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Knowledge Center
  • Home
  • Documentation
    • Get started with the platform
      • Video walkthrough
      • Structure of this guide
      • Before we begin...
      • Platform overview and general workflow
        • Projects view
        • Datasets view
        • Modelling view
        • Evaluation view
        • Deployment view
      • Build, train, and deploy models - detailed workflow
        • Step 1 - Create a Project
        • Step 2 - Upload your data
        • Step 3 - Configure your dataset
        • Step 4 - Build a model
        • Step 5 - Train a model
        • Step 6 - Evaluate your model
        • Step 7 - Deploy your model
      • Use your deployed models in your applications
        • Using the Deployment API
        • Using Sidekick
        • Bubble integration - No-code apps
      • Learn by doing
        • Next step
    • Tutorials
      • For beginners
      • For intermediate users
      • For expert users
      • Deploy an operational AI model
        • Create a project
        • Add the MNIST dataset to the platform
        • Design a deep learning network with the wizard
        • Run experiment
        • Analyze experiment
        • Deploy your trained experiment
        • Test the MNIST classifier
        • Tutorial recap and next steps
      • Predict real estate prices
        • Create a project
        • The data
        • Add the Calihouse dataset to the platform
        • Design a model for image data
        • Create experiment
        • Run experiment
        • Run concurrent experiment with a second input for tabular data
        • Analyze experiments
        • Deploy trained experiment
        • Tutorial recap
      • Self sorting wardrobe
        • The problem
        • Create project
        • Add and manage the dataset
        • Select a CNN model
        • Run experiment to train model
        • More experiments
        • Analyze experiments
        • Tutorial recap
        • Next steps / Read more
      • Movie review feelings
        • Create a project
        • What is BERT?
        • Add the data
        • BERT - Design a text classification model
        • Create experiment
        • Check experiment settings & run the experiment
        • Analyzing the first experiment
        • Deploy your trained experiment
        • Test the text classifier in a browser
        • Test the text classifier in a terminal
        • Tutorial recap and next steps
      • Classify text in any language
        • The data – books in many languages
        • Create a project
        • Import the data
        • Multilingual BERT - the text processing AI
        • Create the experiment
        • Run the experiment
        • Evaluate your model
        • Enable the deployment
        • Test the deployment
        • Recap
      • Build your own music critic
        • Create project - Figure out the mood of a song
        • Upload dataset
        • Preprocess the dataset on platform
        • Select DenseNet in the Experiment wizard
        • Start training from the modeling view
        • Run concurrent experiment
        • Analyze experiments
        • Last iteration with the whole dataset
        • Deploy your model
        • Tutorial recap - You have solved a real-world problem
        • Related content
      • Use AI to detect fraud
        • The problem
        • Credit card fraud data
        • Add class weights and run the model
        • Run experiment
        • Run several experiments
        • Evaluate
        • Next step on the platform
        • Further reading
      • Look deep into DNA
        • Motivation - Replicate results of a research paper
        • Biological background
        • Formulating the problem in terms of deep learning
        • Work with the data on the platform
        • Train the model
        • Conclusion
      • Skin cancer detection
        • The problem - Predict lesion segmentation boundaries
        • The data
        • Goals of the experiment
        • Create project
        • Add the dataset
        • Create a model for image segmentation
        • Train the model
        • Analyze experiment
        • Test the model
        • Tutorial recap
      • Car damage assessment
        • Create project
        • Add dataset to the project
        • Build a model in the Experiment wizard
        • Run experiment
        • Analyze the experiment
        • Deploy the trained model
        • Tutorial recap
        • Further reading
      • Verify images with Zapier and Peltarion
        • Create a spreadsheet with insurance claims
        • Create Zapier flow: Peltarion <--> Google Sheet
        • Recap
        • Next project
      • Kaggle competition with zero code
        • Preprocessed data
        • Create a new project
        • Add dataset to the platform
        • Create a deep learning experiment
        • Run experiment to train the model
        • Analyze experiment
        • Download model
        • Getting started with Kaggle account
        • Submitting predictions
        • Further work — Transfer learning
      • Writing style tutor
        • Test the web app first
        • You will learn to
        • The data – 100 most downloaded ebooks
        • Build a model on the platform
        • Load a BERT snippet
        • Create experiment
        • Run experiment
        • Enable the deployment
        • Create a web app – use the model
        • Test the classifier in a browser
        • Share your results
        • Recap
        • Next steps and linked ideas
      • Find similar images of fruits
        • Create a project
        • Add the Fruit 360 dataset to the platform
        • Build model with the Experiment wizard
        • Change to only 1 epoch
        • How does image similarity search work?
