Machine learning services in IoT platforms

Related lectures (30)

Page 3 of 3

Delves into neural network approximation, supervised learning, challenges in high-dimensional learning, and deep learning experimental revolution.

Introduces the basics of data science, covering decision trees, machine learning advancements, and deep reinforcement learning.

Introduces Convolutional Neural Networks, explaining their architecture, training process, and applications in semantic segmentation tasks.

Explores the Random Feature Method for solving PDEs using machine learning algorithms to approximate high-dimensional functions efficiently.

Explores learning in deep learning for autonomous vehicles, covering predictive models, RNN, ImageNet, and transfer learning.

Introduces deep learning fundamentals, covering data representations, neural networks, and convolutional neural networks.

Explores convolutional neural networks, backpropagation, and stochastic gradient descent in deep learning.

Covers stylized facts of asset returns, summary statistics, testing for normality, Q-Q plots, and efficient market hypothesis.

Covers the fundamentals of deep learning, from artificial neurons to modern networks.

Explores the development and improvements of dhSegment, an open-source document segmentation package using PyTorch.