|
About the jax category
|
|
0
|
238
|
August 11, 2023
|
|
The Base Classification Model
|
|
1
|
853
|
August 6, 2024
|
|
Installation
|
|
1
|
716
|
March 21, 2024
|
|
Transformers for Vision
|
|
0
|
874
|
August 14, 2023
|
|
The Transformer Architecture
|
|
0
|
731
|
August 14, 2023
|
|
Self-Attention and Positional Encoding
|
|
0
|
780
|
August 14, 2023
|
|
Multi-Head Attention
|
|
0
|
802
|
August 14, 2023
|
|
The Bahdanau Attention Mechanism
|
|
0
|
759
|
August 14, 2023
|
|
Attention Scoring Functions
|
|
0
|
456
|
August 14, 2023
|
|
Attention Pooling by Similarity
|
|
0
|
578
|
August 14, 2023
|
|
Queries, Keys, and Values
|
|
0
|
870
|
August 14, 2023
|
|
Encoder-Decoder Seq2Seq for Machine Translation
|
|
0
|
493
|
August 14, 2023
|
|
The Encoder-Decoder Architecture
|
|
0
|
797
|
August 14, 2023
|
|
Machine Translation and the Dataset
|
|
0
|
460
|
August 14, 2023
|
|
Bidirectional Recurrent Neural Networks
|
|
0
|
511
|
August 14, 2023
|
|
Deep Recurrent Neural Networks
|
|
0
|
769
|
August 14, 2023
|
|
Gated Recurrent Units (GRU)
|
|
0
|
623
|
August 14, 2023
|
|
Long Short-Term Memory (LSTM)
|
|
0
|
799
|
August 14, 2023
|
|
Concise Implementation of Recurrent Neural Networks
|
|
0
|
465
|
August 14, 2023
|
|
Recurrent Neural Network Implementation from Scratch
|
|
0
|
839
|
August 14, 2023
|
|
Recurrent Neural Networks
|
|
0
|
328
|
August 14, 2023
|
|
Language Models
|
|
0
|
468
|
August 14, 2023
|
|
Converting Raw Text into Sequence Data
|
|
0
|
449
|
August 14, 2023
|
|
Working with Sequences
|
|
0
|
886
|
August 14, 2023
|
|
Designing Convolution Network Architectures
|
|
0
|
458
|
August 14, 2023
|
|
Densely Connected Networks (DenseNet)
|
|
0
|
457
|
August 14, 2023
|
|
Residual Networks (ResNet) and ResNeXt
|
|
0
|
813
|
August 14, 2023
|
|
Batch Normalization
|
|
0
|
546
|
August 14, 2023
|
|
Multi-Branch Networks (GoogLeNet)
|
|
0
|
718
|
August 14, 2023
|
|
Network in Network (NiN)
|
|
0
|
477
|
August 14, 2023
|