[Self-study on the principles of artificial intelligence] Convolutional neural network: breaking the bottleneck of image recognition

😊Hi, I'm Xiaohang, a literary and artistic young man who is getting bald and getting stronger. 🔔This article explains the convolutional neural network: breaking the bottleneck of image recognition, roll it up together! 1. Handwriting recognition In the field of machine learning and neural networks, there is a classic "Hel ...

Posted by alchemist_fr on Mon, 23 Jan 2023 00:42:36 +0300

NNDL experiment 6 Convolutional neural network ResNet18 implements MNIST

5.4 Handwritten Digit Recognition Experiment Based on Residual Network Residual Network (ResNet) is a way of adding directly connected edges to nonlinear layers in neural network models to alleviate the problem of gradient disappearance, thereby making it easier to train deep neural networks. In the residual network, the most basic unit is th ...

Posted by agsparta on Wed, 09 Nov 2022 21:02:41 +0300

Pytoch implementation of super-resolution network SRCNN

Overall framework SR, i.e. super resolution, i.e. super resolution. CNN is relatively famous as convolutional neural network. As can be seen from the name, SRCNN is the first convolutional neural network applied in the field of super-resolution. In fact, it is true. Super resolution refers to the process of enlarging a low resolution (LR) ...

Posted by thefollower on Thu, 05 May 2022 05:02:16 +0300

Li Mu hands-on learning deep learning V2-NiN model and code implementation

1. NiN LeNet, AlexNet and VGG all share a common design pattern: extract spatial structure features through a series of convolutional layers and pooling layers; and then process the representation of features through fully connected layers. The improvement of LeNet by AlexNet and VGG mainly lies in how to expand and deepen these two modules. ...

Posted by phpshift on Tue, 03 May 2022 01:39:18 +0300

tensorflow practical mnist handwriting recognition based on cnn (shallow network construction)

The example of handwritten numeral recognition mnist is equivalent to helloworld in c. It can be used for reference for students who have just started cnn. I am also a novice Xiaobai. You are welcome to correct any mistakes. Tensorflow practical mnist handwritten numeral recognition We won't introduce more about mnist dataset here. Let's Baid ...

Posted by kawai84 on Sun, 24 Apr 2022 06:09:44 +0300

Summary of Application of INT8 Quantitative Perception Training for CNN Convolution Neural Network Based on PPQ

1. Introduction For CNN convolution neural network acceleration at the end of the field programmer, an appropriate quantification method can not only effectively increase the number of DSP operations in a unit cycle, but also reduce the demand for storage space, in-and Out-of-chip interactive bandwidth, logical resources, etc. For example, with ...

Posted by konigwolf on Fri, 22 Apr 2022 20:31:55 +0300

Application and explanation of convolutional neural network CNN based on Pytorch

Application and explanation of convolutional neural network CNN based on Pytorch 1, Convolutional neural network CNN definition Convolutional neural network (CNN, sometimes called ConvNet) is very attractive. In a short time, they have become a subversive technology, breaking all the most advanced algorithms in many fields, such as text, vide ...

Posted by dyip on Sat, 16 Apr 2022 16:54:55 +0300

Machine learning notes - use CNN and LSTM to generate text descriptions for images

1, Task description Seeing an image, your brain can easily distinguish what the image is about, but can the computer distinguish the content represented by the image? With the progress of deep learning technology, the availability of large data sets and computer capabilities, we can build models that can generate descriptions for images. The ...

Posted by kayess2004 on Mon, 04 Apr 2022 04:30:36 +0300

[hands on learning pytorch notes] 15 Batch normalized BatchNorm (BN)

BatchNorm(BN) Encountered a problem The loss function is at the end, and the later layer is trained faster Data entry is at the bottom The front layer trains slowly As soon as the front floor changes, everything has to change The last layer needs to be relearned many times Slow convergence Can we avoid changing the top layer wh ...

Posted by pramodv on Tue, 29 Mar 2022 18:26:24 +0300