disaster response. Learn to access satellite datasets, fine-tune models, and apply zero-shot classification and image translation techniques.

Build an end-to-end image segmentation pipeline using deep learning. Learn dataset customization, model training, validation, testing, and mobile deployment techniques.

Explore the intersection of machine learning and microscopy, focusing on AI models for active learning in materials discovery, automated experiments, and physics discovery through advanced microscopy techniques.

Explore geometric deep learning techniques for object detection and segmentation in cryo-electron micrographs, focusing on unsupervised methods and graph-based transformers for complex structures.

Learn to create a fall detection app using YOLOv7 pose estimation, covering UI design, push-up counting logic, and implementation on both CPU and GPU. Explore real-world applications of computer vision in healthcare.

Explore Apache OpenWhisk's serverless platform capabilities for cognitive, IoT, and mobile workloads. Learn real-world use cases, architecture benefits, and integrations for building efficient APIs and backends.

Explore open-source AI solutions for edge devices using Panfrost, GStreamer, and TensorFlow Lite. Learn about object detection, delegation, and performance results.

Explore the exciting possibilities of machine learning using TensorFlow.js. Learn to implement object detection, image classification, and other ML techniques in web applications.