Machine Learning 2020 - NTU Speech Processing Laboratory

Text-to-Speech Synthesis Machine Translation Text (Chinese) Text (English) ... •All the assignments have sample codes based on Python. •You need to be able to read and modify the sample ... 3/12 Deep Learning Classification 3/19 Theory of ML (Prof. Pei-Yuan Wu) 3/26 Self-attention CNN / Self-attention

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  Based, Texts, Classification, Learning, Deep, Deep learning classification

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Convolutional Neural Network - 國立臺灣大學

speech.ee.ntu.edu.tw

Fully Connected Feedforward network output. ... object detection and semantic segmentation”, CVPR, 2014. Convolution Max Pooling Convolution Max Pooling input 25 3x3 filters 50 3x3 ... “Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps”, ICLR, 2014 | ...

  Network, Fully, Segmentation, Neural, Convolutional, Convolutional networks, Semantics, Convolutional neural networks, Semantic segmentation

Introduction of Reinforcement Learning - 國立臺灣大學

speech.ee.ntu.edu.tw

Scenario of Reinforcement Learning Agent Environment Observation Action Don’t do Reward that State Change the environment

  Introduction, Learning, Reinforcement, Reinforcement learning

Self-supervised Learning

speech.ee.ntu.edu.tw

•Corpus of Linguistic Acceptability (CoLA) •Stanford Sentiment Treebank (SST-2) •Microsoft Research Paraphrase Corpus (MRPC) •Quora Question Pairs (QQP) ... Sentiment analysis Random initialization Init by pre-train This is the model to be learned. this is good

  Analysis, Learning, Self, Supervised, Pruco, Sentiment, Sentiment analysis, Self supervised learning

Machine Learning PyTorch Tutorial - 國立臺灣大學

speech.ee.ntu.edu.tw

PyTorch Tutorial TA：張恆瑞 (Heng-Jui Chang) 2021.03.05. Outline Prerequisites What is PyTorch? PyTorch v.s. TensorFlow Overview of the DNN Training Procedure ... C++, JavaScript, Swift Debug Easier Difficult (easier in 2.0) Application Research Production. Overview of the DNN Training Procedure Define Neural Network Loss Function Optimizer ...

  Tutorials, Swift, Pytorch, Pytorch tutorial

Convolutional Neural Network - 國立臺灣大學

speech.ee.ntu.edu.tw

Convolutional Neural Network (CNN) Network Architecture designed for Image 1. Image Classification Model ... Benefit of Convolutional Layer Fully Connected Layer •Some patterns are much smaller than the whole image. Receptive Field …

  Network, Neural, Convolutional, Convolutional neural networks

Generation - NTU Speech Processing Laboratory

speech.ee.ntu.edu.tw

minimize cross entropy = class 1 class 2 Train a binary classifier . Discriminator ... Image Style Transfer Domain Domain ... Unsupervised Conditional Generation . Learning from Unpaired Data Network 70 Domain Domain ...

  Generation, Cross, Image, Domain, Unsupervised, Domain domain

You can listen to the English version of this course at ...

speech.ee.ntu.edu.tw

•Math: Calculus (微積分), Linear algebra (線性代數) and Probability (機率) •Programming •All the assignments have sample codes based on Python. •You need to be able to read and modify the sample codes. This course will not teach Python. •This course will not teach any Python package, except PyTorch. •Only focus on ML.

  Linear, Algebra, Linear algebra

Transformer

speech.ee.ntu.edu.tw

Beam Search A B A B A B A B A B A B A B 0.4 0.9 0.9 0.6 0.4 0.4 0.6 0.6 The green path is the best one. Not possible to check all the paths … Assume there are only two tokens (V=2). The red path is Greedy Decoding. →Beam Search

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AUTO-ENCODER

speech.ee.ntu.edu.tw

Vincent, Pascal, et al. "Extracting and composing robust features with denoising autoencoders." ICML, 2008. Add noises The idea sounds familiar? ☺ ...

  Feature, With, Robust, Extracting, Composing, Autoencoder, Denoising, Extracting and composing robust features with denoising autoencoders

Hung-yi Lee 李宏毅 - 國立臺灣大學

speech.ee.ntu.edu.tw

Vector Set as Input 10ms 25ms 400 sample points (16KHz) 39-dim MFCC 80-dim filter bank output frame 1s →100 frames 4

  Hung

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Disease Prediction Using Machine Learning

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success of deep learning in disparate areas of machine learning has driven a shift towards machine learning models ... number of data to improve the accuracy of risk classification ... We not only use structured data, but also the text data of the patient based on the proposed k-mean algorithm. To find that out, we combine both data, and the ...

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Heterogeneous Graph Attention Networks for Semi …

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2.2 Deep neural networks for Text Classification Deep neural networks which automatically rep-resent texts as embeddings, have been widely ... supervised learning method based on SVM to la-bel the unlabeled documents in an iterative way. ... text classification based on a new dual-level atten-tion mechanism. HGAT considers the heterogene-

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Deep One-Class Classification

proceedings.mlr.press

Deep One-Class Classification Lukas Ruff* 1 Robert A. Vandermeulen* 2 Nico Gornitz¨ 3 Lucas Deecke4 Shoaib A. Siddiqui2 5 Alexander Binder6 Emmanuel Muller¨ 1 Marius Kloft2 Abstract Despite the great advances made by deep learn-ing in many machine learning problems, there is a relative dearth of deep learning approaches for anomaly detection.

  Class, Classification, Learning, Learn, Deep, Deep learning, Deep one class classification, Deep learn ing

Introduction to Psychological Disorders

www.ahschools.us

Learning Factors •Through classical conditioning people may associate fear with an object. •Observational learning--watching another experiencing fearfulness--may result in developing fear. •Fear of an object may be reinforced when by avoiding the feared objects.

  Learning

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