Hung-yi Lee 李宏毅 - 國立臺灣大學

Introduction of Reinforcement Learning - 國立臺灣大學

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

•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

Convolutional Neural Network - 國立臺灣大學

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

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

  Search

Machine Learning PyTorch Tutorial - 國立臺灣大學

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

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

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

Convolutional Neural Network - 國立臺灣大學

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

AUTO-ENCODER

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

Generation - NTU Speech Processing Laboratory

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 ...

•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

PCT: Point Cloud Transformer - arXiv

Transformer, a positional encoding module is applied to represent the word order in nature language. This can distinguish the same word in different positions and reflect the positional relationships between words. ... graph convolution learning [3]. From this perspective,

  Cloud, Points, Transformers, Graph, Point cloud transformer

Question paper (Higher) : Paper 2 - Sample set 1 - AQA

transformer varies as the potential difference across the primary coil of each transformer is changed. Figure 13 How can you tell that transformer J is a step-down transformer? [1 mark] Each of the transformers has 50 turns on the primary coil. Calculate the number of turns on the secondary coil of transformer L.

  Paper, Transformers

Transformer Interpretability Beyond Attention Visualization

Transformer networks necessitates tools for the visualiza-tion of their decision process. Such a visualization can aid indebuggingthemodels,helpverifythatthemodelsarefair and unbiased, and enable downstream tasks. The main building block of Transformer networks are self-attention layers [29, 7], which assign a pairwise atten-

  Transformers

Diverse Part Discovery: Occluded Person Re-Identification ...

Aware Transformer for occluded person Re-ID through di-verse part discovery via a transformer encoder-decoder ar-chitecture, including a pixel context based transformer en-coder and a part prototype based transformer decoder. To the best of our knowledge, our PAT is the first work by exploiting the transformer encoder-decoder architecture for

  Transformers

Graph Transformer Networks - NeurIPS

Unlike these approaches, our Graph Transformer Networks can operate on a heterogeneous graph and transform the graph for tasks while learning node representation on the transformed graphs in an end-to-end fashion. 3 Method The goal of our framework, Graph Transformer Networks, is to generate new graph structures and

  Transformers, Graph, Graph transformer

Pearson Edexcel International GCSE Physics

Jun 17, 2016 · • a transformer that is connected to the wire grid (a) The ultraviolet lamp attracts many flying insects towards the device. Ultraviolet is an electromagnetic wave. (i) State two properties of electromagnetic waves. (2) 1

  Transformers

Unit: KPH0/4PH0 Paper: 2P - Edexcel

Jun 12, 2015 · 6 *P44231A0620* 2 An electric kettle is connected to the 230 V mains supply. The power of the kettle is 960 W. (a) (i) A power of 960 watts is the same as (1) A 960 joules per coulomb B 960 joules per second C 960 newtons per metre D 960 newtons per second (ii) State the equation linking power, current and voltage.

Switching regulator fundamentals (Rev. C) - Texas …

1.2 Transformer Operation A transformer is a device that has two or more magnetically-coupled windings. The basic operation is shown in Figure 2. Figure 2. Transformer Theory The action of a transformer is such that a time-varying (AC) voltage or current is transformed to a higher or lower value, as set by the transformer turns ratio.

  Switching, Fundamentals, Texas, Regulators, Transformers, Switching regulator fundamentals