        • Deploy model
        • Test deployment
        • Tutorial recap
        • Further reading
        • Test image similarity deployment with Postman
      • Denoising images
        • The problem — Denoising images
        • The data
        • Create a project for denoising images
        • Add the grayscale MNIST dataset to the platform
        • Design a deep learning autoencoder
        • Run experiment
        • Analyze experiment
        • Test if your autoencoder can remove noise
        • Tutorial recap
        • Alternative solutions to image denoising
      • Shape up your Slack chaos
        • You will learn to
        • Start here
        • Create a Slack app that can collect your conversation history
        • The Data – Scrape your conversation history
        • Build, train and deploy a model on the platform
        • Set the Slack bot live in a #channel
        • Run your Slack bot with Docker
        • Let the bot into your Slack
        • Deploy your besserwisser bot in Google Cloud
      • Book genre classification
        • Preread
        • Embeddings
        • The problem - sci-fi or centaurs?
        • Goal with this experiment
        • Dataset - CMU book summary dataset
        • Create a project
        • Add the data
        • BERT - Design a text binary classification model
        • Create experiment
        • Check experiment settings & run the experiment
        • Analyzing the first experiment
        • Deploy your trained experiment
        • Test the text classifier in a browser
        • Tutorial recap and next steps
      • Audio analysis for industrial maintenance
        • The problem
        • You will learn to
        • The data
        • Create a project
        • Build the model
        • Run the experiment
        • Evaluating the experiment
        • Deploy your model
        • Recap
      • Create a no-code AI app
        • Use our deployed AI model or create your own
        • Create a new app with Bubble
        • Add the Peltarion plugin to Bubble
        • Design the app
        • Create the workflow
        • Extract data from the AI model
        • Use predictions in app
        • Test the app
        • Ideas for next steps
        • What you’ve learned
      • Understand the mood of your team with Slack data
        • Set up your data collection from Slack
        • Make a positivity prediction in Peltarion
        • Extract timestamp
        • Collect data in Google Sheets
        • Create dashboard in Google sheets
        • Final words
      • Detecting defects in mass produced parts
        • Create a project
        • Import the data
        • Preprocess the data
        • Build the model
        • Run the experiment
        • Evaluate the experiment
        • Deploy your model
      • Sales forecasting in Google Sheets
        • Create a project
        • The revenue data
        • Import the data
        • Build the model
        • Evaluate your model
        • Deploy your model
        • Integrate your model with Google Sheets
        • Recap
      • Buy or not / Predict from tabular data
        • The problem - Unleash the power of the spreadsheet
        • Getting started - create a project
        • The data
        • Build your model in the Experiment wizard
        • Evaluation view
        • Improve your model
        • Further reading
      • How to improve a model that uses tabular data
        • Run several experiments and test new ideas
        • Increase patience to train for more epochs
        • Change the model architecture
        • Change the learning rate
        • Set a learning rate schedule
        • Increase batch size
      • Classify customer complaints
        • Create a project
        • Download the data from CFPB
        • Upload and process the data
        • Create an experiment and build a model
        • Deploy the model
        • Next step
      • Use Peltarion connector in Microsoft Power Apps
        • Create your Power App
        • Add the Peltarion connector
        • Call the deployed Peltarion model
        • Run the app and get a prediction
        • What you've learned
    • Projects view
      • Create a new project
      • Navigate to a project
      • Recent experiments overview
        • Recent experiments menu
      • Search and filter for projects
        • Share your filters
    • Datasets view
      • Dataset overview
      • Dataset view for a specific dataset
      • Import files and data sources to the Platform
        • File import
        • Data library: ready-made datasets
        • Data warehouse: import datasets from Azure Synapse and BigQuery
        • Requirements of imported files
          • Zip file specifications
          • Csv file specifications
          • Images specifications
          • Npy file specifications
        • Data warehouse import: Azure, BigQuery
          • Azure Synapse import
          • BigQuery import
      • Edit an imported dataset for use in experiments
        • Edit and inspect datasets
        • Dataset features
          • Feature set
