|
Encoder-Decoder Seq2Seq for Machine Translation
|
|
0
|
607
|
August 14, 2023
|
|
The Encoder-Decoder Architecture
|
|
0
|
916
|
August 14, 2023
|
|
Machine Translation and the Dataset
|
|
0
|
566
|
August 14, 2023
|
|
Bidirectional Recurrent Neural Networks
|
|
0
|
615
|
August 14, 2023
|
|
Deep Recurrent Neural Networks
|
|
0
|
883
|
August 14, 2023
|
|
Gated Recurrent Units (GRU)
|
|
0
|
733
|
August 14, 2023
|
|
Long Short-Term Memory (LSTM)
|
|
0
|
930
|
August 14, 2023
|
|
Concise Implementation of Recurrent Neural Networks
|
|
0
|
569
|
August 14, 2023
|
|
Recurrent Neural Network Implementation from Scratch
|
|
0
|
1003
|
August 14, 2023
|
|
Recurrent Neural Networks
|
|
0
|
386
|
August 14, 2023
|
|
Language Models
|
|
0
|
604
|
August 14, 2023
|
|
Converting Raw Text into Sequence Data
|
|
0
|
621
|
August 14, 2023
|
|
Working with Sequences
|
|
0
|
1017
|
August 14, 2023
|
|
Designing Convolution Network Architectures
|
|
0
|
576
|
August 14, 2023
|
|
Densely Connected Networks (DenseNet)
|
|
0
|
565
|
August 14, 2023
|
|
Residual Networks (ResNet) and ResNeXt
|
|
0
|
935
|
August 14, 2023
|
|
Batch Normalization
|
|
0
|
658
|
August 14, 2023
|
|
Multi-Branch Networks (GoogLeNet)
|
|
0
|
833
|
August 14, 2023
|
|
Network in Network (NiN)
|
|
0
|
604
|
August 14, 2023
|
|
Networks Using Blocks (VGG)
|
|
0
|
897
|
August 14, 2023
|
|
Deep Convolutional Neural Networks (AlexNet)
|
|
0
|
545
|
August 14, 2023
|
|
Convolutional Neural Networks (LeNet)
|
|
0
|
1080
|
August 14, 2023
|
|
Pooling
|
|
0
|
582
|
August 14, 2023
|
|
Multiple Input and Multiple Output Channels
|
|
0
|
998
|
August 14, 2023
|
|
Padding and Stride
|
|
0
|
621
|
August 14, 2023
|
|
Convolutions for Images
|
|
0
|
867
|
August 14, 2023
|
|
GPUs
|
|
0
|
845
|
August 14, 2023
|
|
File I/O
|
|
0
|
556
|
August 14, 2023
|
|
Custom Layers
|
|
0
|
530
|
August 14, 2023
|
|
Lazy Initialization
|
|
0
|
904
|
August 14, 2023
|