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    台湾大学李宏毅机器学习课程-

    共111节2599人学习CSDN讲师 课程详情 领证书
    • 机器学习-1

      • ML Lecture 0-1 Introduction of Machine Learning
      • ML Lecture2 Why we need to learn machine learning
      • ML Lecture 1 Regression - Case Study
      • ML Lecture 1 Regression - Demo
      • ML Lecture 2 Where does the error come from
      • ML Lecture 3-1 Gradient Descent
      • ML Lecture 3-2 Gradient Descent (Demo by AOE)
      • ML Lecture 3-3 Gradient Descent
      • ML Lecture 4 Classification
      • ML Lecture 5 Logistic Regression
      • ML Lecture 6 Brief Introduction of Deep Learning
      • ML Lecture 7 Backpropagation
      • ML Lecture 8-1 “Hello world” of deep learning
      • ML Lecture 8-2 Keras 2.0
      • ML Lecture 8-3 Keras Demo
      • ML Lecture 9-1 Tips for Training DNN
      • ML Lecture 9-2 Keras Demo 2
      • ML Lecture 9-3 Fizz Buzz in Tensorflow (sequel)
      • ML Lecture 10 Convolutional Neural Network
      • ML Lecture 11 Why Deep
      • ML Lecture 12 Semi-supervised
      • ML Lecture 13 Unsupervised Learning
      • ML Lecture 14 Unsupervised Learning
      • ML Lecture 15 Unsupervised Learning
      • ML Lecture 16 Unsupervised Learning - Auto-encoder
      • ML Lecture 17 Unsupervised Learning
      • ML Lecture 18 Unsupervised Learning
      • ML Lecture 19 Transfer Learning
      • ML Lecture 20 Support Vector Machine (SVM)
      • ML Lecture 21-1 Recurrent Neural Network (Part I)
      • ML Lecture 21-2 Recurrent Neural Network (Part II)
      • ML Lecture 22 Ensemble
      • ML Lecture 23-1 Deep Reinforcement Learning
      • ML Lecture 23-2 Policy Gradient
      • ML Lecture 23-3 Reinforcement Learning
    • 机器学习-2

      • Anomaly Detection (1 7)
      • Anomaly Detection (2 7) (2)
      • Anomaly Detection (3 7)
      • Anomaly Detection (4 7)
      • Anomaly Detection (5 7)
      • Anomaly Detection (6 7)
      • Anomaly Detection (7 7)
      • Attack ML Models (1 8)
      • Attack ML Models (2 8)
      • Attack ML Models (3 8)
      • Attack ML Models (4 8)
      • Attack ML Models (5 8)
      • Attack ML Models (6 8)
      • Attack ML Models (7 8)
      • Attack ML Models (8 8)
      • Explainable ML (1 8)
      • Explainable ML (2 8)
      • Explainable ML (3 8)
      • Explainable ML (4 8)
      • Explainable ML (5 8)
      • Explainable ML (6 8)
      • Explainable ML (7 8)
      • Explainable ML (8 8)
      • The Next Step for Machine Learning
    • 高级机器学习

      • Review Basic Structures for Deep Learning Models-1
      • Review Basic Structures for Deep Learning Models-2
      • Computational Graph & Backpropagation
      • Deep Learning for Language Modeling
      • Spatial Transformer Layer
      • Highway Network & Grid LSTM
      • Recursive Network
      • Conditional Generation by RNN & Attention
      • Pointer Network
      • Batch Normalization
      • SELU
      • Tuning Hyperparameters
      • Interesting things about deep learning
      • Generative Adversarial Network
      • Improved Generative Adversarial Network
      • RL and GAN for Sentence Generation and Chat-bot
      • 機械学習で美少女化 ~ あるいはNEW GAME! の世界
      • Imitation Learning
      • Evaluation of Generative Models
      • Ensemble of GAN
      • Energy-based GAN
      • Video Generation by GAN
      • A3C
      • Gated RNN and Sequence Generation
    • 深度学习理论

      • Deep Learning Theory 1-1
      • Deep Learning Theory 1-2 Potential of Deep
      • Deep Learning Theory 1-3
      • Deep Learning Theory 2-1 When Gradient is Zero
      • Deep Learning Theory 2-2 Deep Linear Network
      • Deep Learning Theory 2-3
      • Deep Learning Theory 2-4
      • Deep Learning Theory 2-5
      • Deep Learning Theory 3-1
      • Deep Learning Theory 3-2
    • 深度强化学习

      • DRL Lecture 1 Policy Gradient (Review)
      • DRL Lecture 2 Proximal Policy Optimization (PPO)
      • DRL Lecture 3 Q-learning (Basic Idea)
      • DRL Lecture 4 Q-learning (Advanced Tips)
      • DRL Lecture 5 Q-learning (Continuous Action)
      • DRL Lecture 6 Actor-Critic
      • DRL Lecture 7 Sparse Reward
      • DRL Lecture 8 Imitation Learning
    • 对抗生成网络GAN

      • GAN Lecture 1 (2018) Introduction
      • GAN Lecture 2 (2018) Conditional Generation
      • GAN Lecture 3Unsupervised Conditional Generation
      • GAN Lecture 4 (2018) Basic Theory
      • GAN Lecture 5 (2018) General Framework
      • GAN Lecture 6 (2018) WGAN, EBGAN
      • GAN Lecture 7 (2018) Info GAN, VAE-GAN, BiGAN
      • GAN Lecture 8 (2018) Photo Editing
      • GAN Lecture 9 (2018) Sequence Generation
      • GAN Lecture 10Evaluation & Concluding Remarks
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