Optimizers

Convolutional neural network (CNN)

Image Classification

BERT

Transformer (machine learning model)

Machine learning

Network Architectures

Neural network

Segmentation and Detection

Deep Reinforcement Learning

Optimization

Sequential Data

Generative adversarial network (GAN)

CSE 490 G1 / 599 G1 Introduction to Deep Learning University of Washington Autumn 2019 A survey course on neural network implementation and applications, including image processing, classification, detection, and segmentation. The course also covers semantic understanding, translation, and question-answering applications. It's ideal for those with a background in Machine Learning, Neural Networks, Optimization, and CNNs. A survey class of neural network implementation and applications. Topics include: optimization - stochastic gradient descent, adaptive and 2nd order methods, normalization; convolutional neural networks - image processing, classification, detection, segmentation; recurrent neural networks - semantic understanding, translation, question-answering; cross-domain applications - image captioning, vision and language.   

CSE 490 G1 / 599 G1 Introduction to Deep Learning

Deep Learning

Optimizers

1 courses cover this concept

CSE 490 G1 / 599 G1 Introduction to Deep Learning