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CNN Parameter Tuner 1.0

Authors: Kottilapurath Surendran, Gokul; Mokros, Martin;

CNN Parameter Tuner 1.0

Abstract

Greetings and welcome to CNN Parameter Tuner 1.0.1! This innovative application is your gateway to the captivating world of Convolutional Neural Network Parameter Tuning. Dive into the realm of image analysis, powered by Convolutional Neural Networks (CNN), and fine-tune this algorithm with precision and ease, experimenting with Train-Test ratios, Epochs, Batch sizes, Activation functions, Optimizer functions, and Loss functions, etc. Note: The version 1.0.1 has been specifically developed to address big data challenges. This version incorporates a CNN model featuring four convolutional layers with 32, 64, 128, and 512 filters, respectively, as well as a dense layer with 128 units.

Related Organizations
Keywords

Model Saving & Deployment, Jupyter Notebook Generator, AI Model Development, Advanced CNN Techniques, Image Processing, Computer Vision, Reproducible Research, Training Neural Networks, Machine Learning, AI Model Tuning, CNN Model Training, AI Research Tools, Python Script Generator, AI Research and Development, Activation Functions, Activation Function Selection, Image Classification, hyperparameter tuning, Model Fine-tuning, Deep Neural Networks (DNN), Scientific Computing, Automated ML Workflow, AI Performance Tuning, AI Parameter Optimization, Confusion Matrix Visualization, Train-Test-Validation Split, AI Model Configuration, Training Dataset Optimization, Convolutional neural networks, Forestry Applications, Epoch Tuning, CNN, AI Development Tools, No-Code AI, Parameter Search, Neural Network Training, Neural Networks, AI Tool, Optimizer Tuning, Model Optimization, Convolutional Neural Network, Performance Tuning, Loss Function Tuning, Model Training, Deep Learning Tools, CNN Parameter Optimization, Deep Learning, Custom CNN, Easy ML Tool, Machine learning, Machine Learning Model, Optimizer Selection, Neural Network Tuning, Deep Learning Frameworks, Image Augmentation, Educational Tool, Image Analysis Tools, Python Machine Learning, Batch Size Tuning, cnn, Model Saving & Deployment, Parameter Search AI, Loss Functions, Deep learning, Image Processing Software, AI Model Training Tools, Classification Report, Train-Test Split, Convolutional Neural Networks (CNN), Interactive Data Visualization, Overfitting Prevention, No-Code Deep Learning, Data Preprocessing, CNN parameter tuner, CNN Hyperparameters, Dropout Regularization, Hyperparameter Tuning, Tree Species Identification

EOSC Subjects

Jupyter Notebook

  • BIP!
    Impact byBIP!
    selected citations
    These citations are derived from selected sources.
    This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
    0
    popularity
    This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
    Average
    influence
    This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
    Average
    impulse
    This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
    Average
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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
Average
Average
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