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Part-I-Applied-Math-and-Machine-Learning-Basics
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1-Introduction
5-Machine-Learning-Basics
5-Machine-Learning-Basics
5-Machine-Learning-Basics
5.1-Learning-Algorithms
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Pattern-recognition
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Sequence-labeling
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5.4-Estimators-Bias-and-Variance
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Bias–variance tradeoff
5.5-Maximum-Likelihood-Estimation
5.5-Maximum-Likelihood-Estimation
5.5-Maximum-Likelihood-Estimation
Part-II-Deep-Networks-Modern-Practices
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Part-II-Deep-Networksb-Modern-Practice
6-Deep-Feedforward-Networks
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6.4-Architecture-Design
6.4-Architecture-Design
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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
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7-Regularization-for-Deep-Learning
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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
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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
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ujjwalkarn-A-Quick-Introduction-to-Neural-Networks
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sigmoid-in-deep-learning
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TensorFlow
TensorFlow
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TensorFlow-white-paper
TensorFlow-white-paper
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whitepaper2015
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tf.placeholder-VS-tf.Variable
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TODO-C++
TODO-C++
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pytorch
paper-Automatic-differentiation-in-PyTorch
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intro-Keras
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keras-Layers-Convolutional-Layers
VS-pytorch-vs-tensorflow
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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
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Distant-supervision
Distant-supervision
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BERT
BERT
BERT
BERT-paper
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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
Papers
基于transformer的大规模预训练语言模型： - GPT -BERT