Detailed SVD and common Embedding applications

The reason for writing this article is that after embedding with SVD and deepwall in a recommended task, the effect of the model has been improved, and the application of SVD is beyond the knowledge of dimension reduction and there is a lot to think about, so some methods of SVD and embedding are summarized. 1. Singular Value Decomposition SVD ...

Posted by EsOne on Sat, 18 Sep 2021 04:18:58 -0700

Natural language processing - use of Jieba word splitter

1. jieba Chinese word segmentation import jieba text = "In most cases, vocabulary is the basis of our understanding of sentences and articles, so we need a tool to decompose the complete text into finer grained words." cut_result = jieba.cut(text, cut_all=True) # Full mode print(cut_result) print("\n Full mode : " + "/ ".join(cut_result)) ...

Posted by bgomillion on Sat, 18 Sep 2021 04:13:59 -0700

7, Binary tree: the maximum depth of a binary tree

After reading this article, you can do the following two questions together: 104. Maximum depth of binary tree559.n maximum depth of fork tree Force button topic link (opens new window) Given a binary tree, find its maximum depth. The depth of the binary tree is the number of nodes on the longest path from the root node to the farthest ...

Posted by lathifmca on Fri, 17 Sep 2021 21:36:26 -0700

PaddleOCR -- using streamlit and docker to build a fast serving

Overall reference: Paddleocr 2.3 - Documentation tutorial You must see the first three steps and ask for them on demand one 🎨 environment If the cuda version of this machine is not very satisfied with the image requirements of paddlepaddle, you can consider creating a cpu version. It wouldn't be that complicated. For example: docker pull pa ...

Posted by shorty114 on Fri, 17 Sep 2021 20:29:27 -0700

Installation of Anaconda(3-2021.05)+Tensorflow(2.6) in Win10 environment

Installation of Anaconda(3-2021.05)+Tensorflow(2.6) in Win10 environment In the process of learning machine learning, many Python libraries will be used, such as tensorflow and pandas. It is very inconvenient to install them alone. Therefore, in most cases, people will install Anaconda first. 1. Install Anaconda3 version You can find the lat ...

Posted by leegreaves on Fri, 17 Sep 2021 17:14:15 -0700

Datawhale September Group Learning--Emotional Analysis--Task01

Tip 1: Learning Address Point Here Tip 1: Word embeddings: how to transform text into numbers Preface _Task01 mainly uses RNN framework (note: this paper does not give a detailed explanation of RNN principles), IMDB dataset to build a Baseline model of text affective analysis tasks. 1. Model building process 1.1 Data Preprocessing ...

Posted by semtex on Wed, 15 Sep 2021 09:34:10 -0700

[pytorch] freeze part of the network

Preface The best, most efficient and most concise is Plan One. Scheme One Step 1: Fixed basic network Code template: # Get the state_dict for the fixed part: pre_state_dict = torch.load(model_path, map_location=torch.device('cpu') # Imported (remember strict=False): model.load_state_dict(pre_state_dict, strict=False) print('Load mode ...

Posted by tauchai83 on Tue, 14 Sep 2021 09:46:30 -0700

Pytoch learning notes -- transforms

Why transforms? Generally, the collected image samples are different in size and brightness. In deep learning, we want the sample distribution to be independent and identically distributed, so we need to normalize the samples.Sometimes only a small amount of sample data can be obtained, and it is not easy to obtain a large number of samples. H ...

Posted by The Chancer on Sun, 12 Sep 2021 00:49:40 -0700

Recognition of fashion MNIST data set by convolutional neural network (DenseNet) (pytoch version)

1. Preface 1.1 case introduction In this case, pytoch is used to build a DenseNet network structure for image classification of fashion MNIST dataset. The analysis of this problem can be divided into data preparation, model establishment, training with training set and testing the effect of model with test set. 1.2 environment configurat ...

Posted by OhLordy on Fri, 10 Sep 2021 01:45:07 -0700

Image classification for deep learning -- a detailed explanation of Vision Transformer(ViT) network

Deep learning image classification (XVIII) detailed explanation of Vision Transformer(ViT) network In the previous section, we talked about the self attention structure in Transformer. In this section, learn the detailed explanation of Vision Transformer(vit). Learning video from Bilibili , refer to blog Detailed explanation of Vision Tran ...

Posted by seaweed on Thu, 09 Sep 2021 21:09:01 -0700