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Neural network and deep learning Chapter 1: introduction reading questions
2022-07-05 04:25:00 【Sleeping Raki】
1. Is neural network equivalent to deep learning ?
Not equivalent , Deep learning can use neural network model , Other models can also be used ( Such as deep belief network )
2. What is the main difference between shallow learning and deep learning ?
An important feature of shallow learning is that it does not involve feature learning , Its features are mainly extracted by manual experience or feature transformation methods , The deep learning model can automatically extract features
3. What is learning ? What is a good expression ? What are the representations ?
In order to improve the accuracy of machine learning system , We need to transform the input information into effective features ( such as word embedding), Or more generally, it's called expressing (Representation). If there is an algorithm that can automatically learn effective features , And improve the performance of the final machine learning model , Then this kind of learning can be called representation learning
The key to representation learning is to solve the semantic gap (Semantic Gap) problem . Semantic gap refers to the inconsistency and difference between the underlying characteristics of input data and high-level semantic information . for instance , Computers cannot understand texts directly , The representation of text is only a low-level code in the computer ( Such as ASCII code ), So we need some conversion methods to make the computer “ understand ” semantics .
(1) A good presentation should have strong presentation ability , That is, vectors of the same size can represent more information .
(2) A good presentation should make the follow-up learning task simple , That is, it needs to contain higher-level semantic information .
(3) A good representation should be general , It's task or domain independent . Although the current large
Part of it means that the learning method is still based on a certain task , But we expect that the representations they learned can be easily migrated to other tasks . In machine learning , We often use two ways to express features : Part means (Local Representation) And distributed presentation (Distributed Representation).
Part means : Such as one-hot code
Distributed representation : Such as RGB Show color ,word embedding
4. What is deep learning ?
“ depth ” It refers to the number of nonlinear transformations of the original data ( The layer number ), Deep learning is a sub problem of machine learning , Its main purpose is to learn effective feature representation from data automatically , The key problem to be solved in deep learning is : Contribution distribution problem , That is, the contribution or influence of different components or their parameters in a system to the final system output
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