Deep learning in the IoT context

Related lectures (32)

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Covers hand pose estimation, regression techniques, and the evolution of image classification models from LeNet to VGG19.

Covers Convolutional Neural Networks, standard architectures, training techniques, and adversarial examples in deep learning.

Covers the basics of pedagogy, lesson structure, video capsules, and student evaluation.

Explores privacy technologies, data protection, surveillance risks, and Privacy Enhancing Technologies for social and institutional privacy.

Introduces unsupervised learning through clustering with K-means and dimensionality reduction using PCA, along with practical examples.

Covers the implementation of fully connected neural networks on two datasets using PyTorch.

Delves into the application of machine learning in enhancing human rights documentation and advocacy for organizations like HURIDOCS.

Covers fundamental principles, opportunities, and challenges in machine learning.

Explores cloud computing, data aggregation, security risks, privacy defenses, and data protection techniques.

Covers the basics of neural networks, focusing on the development from two layers neural networks to deep neural networks.