Tag：neural network
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SENet code replication + annotation (PyTorch)
The SENet module is often used for channel attention in convolutional networks to enhance the network model’s ability to select in channel weights and thus lift points. About the principle and specific details of SENet, we have already described in detail in the previous article:Ultra-detailed Explanation of Classical Neural Network Papers (VII) – SENet (Attention […]
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Building Neural Networks with PyTorch
PyTorch Deep Learning in Action (3) – Building Neural Networks with PyTorch 0. Preface1. PyTorch building neural network first experience1.1 Building Neural Networks with PyTorch1.2 Neural network data loading1.3 Model testing1.4 Getting the value of the middle layer 2. Using Sequential Classes to Build Neural Networks3. Saving and loading PyTorch models3.1 Components required for model […]
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Neural Networks and Model Training Process in Detail
PyTorch Deep Learning in Action (1) – Neural Network and Model Training Process in Detail 0. Preface1. Traditional machine learning and artificial intelligence2. Basics of artificial neural networks2.1 Artificial Neural Network Composition2.2 Training of Neural Networks 3. Forward dissemination3.1 Calculating hidden layer values3.2 Perform nonlinear activation3.3 Calculating the output layer values3.4 Calculation of the value […]
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Deep Learning – Convolutional Neural Network (CNN) Theory
Study period: 2022.04.10~2022.04.12 Article Catalog 3. Convolutional neural network CNN3.1 Concept of Convolutional Neural Networks3.1.1 What is a CNN?3.1.2 Why use CNNs?3.1.3 Principles of human vision 3.2 Fundamentals of CNN3.2.1 Main structures3.2.2 Convolution layer1. Convolutional operations2. Three modes of convolution3. The nature of convolution 3.2.3 Pooling layer3.2.4 Activation layer3.2.5 Rasterization3.2.6 Full connectivity layer3.2.7 Reverse propagation3.2.8 […]
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Python’s method for importing torch packages
Article Catalog preambleI. What is Pythorch?Second, how to download and import the torch package?Third, pip package manager download failure torch, the solution and the official website to download teaching1. Download failure, solution2.Official website download Fourth, why there will be the official website to download and local direct download, torch two ways?summarize preamble With the continuous […]
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The 100 Best Deep Learning Projects for Getting Started
attention (heed): Recently by the fans feedback, found that some subscribers will be the content of this column for secondary sale, hereby declare that the content of this column is for learning only, shall not be sold in any way, without the author’s permission shall not be the content of this column to exercise the […]
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YOLOv8 trains its own dataset
I. Preparing the Deep Learning Environment My laptop system is: Windows 10 The latest version of the YOLO series, YOLOv8, has been released, for details you can refer to theThe blog I wrote earlier, currently ultralytics has released thePartial code and descriptionTheDownload YOLOv8 code on github, the code folder will contain the requirements.txt file, which […]
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NLP Information Extraction Fully Explained: a Practical Guide to PyTorch from Named Entities to Events
catalogs introductoryImportance of context and information extractionObjectives and structure of the article Overview of information extractionWhat is Information ExtractionApplication Scenarios for Information ExtractionKey challenges in information extraction entity identificationWhat is entity identificationApplication Scenarios for Entity RecognitionPyTorch implementation codeInputs, outputs and processes Relational extractionWhat is Relationship ExtractionApplication Scenarios for Relational ExtractionPyTorch implementation codeInputs, outputs and processes […]
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Theory of Convolutional Neural Networks (CNNs)
Study period: 2022.04.10~2022.04.12 Article Catalog 3. Convolutional neural network CNN3.1 Concept of Convolutional Neural Networks3.1.1 What is a CNN?3.1.2 Why use CNNs?3.1.3 Principles of human vision 3.2 Fundamentals of CNN3.2.1 Main structures3.2.2 Convolution layer1. Convolutional operations2. Three modes of convolution3. The nature of convolution 3.2.3 Pooling layer3.2.4 Activation layer3.2.5 Rasterization3.2.6 Full connectivity layer3.2.7 Reverse propagation3.2.8 […]