Skip to content
machine-learning
paper-Convolutional-Sequence-to-Sequence-Learning
Initializing search
GitHub
machine-learning
GitHub
Home
AI-meeting
AI-papers
AI-papers
Introduction
Reading-record
Research-Institution&Researcher
Research-Institution&Researcher
Researcher
Research-institution
Theory
Theory
Introduction
Deep-learning
Deep-learning
Introduction
Book-deep-learning
Book-deep-learning
Introduction
Part-I-Applied-Math-and-Machine-Learning-Basics
Part-I-Applied-Math-and-Machine-Learning-Basics
1-Introduction
5-Machine-Learning-Basics
5-Machine-Learning-Basics
5-Machine-Learning-Basics
5.1-Learning-Algorithms
Task
Task
Pattern-recognition
Sequence-labeling
Sequence-labeling
Sequence-labeling
Sequence-Labeling-Generative-and-Discriminative
5.2-Capacity-Overfitting-and-Underfitting
5.3-Hyperparameters-and-Validation-Sets
5.3-Hyperparameters-and-Validation-Sets
Hyperparameter(machine-learning)
Cross-validation(statistics)
5.4-Estimators-Bias-and-Variance
5.4-Estimators-Bias-and-Variance
5.4-Estimators-Bias-and-Variance
Bias–variance tradeoff
5.5-Maximum-Likelihood-Estimation
5.5-Maximum-Likelihood-Estimation
5.5-Maximum-Likelihood-Estimation
Part-II-Deep-Networks-Modern-Practices
Part-II-Deep-Networks-Modern-Practices
Part-II-Deep-Networksb-Modern-Practice
6-Deep-Feedforward-Networks
6-Deep-Feedforward-Networks
6-Deep-Feedforward-Network
6.2-Gradient-Based-Learning
6.4-Architecture-Design
6.4-Architecture-Design
6.4-Architecture-Design
Universal-approximation-theorem
6.5-Back-Propagation-and-Other-Differentiation-algorithms
6.5-Back-Propagation-and-Other-Differentiation-algorithms
Introduction
Back-Propagation
Back-Propagation
Introduction
zhihu-Back-Propagation
wikipedia-Backpropagation
6.5-Back-Propagation-and-Other-Differentiation
Implementation
Backpropagation-through-time
7-Regularization-for-Deep-Learning
7-Regularization-for-Deep-Learning
7.8-Early-Stopping
8-Optimization-for-Training-Deep-Models
8-Optimization-for-Training-Deep-Models
8-Optimization-for-Training-Deep-Models
SGD
SGD
5.9-Stochastic-Gradient-Descent
Gradient-descent
Stochastic-gradient-descent
9-Convolutional-Networks
9-Convolutional-Networks
9-Convolutional-Networks
9.3-Pooling
CS231n
CS231n
Introduction
Convolutional-Neural-Networks(CNNs-or-ConvNets)
CNN-translation-invariance
CNN-pooling-layer
VGG
VGG
VGG
AlexNet
AlexNet
AlexNet
Fei-Fei-Li
ImageNet
paper-Convolutional-Sequence-to-Sequence-Learning
10-Sequence-Modeling-Recurrent-and-Recursive-Nets
10-Sequence-Modeling-Recurrent-and-Recursive-Nets
10-Sequence-Modeling-Recurrentand-Recursive-Nets
10.1-Unfolding-Computational-Graphs
10.3-Bidirectional-RNNs
10.4-Encoder-Decoder-Sequence-to-Sequence-Architectures
10.10-The-Long-Short-Term-Memory-and-Other-Gated
LSTM
LSTM
LSTM
colah-Understanding-LSTM-Networks
RNN-and-LSTM-tutorial
RNN
RNN
Introduction
12-Applications
12-Applications
12.4-Natural-Language-Processing
12.4-Natural-Language-Processing
12.4.5-Neural-Machine-Translation
Book-Neural-Networks-and-Deep-Learning
Book-Neural-Networks-and-Deep-Learning
Introduction
Guide
Guide
Tutorial
Tutorial
Artificial-neural-network
Neural-Networks-Tutorial
ujjwalkarn-A-Quick-Introduction-to-Neural-Networks
Computation-And-model-And-computational-graph
Computation-And-model-And-computational-graph
Introduction
Computational-graph
Model-capacity
Model-capacity
Model-capacity
Model-initialization
Model-initialization
Model-initialization
Batch-epoch-step
Batch-epoch-step
Buzz-word-batch-size
Batch-VS-epoch
Steps-VS-epochs
Word-epoch
Activation-function
Activation-function
Activation-function
ReLU-VS-sigmoid-VS-softmax
sigmoid-in-deep-learning
Design-neural-network
End-to-end
End-to-end
Introduction
End-to-end-reinforcement-learning
Transformer-and-attention
Transformer-and-attention
Papers
Feature-engineering
Feature-engineering
Data-transformation(statistics)
Feature-scaling
Normalization(statistics)
Machine-learning
Machine-learning
Introduction
Markov-model
Markov-model
Markov-chain
Markov-models
Hidden-Markov-model
Viterbi-algorithm
Forward-algorithm
CRF
CRF
CRF
Data-generating-process
VS-statistical-model-VS-machine-learning-model
VS-statistics-model-VS-stochastic-process
Programming
Programming
Programming-paradigm
Programming-paradigm
Introduction
Symbolic-and-imperative
TensorFlow
TensorFlow
Introduction
Doc
Implementation
Implementation
Introduction
TensorFlow-white-paper
TensorFlow-white-paper
Introduction
whitepaper2015
XLA
XLA
Introduction
Tensorflow-src-explanation
Tensorflow-src-explanation
Introduction
API
API
Introduction
Python
Python
Introduction
Core-graph-data-structures
Building-Graphs
Building-Graphs
Introduction
tf.control_dependencies
Low-level-API
Low-level-API
tf.placeholder-VS-tf.Variable
tf.placeholder-VS-tf.Variable
Introduction
How-to-understand-tf.Variable
TODO-C++
TODO-C++
Introduction
pytorch
pytorch
paper-Automatic-differentiation-in-PyTorch
Keras
Keras
intro-Keras
Getting-started-with-the-Keras-Sequential-model
keras-Layers-Convolutional-Layers
VS-pytorch-vs-tensorflow
Data-set
Data-set
Introduction
Application
Application
NLP
NLP
Introduction
NLP
WordNet
NLP-progress
NLP-progress
Task-of-NLP
Entity-Linking
Entity-Linking
Entity-Linking
Relationship-Extraction
Relationship-Extraction
Relationship-Extraction
Shallow-parsing
Shallow-parsing
Shallow-parsing
Text-Classification
Text-Classification
Text-Classification
paper-Bidirectional-LSTM-with-attention-mechanism-and-convolutional-layer
Part-of-speech-tagging
Part-of-speech-tagging
Introduction
Neural-machine-translation
Neural-machine-translation
Neural-machine-translation
Distant-supervision
Distant-supervision
Distant-supervision
Representation-of-word
Representation-of-word
Representation-of-image-VS--word
Linguistics
Linguistics
Introduction
Part-of-speech
Model
Model
BERT
BERT
BERT
BERT-paper
BERT-implementation
GPT
GPT
openai-GPT-2
Lib
Lib
BERT
BERT
BERT
BERT-paper
BERT-implementation
GPT
GPT
openai-GPT-2
Book-Natural-Language-Processing-with-Python
Book-Natural-Language-Processing-with-Python
Introduction
Computable-knowledge
Computable-knowledge
Introduction
Sequence
Sequence
Sequence
NRE
NRE
Introduction
Convolutional Sequence to Sequence Learning