Programming VIP

0x0. preface The previous articles in this series have a superficial understanding of the components of MLIR. This article will not continue to talk about the architecture of MLIR. But from a practical point of view, let's take readers to see what MLIR has helped me do. Here we still take OneFlow Dialect as an example. stay Interpretation of ...

This chapter mainly takes SMS Spam Collection data set as an example to introduce the identification technology of harassing SMS. This section explains in detail the feature extraction method of harassing SMS with Word2Vec. Word2Vec model 1, Principle Word2Vec is an efficient tool that Google opened in 2013 to represent words as real value v ...

This paper introduces the multitasking learning algorithm published by Ali in 2019. The model shows the Bayesian network causality between targets, integrates and models the complex causality network between features and multiple targets, and eliminates the strong independent assumptions in the general MTL model. Since there is no specific assu ...

In the network of deep learning, I think the most basic is the residual network. What I share today is not the theoretical part of the residual network. Just remember that the idea of the residual network runs through many network structures behind. If you understand the residual network structure, then some advanced network structures behind a ...

1 sample processing 1.1 load sample code --- Titanic forecast Py (Part 1) import numpy as np import torch import torch.nn as nn import torch.nn.functional as F from scipy import stats import pandas as pd import matplotlib.pyplot as plt import os os.environ["KMP_DUPLICATE_LIB_OK"]="TRUE" def moving_average(a, w=10):#Define a function to calcu ...

Despite the recent success of long tail target detection, almost all long tail target detectors are developed based on the two-stage paradigm. In practice, one-stage detectors are more common in the industry because they have a simple and fast Pipeline and are easy to deploy. However, in the case of long tail, this work has not been explore ...

Get more training data (data enhancement) Data enhancement using geometric transformations Geometric transformations such as flip, crop, rotation and translation are some commonly used data enhancement techniques. GAN based data enhancement Reduce network capacity The simplest way to prevent overfitting is to reduce the size of the mod ...

Reprinted from AI Studio Project link https://aistudio.baidu.com/aistudio/projectdetail/3449604 XCiT: covariance image Quansi transformation network This is the fifth issue of the oar paper reproduction challenge< XCiT: Cross-Covariance Image Transformers >The champion code of GitHub is https://github.com/BrilliantYuKaimin/XCiT-PaddlePa ...

Firstly, the network model is introduced, and then the experimental results of SL1 and SP5 are introduced. 1 introduction of shallow self encoder used 1.1 shallow self encoder model (AE) The encoder and decoder adopt convolution with three-layer step size of 2, and the number of characteristic layers of the encoder changes to 3-64-128-25 ...

  you can run the program if you want Reference here . For ndarray and autograd, please refer to the previous blogs. preface    now there is a function, y=w*x+b, W, b is known, so give an X, you can find the corresponding y.    but when w and b are unknown, we only give a pair of X and Y. the obtained W and b may on ...