courseraАнализ данных

Deep Learn Imagery

Курс от Coursera

Начальный≈ 3 чАнглийский

О курсе

This hands-on course proves that deep learning isn't just about pressing "run" on a model. It's about turning satellite imagery into actual, useful insights. You'll work with convolutional neural networks for land cover classification, fine-tune a pre-trained CNN using transfer learning, use data augmentation to improve performance, and apply Grad-CAM to see where the model is actually looking. Along the way, you'll practice translating raw satellite imagery into insights you can clearly communicate to others. You are required to have basic Python programming, familiarity with machine learning concepts, and introductory knowledge of neural networks and image data. Designed for beginners in machine learning and remote sensing, Deep Learn Imagery builds your confidence in both working with deep learning and explaining what your models are doing.

Навыки, которые вы освоите

Fine-tuning Technical Communication Convolutional Neural Networks Image Analysis Computer Vision Machine Learning Data Pipelines Deep Learning Transfer Learning Heat Maps Data Preprocessing Augmented Reality Geospatial Information and Technology Machine Learning Algorithms Model Optimization Applied Machine Learning Model Deployment

Программа курса

3 модулей · 20 учебных материалов

01Fine-Tuning CNNs for Land Cover7 материалов

Introduction and WelcomeВидео Why Pre-Trained CNNs Are Essential for Remote SensingDIALOGUE Fine-Tuning Pre-Trained CNNs and Feature Reuse for Satellite Imagery Видео Avoiding Spatial Data Leakage in Land-Cover Classification Models Чтение

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Professionals from the Industry

Преподаватель курса