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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
CRF
Table of contents
Performing Sequence Labelling using CRF in Python
维基百科Conditional random field
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
Table of contents
Performing Sequence Labelling using CRF in Python
维基百科Conditional random field
CRF
Performing Sequence Labelling using CRF in Python
维基百科
Conditional random field