Transformer
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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Introduction of Reinforcement Learning - 國立臺灣大學
speech.ee.ntu.edu.twScenario of Reinforcement Learning Agent Environment Observation Action Don’t do Reward that State Change the environment
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
Convolutional Neural Network - 國立臺灣大學
speech.ee.ntu.edu.twFully 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 | ...
Machine Learning PyTorch Tutorial - 國立臺灣大學
speech.ee.ntu.edu.twPyTorch 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 ...
Machine Learning 2020 - NTU Speech Processing Laboratory
speech.ee.ntu.edu.twText-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
Convolutional Neural Network - 國立臺灣大學
speech.ee.ntu.edu.twConvolutional 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 …
AUTO-ENCODER
speech.ee.ntu.edu.twVincent, Pascal, et al. "Extracting and composing robust features with denoising autoencoders." ICML, 2008. Add noises The idea sounds familiar? ☺ ...
Generation - NTU Speech Processing Laboratory
speech.ee.ntu.edu.twminimize 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 ...
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.
Hung-yi Lee 李宏毅 - 國立臺灣大學
speech.ee.ntu.edu.twVector Set as Input 10ms 25ms 400 sample points (16KHz) 39-dim MFCC 80-dim filter bank output frame 1s →100 frames 4
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1 Squeeze-and-Excitation Networks - arXiv
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INTRODUCTION MACHINE LEARNING
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AutoAugment: Learning Augmentation Strategies From Data
openaccess.thecvf.comFigure 1. Overview of our framework of using a search method (e.g., Reinforcement Learning) to search for better data augmen-tation policies. A controller RNN predicts an augmentation policy from the search space. A child network with a fixed architecture is trained to convergence achieving accuracy R. The reward R will
ShuffleNet: An Extremely Efficient Convolutional Neural ...
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4 Perceptron Learning - fu-berlin.de
page.mi.fu-berlin.de“learning”. A learning algorithm must adapt the network parameters accord-ing to previous experience until a solution is found, if it exists. 4.1.1 Classes of learning algorithms Learning algorithms can be divided into supervised and unsupervised meth-ods. Supervised learning denotes a method in which some input vectors are