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AI in Medicine and Imaging - Stanford Symposium 2020
Through the AIMI Symposium we hope to address gaps and barriers in the field and catalyze more evidence-based solutions to improve health for all.
health medicine medical-imaging stanford
Named Entity Recognition Tagging
In this post, we go through an example from Natural Language Processing, in which we learn how to load text data and perform NER tagging for each token.
named-entity-recognition cs230 stanford recurrent-neural-networks
CS229: Machine Learning
A broad introduction to machine learning and statistical pattern recognition.
course machine-learning stanford cs229
CS231n: Convolutional Neural Networks for Visual Recognition
Deep dive into details of the deep learning architectures with a focus on learning end-to-end models for these tasks, particularly image classification.
convolutional-neural-networks computer-vision deep-learning cs231n
CS224n: Natural Language Processing with Deep Learning
In this course, students will gain a thorough introduction to cutting-edge research in Deep Learning for NLP.
natural-language-processing deep-learning cs224n stanford
CS330: Deep Multi-Task and Meta Learning
Study how the structure arising from multiple tasks can be leveraged to learn more efficiently or effectively.
multi-task-learning meta-learning cs330 stanford
Lecture 10 | Recurrent Neural Networks
Discuss the use of recurrent neural networks for modeling sequence data.
recurrent-neural-networks gated-recurrent-units lstm language-modeling
Module 2: Convolutional Neural Networks - CS231n
In Lecture 5 we move from fully-connected neural networks to convolutional neural networks.
convolutional-neural-networks cs231n stanford video-games
Module 1: Neural Networks - CS231n
These notes accompany the Stanford CS class CS231n: Convolutional Neural Networks for Visual Recognition.
multilayer-perceptrons cs231n stanford tutorial
Linear classification: Support Vector Machine, Softmax
The SVM loss is set up so that the SVM “wants” the correct class for each image to a have a score higher than the incorrect classes by some fixed margin Δ.
support-vector-machines cs231n stanford tutorial
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