        • Feature encoding
          • Types of encoding
          • Categorical encoding
          • Binary encoding
          • Image encoding
          • Text encoding
          • Normalization
        • Subset of a dataset
          • How to create subsets
          • Filter data
          • Split subset
        • Feature distribution
          • Understanding the feature distribution
          • Subset statistics
      • Working with multiple dataset versions
        • Naming of dataset versions
        • How to check subset settings of a saved dataset version
        • How to edit a dataset version
        • Create a new version of a dataset
      • Search and filter for datasets
      • Datasets used in tutorials
        • Calihouse dataset
          • California housing
          • Open street map
          • Download complete dataset
          • Licence
        • Fashion MNIST dataset
          • Download here
          • Licence
        • MNIST dataset
          • Download here
          • Licence
        • Tagger dataset
          • Licence
        • The Large Movie Review Dataset
          • Content and format of the raw dataset
          • Data library dataset is preprocessed
          • Explore the dataset with Python
          • Terms of use
        • Bank marketing
          • Dataset origin
          • Attribute information
      • Example workflows - Datasets view
        • Create new dataset / Example workflow
          • Step 1: Data acquisition
          • Step 2: Data preprocessing
          • Step 3: Create csv file
          • Step 4: Create the zip-file
          • What’s next
        • Impact of standardization - create different versions of a dataset / Example workflow
          • Step 1: Create a new project
          • Step 2: Import data
          • Step 3: Rename a feature with a meaningful label
          • Step 4: Manage data encoding
          • Step 5: Create five versions of the dataset
          • Conclusion
    • Modeling view
      • Modeling canvas
      • Experiments overview
      • Build an AI model
        • Create ready-to-run experiment with the Experiment wizard
        • Create blank experiment
        • Add a block or a snippet to an experiment
        • Snippets - your gateway to deep neural network architectures
          • Choosing the right snippet
          • Going further
          • DenseNet snippet
          • Tiramisu snippet
          • U-net snippet
          • VGG snippet
          • Inception snippet
          • ResNetv2 snippet
          • CNN snippet
          • CNN + FC snippet
        • Pretrained snippets
          • Transfer learning with pretrained snippets
          • Available pretrained snippets
          • How to use pretrained snippets
          • VGG - pretrained
          • MobileNetV2 - pretrained
          • EfficientNet - pretrained
          • Multilingual BERT snippet
          • English BERT snippet
          • XLM-R Embedding snippet
          • Universal sentence encoder snippet
          • BERT - pretrained
        • Blocks
          • Input
          • Target
          • Output
          • Dense
          • Activation
          • Scaling
          • Dropout
          • Batch normalization
          • 2D Convolution block
          • 2D Deconvolution block
          • 2D Depthwise convolution
          • 2D Max pooling block
          • 2D Average pooling
          • 2D Global average pooling
          • 2D Global max pooling
          • 2D Upsampling
          • 2D Zero padding
          • 1D Average pooling
          • 1D Convolution block
          • 1D Global average pooling
          • 1D Global max pooling
          • 1D Max pooling block
          • 1D Upsampling
          • 1D Zero padding
          • Flatten
          • Concatenate
          • Add
          • Multiply
          • Reshape
          • Embedding
          • BERT Tokenizer
          • Multilingual BERT encoder
          • English BERT encoder
          • Universal sentence encoder
          • XLM-R Tokenizer
          • XLM-R Encoder
          • Text embedding
          • BERT Encoder
        • Image augmentation
          • Image augmentation settings
          • How to augment images in a dataset
        • Class weights
          • Why use class weights
          • How to use class weights
          • How does class weighting work
          • Test it on the platform
        • Activations
          • Linear
          • ReLU
          • Swish
          • Sigmoid
          • Softmax
          • Tanh
          • Hard sigmoid
        • Loss functions
          • Choosing a loss function
          • Compatibility with activation functions
          • Binary crossentropy
          • Categorical crossentropy
          • Mean absolute error
          • Mean squared logarithmic error (MSLE)
          • Mean squared error
          • Poisson
          • Squared hinge
      • Run a model
        • Before running a model
        • The Run settings section
        • Batch size
        • Epoch
        • Data access seed
        • Optimizers
        • The Run button
