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  in TensorFlow, you can not only build neural networks through the bottom implementation of custom weights, but also directly call the high-level implementation of existing convolution layer classes to quickly build complex networks. We mainly take 2D convolution as an example to introduce how to realize convolution neural network lay ...

The network depth of VGG series is improved compared with its previous networks. VGG16 and VGG19 are the representatives of VGG series. This time, VGG16 network is realized based on tensorflow 2.0. 1. Introduction to vgg16 network VGG16 network model stood out in the 2014 ImageNet competition, ranking second in classification tasks and first ...

preface TensorRT is an efficient deep learning model reasoning framework launched by NVIDIA. It includes deep learning reasoning optimizer and runtime, which can make deep learning reasoning applications have the advantages of low latency and high throughput. In essence, it is to accelerate the reasoning speed of the whole network by fusing s ...

1, Introduction Alex net was designed by Hinton, the winner of the 2012 ImageNet competition, and his student Alex Krizhevsky. Also after that year, more and deeper neural networks were proposed, such as the excellent VGg and Google lenet. The accuracy of the official data model is 57.1% and the top 1-5 is 80.2%. This is quite excellent for th ...

Starting from 0, data analysis and machine learning in Xueda University are simple and simple to write down the contest experience. This paper uses a variety of machine learning regression algorithms, and also uses deep learning pytorch to build a neural network for regression calculation. 1. Background introduction This is an industrial stea ...

Part of the content comes from Keras introductory course 6 of blogger Stanley compound field - migration learning using concept V3 model Address: https://blog.csdn.net/tsyccnh/article/details/78889838 There are two main types of transfer learning The first is the so-called transfer learning. When migrating training, remove the top layer. Fo ...

1. Overview Using YOLOv5 training dataset, there are three steps: data preparation and processing before training, training your own dataset, detection, and some optimization problems after training. 2. Preparations before training First from the official website of YOLOv5: https://github.com/ultralytics/yolov5 Download the corresponding pro ...

Fat introduction Fat (Federated AI Technology Enabler) is an open source project initiated by the AI Department of Weizhong bank, which aims to provide a secure computing framework to support the federal AI ecosystem. It implements a secure computing protocol based on homomorphic encryption and multiparty computing (MPC). It supports the secur ...

abstract In order to understand what happened, we printed out some statistics during model training to see whether the training was in progress. However, we can do better: PyTorch is integrated with TensorBoard, which is a tool for visualizing the results of neural network training. This tutorial explains some of its functions using the fa ...

1, Introduction to firefly optimization algorithm (FA) 1 Introduction There are many kinds of fireflies, mainly distributed in the tropics. Most fireflies produce rhythmic flashes in a short time. This flash is due to a chemical reaction of bioluminescence, and the flash pattern of fireflies varies from species to species. firefly algorithm (F ...