Все публикации

[MXDL-10-07] Recurrent Neural0:14:22

[MXDL-10-07] Recurrent Neural Networks (RNN) [7/8] - Gated Recurrent Unit (GRU)

[MXDL-10-06] Recurrent Neural0:21:15

[MXDL-10-06] Recurrent Neural Networks (RNN) [6/8]- Peephole LSTM models for time series forecasting

[MXDL-10-04] Recurrent Neural0:19:52

[MXDL-10-04] Recurrent Neural Networks (RNN) [4/8] - Long Short-Term Memory (LSTM)

[MXDL-10-03] Recurrent Neural0:16:20

[MXDL-10-03] Recurrent Neural Networks (RNN) [3/8] - Build RNN models for time series forecasting

[MXDL-10-02] Recurrent Neural0:16:28

[MXDL-10-02] Recurrent Neural Networks (RNN) [2/8] - Backpropagation Through Time (BPTT)

[MXDL-10-01] Recurrent Neural0:17:30

[MXDL-10-01] Recurrent Neural Networks (RNN) [1/8] - Basics of RNNs and their data structures.

[MXDL-9-01] Highway Networks0:14:04

[MXDL-9-01] Highway Networks [1/1] - Shortcut connections, implementing highway networks using Keras

[MXDL-8-03] Weights Intialization0:21:10

[MXDL-8-03] Weights Intialization [3/3] - Kaiming He Initializer

[MXDL-8-02] Weights Initialization0:18:44

[MXDL-8-02] Weights Initialization [2/3] - Xavier Glorot Initializer

[MXDL-8-01] Weights Initialization0:17:26

[MXDL-8-01] Weights Initialization [1/3] - Observation of the outputs of a hidden layer

[MXDL-7-02] Batch Normalization0:16:54

[MXDL-7-02] Batch Normalization [2/2] - Custom Batch Normalization layer using Keras

[MXDL-7-01] Batch Normalization0:15:24

[MXDL-7-01] Batch Normalization [1/2] - Training and Prediction stage

[MXDL-6-02] Dropout [2/2]0:18:27

[MXDL-6-02] Dropout [2/2] - Scale-down and Scale-up

[MXDL-6-01] Dropout [1/2]0:13:29

[MXDL-6-01] Dropout [1/2] - Zero-out step in dropout

[MXDL-5-02] Regularization [2/2]0:15:50

[MXDL-5-02] Regularization [2/2] - Activity (or Activation) Regularization

[MXDL-5-01] Regularization [1/2]0:17:47

[MXDL-5-01] Regularization [1/2] - Weights and Biases Regularization

[MXDL-4-02] TensorFlow &0:17:48

[MXDL-4-02] TensorFlow & Keras [2/2] - Build neural networks with Keras

[MXDL-4-01] TensorFlow &0:15:36

[MXDL-4-01] TensorFlow & Keras [1/2] - Build neural networks with TensorFlow

[MXDL-3-03] Backpropagation [3/3]0:16:54

[MXDL-3-03] Backpropagation [3/3] - Automatic Differentiation

[MXDL-3-02] Backpropagation [2/3]0:17:27

[MXDL-3-02] Backpropagation [2/3] - Error Backpropagation along multiple paths

[MXDL-3-01] Backpropagation [1/3]0:15:18

[MXDL-3-01] Backpropagation [1/3] - Error Backpropagation along single path

[MXDL-2-03] Optimizers [3/3]0:20:32

[MXDL-2-03] Optimizers [3/3] - Adadelta and Adam optimizers

[MXDL-2-02] Optimizers [2/3]0:17:05

[MXDL-2-02] Optimizers [2/3] - NAG, Adagrad, and RMSprop optimizers

[MXDL-2-01] Optimizers [1/3]0:17:45

[MXDL-2-01] Optimizers [1/3] - Gradient descent and Momentum optimizer