        • Early stopping
          • Why use early stopping
          • How to use early stopping
        • Optimization principles (in deep learning)
          • Optimization with labeled training
          • Model loss
          • Batch size
          • Learning rate
          • B and B2 rate
          • p
          • Momentum
          • Nesterov momentum
          • Resources
          • Learning rate schedule
        • The optimizers
          • Optimizer test with Rosenbrock function
          • How optimizers behave at a saddle point
          • Adadelta
          • Adagrad
          • Adam
          • Adamax
          • AMSgrad
          • Nadam
          • RMSprop
          • Stochastic gradient descent
        • Experiment states
          • Created state
          • Queued state
          • Running state
          • Paused state
          • Completed state
          • Failed state
          • Dequeued state
      • Improve your model
        • Change run settings
        • Change block parameters
      • Working with multiple experiments
        • How to duplicate experiment
        • Link to parent experiment
        • Copy blocks with weights to another model
          • Multiple inputs
          • Transfer learning
          • Trainable or non-trainable
          • Copy with weights parameters
          • How to copy blocks with weights
      • Download and deploy model with weights
        • h5 limitations
      • Search and filter for experiments
        • Share your filters
        • Experiment tagging
          • How to edit an experiment’s tags
          • How to use tags in the experiment search
      • Modeling canvas controls
        • Controls to navigate the Modeling canvas
        • Tools
        • macOS keyboard shortcuts
        • Windows/Linux keyboard shortcuts
      • Information pop-up
        • Troubleshooting
      • Example workflows - Modeling view
        • Modeling view - with and without standardization on image data / Example workflow
          • Step 1: Create experiments with dataset version [.userinput]#NoStdImage/TargetStd#
          • Step 2: Configure the dataset settings
          • Step 3: Configure the blocks settings in the CNN snippet
          • Step 4: Config the settings for running the model
          • Step 5: Click Run
          • Step 6: Duplicate the experiment
          • Step 7: Create experiments with dataset version [.userinput]#StdImage/TargetStd#
          • Conclusion
        • Modeling view - with and without standardization on tabular data / Example workflow
          • Step 1: Create experiments with dataset version [.userinput]#NoStdTabular/TargetStd#
          • Step 2: Configure the dataset settings
          • Step 3: Configure the blocks settings in the CNN snippet
          • Step 4: Config the settings for running the model
          • Step 5: Click Run
          • Step 6: Duplicate the experiment
          • Step 7: Create experiments with dataset version [.userinput]#StdTabular/TargetStd#
          • Conclusion
    • Evaluation view
      • Evaluation view elements
      • Loss and metrics
        • Navigating the loss and metrics plot
        • How to read the loss curve
      • Predictions inspection
        • Select the subset and checkpoint to inspect
        • Predictions table
        • Confusion matrix
          • Interact with the confusion matrix
          • How to improve classification results
          • Multi-dimensional target
        • Scatter plot
          • Interact with the scatter plot
          • How to improve regression results
          • Multi-dimensional target
        • ROC Curve
          • How to read the ROC curve
          • Interact with the ROC curve
          • How to decide on a threshold
      • Classification loss metrics
        • Loss curve
        • Binary accuracy
          • Suggestions on how to improve
        • Categorical accuracy
          • Suggestions on how to improve
        • Binary error
          • Suggestions on how to improve
        • Categorical error
          • Suggestions on how to improve
        • Precision
          • Definition
        • Macro-precision
          • Precision
          • Macro-averaging
        • Micro-precision
          • Precision
          • Micro-averaging
        • Recall
          • Definition
        • Macro-recall
          • Recall
          • Macro-averaging
        • Micro-recall
          • Recall
          • Micro-averaging
        • AUC / Area under curve
          • AUC explained
        • F1-score
          • Definition
        • Macro F1-score
          • Definition
          • Macro-averaging
        • Micro F1-score
          • Definition
          • Micro-averaging
      • Regression loss metrics
        • MAE / Mean absolute error
          • Definition
          • Why use MAE
          • When to use MAE
          • Example of use
        • MSE / Mean squared error
          • Definition
          • Why use MSE
          • When to use MSE
          • Example of use
        • RMSE / Root mean squared error
          • When to use RMSE
