Deep learning series notes 03 multilayer perceptron

1 perceptron The output of the perceptron can be set to 0 or 1; Or - 1, 1. By continuously introducing data points, this line can effectively segment all kinds of training data. It is worth noting that the convergence theorem is a concept in mathematical statistics. Points cannot be taken on the plane to separate such two data (red ...

Posted by mitjakac on Tue, 21 Sep 2021 02:35:00 +0200

Review and summary of pytorch

Today, let's review the data reading mechanism of pytorch torch.utils.data.DataLoader(); Build an iterative data loader. Each for loop and each iteration obtain a batch from the dataloader_ Size data. Have you ever wondered how to load these classes, then read the data, and load them in batch? Today, let's study slowly and deeply to learn what ...

Posted by rmmo on Tue, 21 Sep 2021 00:16:43 +0200

Image convolution operation

image convolution 1 cross correlation operation Strictly speaking, convolution layer is a wrong name, because the operation it expresses is actually cross-correlation operation, not convolution operation. In the convolution layer, the input tensor and kernel tensor generate the output tensor through cross-correlation operation. First, let's ...

Posted by apulmca on Fri, 17 Sep 2021 22:05:11 +0200

Deep learning.22 practice of improving deep neural network

Three methods of initializing parameters First, download the data set and necessary files required for this practice. ( Download link )Put it in the project folder established below. Then open pycharm, create a new project called improved neural network, and then create a new python file called init.py. Three initialization methods are us ...

Posted by LanceT on Thu, 16 Sep 2021 19:23:55 +0200

September deep learning summary

1. Debug the program with breakpoints in pycharm to understand the logic of each line of code How to enable debug debugging: if name = = 'main': (referenced in the figure below) https://mp.weixin.qq.com/s/YNfoI-KbUg6jLaWUya-21g) The meaning of setting breakpoints: breakpoint debugging is actually that you mark a breakpoint at a certain place i ...

Posted by kelly3330 on Wed, 15 Sep 2021 23:30:50 +0200

Deep learning - from getting started to giving up CNN

Deep learning - from getting started to giving up (VI) introduction to CNN and RNN 1. Introduction of CNN and RNN CNN 1. It is mainly used in image processing. CNN is a feedforward neural network through convolution calculation. It is proposed by the receptive field mechanism in biology. It has translation invariance. It uses convolution kern ...

Posted by interactive on Sat, 11 Sep 2021 21:29:08 +0200

Simple digit recognition based on opencv

Because my knowledge is still shallow, I can't use python deep learning to identify the numbers here, so I completely use opencv to identify the numbers, and then share and record what I saw and thought in the learning process here Problems to be solved This is a number to be recognized. I first extract the ROI of the image, and only the numbe ...

Posted by txmedic03 on Wed, 08 Sep 2021 11:40:04 +0200

[Fruit detection] Fruit size detection based on morphology matlab source code contains GUI

1. Introduction Mathematical morphology operations can be divided into binary morphology and gray morphology, which is extended from binary morphology.Mathematical morphology has two basic operations, corrosion and expansion, which combine to form open and closed operations. Open operation means corrosion before expansion, close operation mean ...

Posted by mark103 on Tue, 07 Sep 2021 21:39:55 +0200

OpenCV4 machine learning: principle and implementation of K-means

preface: This column mainly combines OpenCV4 to realize some basic image processing operations, classical machine learning algorithms (such as K-Means, KNN, SVM, decision tree, Bayesian classifier, etc.), and common deep learning algorithms. Series of articles, continuously updated: OpenCV4 machine learning (I): Construction and configuratio ...

Posted by Christopher on Tue, 07 Sep 2021 06:41:32 +0200

Implementation and explanation of simple countermeasure neural network GAN - picture countermeasure

1. The theoretical explanation is clear and easy to understand: Understand the basic principle of "generating countermeasure network GAN" + 10 typical algorithms + 13 applications (easyai.tech) 2. Code implementation set: GitHub - eriklindernoren/Keras-GAN: Keras implementations of Generative Adversarial Networks. 3. Here is a bri ...

Posted by funkyres on Tue, 07 Sep 2021 06:34:45 +0200