|
Encoder-Decoder Seq2Seq for Machine Translation
|
|
0
|
466
|
August 14, 2023
|
|
The Encoder-Decoder Architecture
|
|
0
|
769
|
August 14, 2023
|
|
Machine Translation and the Dataset
|
|
0
|
432
|
August 14, 2023
|
|
Bidirectional Recurrent Neural Networks
|
|
0
|
468
|
August 14, 2023
|
|
Deep Recurrent Neural Networks
|
|
0
|
744
|
August 14, 2023
|
|
Gated Recurrent Units (GRU)
|
|
0
|
581
|
August 14, 2023
|
|
Long Short-Term Memory (LSTM)
|
|
0
|
763
|
August 14, 2023
|
|
Concise Implementation of Recurrent Neural Networks
|
|
0
|
433
|
August 14, 2023
|
|
Recurrent Neural Network Implementation from Scratch
|
|
0
|
789
|
August 14, 2023
|
|
Recurrent Neural Networks
|
|
0
|
311
|
August 14, 2023
|
|
Language Models
|
|
0
|
445
|
August 14, 2023
|
|
Converting Raw Text into Sequence Data
|
|
0
|
429
|
August 14, 2023
|
|
Working with Sequences
|
|
0
|
832
|
August 14, 2023
|
|
Designing Convolution Network Architectures
|
|
0
|
435
|
August 14, 2023
|
|
Densely Connected Networks (DenseNet)
|
|
0
|
430
|
August 14, 2023
|
|
Residual Networks (ResNet) and ResNeXt
|
|
0
|
780
|
August 14, 2023
|
|
Batch Normalization
|
|
0
|
519
|
August 14, 2023
|
|
Multi-Branch Networks (GoogLeNet)
|
|
0
|
693
|
August 14, 2023
|
|
Network in Network (NiN)
|
|
0
|
448
|
August 14, 2023
|
|
Networks Using Blocks (VGG)
|
|
0
|
722
|
August 14, 2023
|
|
Deep Convolutional Neural Networks (AlexNet)
|
|
0
|
426
|
August 14, 2023
|
|
Convolutional Neural Networks (LeNet)
|
|
0
|
897
|
August 14, 2023
|
|
Pooling
|
|
0
|
447
|
August 14, 2023
|
|
Multiple Input and Multiple Output Channels
|
|
0
|
785
|
August 14, 2023
|
|
Padding and Stride
|
|
0
|
469
|
August 14, 2023
|
|
Convolutions for Images
|
|
0
|
726
|
August 14, 2023
|
|
GPUs
|
|
0
|
707
|
August 14, 2023
|
|
File I/O
|
|
0
|
416
|
August 14, 2023
|
|
Custom Layers
|
|
0
|
399
|
August 14, 2023
|
|
Lazy Initialization
|
|
0
|
728
|
August 14, 2023
|