Photography Style Transfer

Related lectures (30)

Page 3 of 3

Explores the application of statistical physics in understanding deep learning with a focus on neural networks and machine learning challenges.

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

Covers PyTorch tensor data structure and training a CNN to classify images.

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

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

Covers data representations, Bag of Words, histograms, data pre-processing, and neural networks.

Explores Bayesian classification, decision making, and pattern recognition applications in image processing.

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

Explores a 2019 paper on image recognition, dataset challenges, biases, and the impact of large-scale datasets on deep learning models.

Covers the No Free Lunch Theorem and the role of inductive bias in deep learning and reinforcement learning.