|
About the jax category
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0
|
560
|
August 11, 2023
|
|
The Base Classification Model
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1
|
1540
|
August 6, 2024
|
|
Installation
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|
1
|
1475
|
March 21, 2024
|
|
Transformers for Vision
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|
0
|
1547
|
August 14, 2023
|
|
The Transformer Architecture
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|
0
|
1377
|
August 14, 2023
|
|
Self-Attention and Positional Encoding
|
|
0
|
1436
|
August 14, 2023
|
|
Multi-Head Attention
|
|
0
|
1562
|
August 14, 2023
|
|
The Bahdanau Attention Mechanism
|
|
0
|
1371
|
August 14, 2023
|
|
Attention Scoring Functions
|
|
0
|
957
|
August 14, 2023
|
|
Attention Pooling by Similarity
|
|
0
|
1160
|
August 14, 2023
|
|
Queries, Keys, and Values
|
|
0
|
1587
|
August 14, 2023
|
|
Encoder-Decoder Seq2Seq for Machine Translation
|
|
0
|
1019
|
August 14, 2023
|
|
The Encoder-Decoder Architecture
|
|
0
|
1477
|
August 14, 2023
|
|
Machine Translation and the Dataset
|
|
0
|
956
|
August 14, 2023
|
|
Bidirectional Recurrent Neural Networks
|
|
0
|
1047
|
August 14, 2023
|
|
Deep Recurrent Neural Networks
|
|
0
|
1393
|
August 14, 2023
|
|
Gated Recurrent Units (GRU)
|
|
0
|
1147
|
August 14, 2023
|
|
Long Short-Term Memory (LSTM)
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|
0
|
1473
|
August 14, 2023
|
|
Concise Implementation of Recurrent Neural Networks
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|
0
|
921
|
August 14, 2023
|
|
Recurrent Neural Network Implementation from Scratch
|
|
0
|
1567
|
August 14, 2023
|
|
Recurrent Neural Networks
|
|
0
|
606
|
August 14, 2023
|
|
Language Models
|
|
0
|
1072
|
August 14, 2023
|
|
Converting Raw Text into Sequence Data
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|
0
|
1044
|
August 14, 2023
|
|
Working with Sequences
|
|
0
|
1566
|
August 14, 2023
|
|
Designing Convolution Network Architectures
|
|
0
|
943
|
August 14, 2023
|
|
Densely Connected Networks (DenseNet)
|
|
0
|
944
|
August 14, 2023
|
|
Residual Networks (ResNet) and ResNeXt
|
|
0
|
1467
|
August 14, 2023
|
|
Batch Normalization
|
|
0
|
1149
|
August 14, 2023
|
|
Multi-Branch Networks (GoogLeNet)
|
|
0
|
1369
|
August 14, 2023
|
|
Network in Network (NiN)
|
|
0
|
1078
|
August 14, 2023
|