• Machine Learning NTU 筆記
  • Introduction
  • 機器學習基石
    • Week1 The Learning Problem
    • Week2 Learning to Answer Yes/No
    • Week3 Types of Learning
    • Week4 Feasibility of Learning
    • Week5 Training vs Testing
    • Week6 Theory of Generalization
    • Week7 VC Dimension
    • Week8 Noise and Error
    • Ch9 Linear Regression
  • 機器學習技法
    • Ch1 Linear SVM
  • 李宏毅 Machine Learning
    • Lec 4: Classification
    • Lec 5: Logistic Regression
    • Lec 7: Backpropagation
    • Lec 8-1: “Hello world” of deep learning
    • Lec 9-1: Tips for Training DNN
    • Lec 10: Convolutional Neural Network
    • Lec 11: Why Deep?
    • Lec 12: Semi-supervised
    • Lec 13: Unsupervised Learning - Linear Methods
    • Lec 15: Unsupervised Learning - Neighbor Embedding
    • Lec 17: Unsupervised Learning - Deep Generative Model (Part I)
    • Lec 18: Unsupervised Learning - Deep Generative Model (Part II)
    • Lec 19: Transfer Learning
    • Lec 20: Support Vector Machine
    • Lec 22: Ensemble
    • Lec 23-1: Deep Reinforcement Learning
    • Lec 23-2: Policy Gradient
    • Lec 23-3: Reinforcement Learning (including Q-learning)
    • 2019 Life Long Learning (LLL)
    • 2019 Meta Learning
      • Automatic Hyperparameter Tuning
    • 2019 More about Auto-encoder
  • 李宏毅 Advanced Topics in Deep Learning
    • Deep Learning for Language Modeling
    • Spatial Transformer Layer
    • Highway Network & Grid LSTM
    • Conditional Generation by RNN & Attention
    • Generative Adversarial Network
    • Improved Generative Adversarial Network
    • RL and GAN for Sentence Generation and Chat-bot
    • Ensemble of GAN
    • Video Generation by GAN
    • Ensemble of GAN
  • 李宏毅 Deep Learning Theory
    • DLT 1-1: Can shallow network fit any function?
  • 李宏毅 GAN Lecture
    • 2017-1: Intro of GAN
    • 2017-2: CycleGAN
    • 2017-3: Improving Sequence Generation by GAN
    • 2017-3: Unsupervised Conditional Generation
    • 2017-4: From A to Z
    • 2018-3: Unsupervised Conditional Generation
    • 2018-7: Info GAN, VAE-GAN, BiGAN
    • 2018-9: Sequence Generation
    • 2018-10 Evaluation & Concluding Remarks
  • 李宏毅 Deep Reinforcement Learning
    • Lec 1: Policy Gradient (Review)
    • Lec 2: Proximal Policy Optimization (PPO)
    • Lec 3: Q-learning (Basic Idea)
    • Lec 4: Q-learning (Advanced Tips)
    • Lec 5: Q-learning (Continuous Action)
    • Lec 6: Actor-Critic
    • Lec 7: Sparse Reward
    • Lec 8: Imitation Learning
  • 清大吳尚鴻 Deep Learning
    • ML-17: Deep Reinforcement Learning
      • Prioritized Replay
  • CS231n - CNN for Visual Recognition (2017)
    • Lec 3 | Loss Functions and Optimization
    • Lec 10 | Recurrent Neural Networks
    • Lec 11 | Detection and Segmentation
    • Lec 12 | Visualizing and Understanding
    • Lec 16 | Adversarial Examples and Adversarial Training
  • 李宏毅 Deep Learning Theory
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Lec 10 | Recurrent Neural Networks

Lecture 10 | Recurrent Neural Networks

10.3 Image Captioning, Visual Q&A?, Soft Attention, LSTM, GRU?

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