Statistical Learning Theory: Conclusions on Deep Learning

Related lectures (32)

Page 3 of 4

Explores supervised learning as an ill-posed problem and the integration of sparse adaptive splines into neural architectures.

Explores the challenges and insights of deep learning, focusing on loss landscape, generalization, and feature learning.

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

Explores the impact of Deep learning on Digital Humanities, focusing on non conceptual knowledge systems and recent advancements in AI.

Covers deep learning concepts, focusing on graphs, transformers, and their applications in multimodal data processing.

Explores how modern architectures beat the curse of dimensionality and the importance of stability in deep learning models.

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

Explores neural networks, activation functions, backpropagation, and PyTorch implementation.

Explores language detection using Recurrent Neural Networks and supervised learning concepts.

Explores neural networks optimization, including backpropagation, batch normalization, weight initialization, and hyperparameter search strategies.