|
About the jax category
|
|
0
|
518
|
August 11, 2023
|
|
The Base Classification Model
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|
1
|
1443
|
August 6, 2024
|
|
Installation
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|
1
|
1344
|
March 21, 2024
|
|
Transformers for Vision
|
|
0
|
1444
|
August 14, 2023
|
|
The Transformer Architecture
|
|
0
|
1269
|
August 14, 2023
|
|
Self-Attention and Positional Encoding
|
|
0
|
1334
|
August 14, 2023
|
|
Multi-Head Attention
|
|
0
|
1447
|
August 14, 2023
|
|
The Bahdanau Attention Mechanism
|
|
0
|
1268
|
August 14, 2023
|
|
Attention Scoring Functions
|
|
0
|
863
|
August 14, 2023
|
|
Attention Pooling by Similarity
|
|
0
|
1057
|
August 14, 2023
|
|
Queries, Keys, and Values
|
|
0
|
1491
|
August 14, 2023
|
|
Encoder-Decoder Seq2Seq for Machine Translation
|
|
0
|
935
|
August 14, 2023
|
|
The Encoder-Decoder Architecture
|
|
0
|
1374
|
August 14, 2023
|
|
Machine Translation and the Dataset
|
|
0
|
867
|
August 14, 2023
|
|
Bidirectional Recurrent Neural Networks
|
|
0
|
952
|
August 14, 2023
|
|
Deep Recurrent Neural Networks
|
|
0
|
1305
|
August 14, 2023
|
|
Gated Recurrent Units (GRU)
|
|
0
|
1050
|
August 14, 2023
|
|
Long Short-Term Memory (LSTM)
|
|
0
|
1363
|
August 14, 2023
|
|
Concise Implementation of Recurrent Neural Networks
|
|
0
|
840
|
August 14, 2023
|
|
Recurrent Neural Network Implementation from Scratch
|
|
0
|
1471
|
August 14, 2023
|
|
Recurrent Neural Networks
|
|
0
|
559
|
August 14, 2023
|
|
Language Models
|
|
0
|
951
|
August 14, 2023
|
|
Converting Raw Text into Sequence Data
|
|
0
|
957
|
August 14, 2023
|
|
Working with Sequences
|
|
0
|
1465
|
August 14, 2023
|
|
Designing Convolution Network Architectures
|
|
0
|
861
|
August 14, 2023
|
|
Densely Connected Networks (DenseNet)
|
|
0
|
862
|
August 14, 2023
|
|
Residual Networks (ResNet) and ResNeXt
|
|
0
|
1367
|
August 14, 2023
|
|
Batch Normalization
|
|
0
|
1040
|
August 14, 2023
|
|
Multi-Branch Networks (GoogLeNet)
|
|
0
|
1273
|
August 14, 2023
|
|
Network in Network (NiN)
|
|
0
|
978
|
August 14, 2023
|