BiLSTM-Attention_CN_Text_Clf_Pytorch

KO | EN

gitlite — search

#typescript #ai-agents #ai #dsh-plugin #deepseek-harness #open-source #cli #claude-code #codex #developer-tools #react #windows

BiLSTM-Attention_CN_Text_Clf_Pytorch

★ 33 Open GitHub ↗

BiLSTM 加普通Attention中文文本多分类Pytorch实现

// repository documentation

Was this content helpful?

★ 0 (0 ratings)

####Bain’s Idea 2: Making Memories

###Connectionist Machines
>Neurons connect to other neurons.The processing/capacity of the brain is a function of these connections
All world knowledge is stored in the connections between the elements
- Neural networks are connectionist machines
- The machine has many non-linear processing units
- Connections may also define memory

###The Universal Model Multi-layer Perceptron
- Neural networks began as computational models of the brain
- Neural network models are connectionist machines 
~ The comprise networks of neural units
- Multi-layer perceptrons can model arbitrarily complex Boolean functions
- Multi-layer perceptrons are connectionist computational models
– Individual perceptrons are computational equivalent of neurons 
– The MLP is a layered composition of many perceptrons
- MLPs can model Boolean functions
– Individual perceptrons can act as Boolean gates 
– Networks of perceptrons are Boolean functions
- MLPs are Boolean machines
– They represent Boolean functions over linear boundaries 
– They can represent arbitrary decision boundaries 
– They can be used to classify data

- MLPs are classification engines
– They can identify classes in the data 
– Individual perceptrons are feature detectors 
– The network will fire if the combination of the detected basic features matches an “acceptable” pattern for a desired class of signal
- MLP can also model continuous valued functions
- MLP can Represent probability distributions
– Over integer, real and complex-valued
domains 
– MLPs can model both a posteriori and a priori distributions of data
• A posteriori conditioned on other variables 
– MLPs can generate data from complicated,or even unknown distributions
- The network is a function
- Interesting AI tasks are functions that can be
modelled by the network

BiLSTM-Attention_CN_Text_Clf_Pytorch

Explore Similar Repositories

Was this content helpful?

Recent Feedback

Download README