第 6 节，共 23 节
Lecture 6 | Normalization Initialization
课时23 节课
- 1Lecture 1 | Introduction to Neural Networks
- 2Lecture 2 | Gradient Descent, Linear Regression
- 3Lecture 3 | Logistic Regression, Regularization, Softmax Classifier
- 4Lecture 4 | Stochastic Gradient Descent, Back Propagation
- 5Lecture 5 | Tensorflow (part 1)
- 6Lecture 6 | Normalization Initialization
- 7Lecture 7 | Dropout, Batch Normalization
- 8Lecture 8 | Adaptive Momentum (ADAM), Tensorflow keras
- 9Lecture 9 | Convolution
- 10Lecture 10 | Famous Convolutional Neural Networks
- 11Lecture 11 | Receptive Field, Transfer Learning
- 12Lecture 12 | Multitask Learning Metrics
- 13Lecture 13 | Recurrent Neural Networks
- 14Lecture 14 | Long short-term memory (LSTM)
- 15Lecture 15 | Attention Transformers
- 16Lecture 16 | Dilated & Transposed Convolutions
- 17Lecture 17 | Kullback–Leibler divergence, Autoencoders
- 18Lecture 18 | Variational Autoencoders, Generative Adversarial Networks
- 19Lecture 19 | Generative Adversarial Networks
- 20Lecture 20 | Wasserstein Generative Adversarial Networks
- 21Lecture 21 | Bayesian & Siamese Neural Networks
- 22Lecture 22 | word2vec
- 23Lecture 23 | Tensorflow (part 2)