Explaining CNN Models with Explainable AI (XAI) Techniques | Grad-CAM, Saliency Maps скачать в хорошем качестве

Explaining CNN Models with Explainable AI (XAI) Techniques | Grad-CAM, Saliency Maps

Explaining CNN Models with Explainable AI (XAI) Techniques | Grad-CAM, Saliency Maps, Gradient Backpropagation In this video, we dive deep into Explainable AI (XAI) applied to Convolutional Neural Networks (CNNs). 🤖✨ We explore gradient-based and activation visualization techniques that help us understand what CNNs are seeing and why they make certain decisions. 🔍 Topics Covered: What is Explainable AI (XAI) in deep learning? Overview of CNN architecture and the need for interpretability Saliency Maps: Highlighting important regions in input images Gradient Backpropagation: Tracing contributions of input pixels Grad-CAM: Localizing class-discriminative regions using gradients and activations Visualizing internal activations and feature maps Whether you're a machine learning enthusiast, AI researcher, or student, this video will help you build intuition about how CNNs work under the hood and how to debug or trust their decisions. 📊 Visualizations Included! See exactly how images are processed and interpreted by the models using real examples. 👍 Like | 💬 Comment | 🔔 Subscribe for more deep learning explainability content! #ExplainableAI #CNN #GradCAM #SaliencyMap #DeepLearning #XAI #MachineLearning #ActivationMaps #ComputerVision #AIInterpretability #GradientBackpropagation #NeuralNetworks #DataScience #ArtificialIntelligence