PaddlePaddle provides a rich set of computing units to help you build a modular approach to the ever-changing learning model to solve different application problems. Here, we are for the common machine learning tasks, providing a different neural network model for everyone to learn and use.
PaddlePaddle provides a rich set of computational units to enable users to adopt a modular approach to solving various learning problems. In this repo, we demonstrate how to use PaddlePaddle to solve common machine learning tasks, providing several different neural network model that anyone can easily learn and use.
## 1. Word Embedding
## 1. Word vector
The word vector expresses words with a real vector. Each dimension of the vector represents some of the latent grammatical or semantic features of the text and is one of the most successful concepts in the field of natural language processing. The generalized word vector can also be applied to discrete features. The study of word vector is usually an unsupervised learning. Therefore, it is possible to take full advantage of massive unmarked data to capture the relationship between features and to solve the problem of sparse features, missing tag data, and data noise. However, in the common word vector learning method, the last layer of the model often encounters a large-scale classification problem, which is the bottleneck of computing performance.
The word embedding expresses words with a real vector. Each dimension of the vector represents some of the latent grammatical or semantic features of the text and is one of the most successful concepts in the field of natural language processing. The generalized word vector can also be applied to discrete features. The study of word vector is usually an unsupervised learning. Therefore, it is possible to take full advantage of massive unmarked data to capture the relationship between features and to solve the problem of sparse features, missing tag data, and data noise. However, in the common word vector learning method, the last layer of the model often encounters a large-scale classification problem, which is the bottleneck of computing performance.
In the example of word vectors, we show how to use Hierarchical-Sigmoid and Noise Contrastive Estimation (NCE) to accelerate word-vector learning.