        • MAPE / Mean absolute percentage error
          • Example of use
        • R2 / R-squared
          • When to use R-squared
      • Measure performance when working with imbalanced data
        • Evaluate on macro-precision, -recall, and -F1
        • Evaluate with the confusion matrix
        • Don’t evaluate on loss or accuracy
        • Test it on the platform
      • Search and filter for experiments
        • Share your filters
        • Experiment tagging
          • How to edit an experiment’s tags
          • How to use tags in the experiment search
      • Example workflows - Evaluation view
        • Evaluation view - with and without standardization / Example workflow
          • Step 1: Document experiment results
          • Step 2: Compare results
          • Conclusion:
    • Deployment view
      • Enable deployment for requests
      • Parameters
      • API information
      • Using your deployment
      • Deploy to API limitations
        • Unsupported deployment scenarios
        • Request size
        • Latency
        • Input data
      • Search and filter deployed experiments
        • Share your filters
    • Organization settings view
      • Access Organization settings view
      • Members view
        • Add and remove members of the organization
        • Platform roles
        • Possible account membership status in the organization
        • How to remove a team member
        • How to change the role of a team member
        • How to invite a team member
        • How to resend a team member invitation
        • How to withdraw a new team member invitation
      • My profile view
        • Reset password
        • Leave organization
      • Quota view
        • Organization resources
        • How to manage quotas
        • End of organization quota plan
    • Cheat sheets
      • Problem types
      • Specific use cases
      • BERT - Text classification / cheat sheet
        • Use this cheat sheet
        • Data preparation
        • Modeling view
        • Evaluation view
        • Deployment view
      • Multi-label image classification / cheat sheet
        • Problem formulation
        • Data preparation
        • Modeling
      • Single-label image classification / cheat sheet
        • Use this cheat sheet
        • Data preparation
        • Modeling
        • Deployment view
      • Image segmentation / mark a single object type within an image / cheat sheet
        • Problem formulation
        • Data preparation
        • Modeling
        • Evaluation
      • No-code AI / cheat sheet
        • No-code connections
        • Start with an idea and collect data
        • Start with the AI model on the Pelation Platform
        • Use your trained AI model
        • Example: Mood dashboard
      • USE - Text similarity / cheat sheet
        • What does it mean?
        • How to use it?
      • Image similarity / cheat sheet
        • Problem formulation
        • Data preparation
        • Modeling
        • Evaluation
        • Deployment
        • Test the deployment
      • Snippet selector for image projects
    • Glossary
    • AI concepts
      • Available AI concepts
      • Image similarity
        • What can you do with image similarity?
        • Similarity implemented on the platform
        • Deep dive explanation of image similarity
        • Further reading
    • Peltarion APIs
      • What can you do with the Peltarion APIs?
      • How can you use the Peltarion APIs?
      • Data API
        • Enabling the Data API
        • Prerequisites
        • How to upload files
        • Examples
      • Deployment API
        • Prerequisites
        • Structure of a prediction request
        • Examples
        • Working with responses
        • Understanding prediction results
        • Test the deployment from a Python script
          • Python script example with deployed mnist model
          • More than one input
          • String and numerical values as input
      • AI for Sheets
        • How to install Peltarion AI for Sheets
        • How to use Peltarion AI for Sheets
      • Test with Postman
        • Available how-tos
        • Test image similarity deployment
          • Deploy an experiment
          • Configure Postman
          • Manage environment
          • Test your deployment with an image
          • Show results
      • Download API spec
    • Terms
      • Terms of use
      • Privacy notice
      • Dataset licenses
      • Pretrained licenses
      • Dataset licenses
        • 17k Mobile strategy games
          • License
        • Boat types
          • License
        • Book summaries
          • References
          • License
        • Calihouse
          • California housing
          • Open street map
          • How to credit OpenStreetMap
          • Download complete dataset
        • Car damage
          • License
          • Download dataset
        • Cifar-10
          • License
          • Download dataset
        • Deep learning yeast UTRs
          • References
          • License
        • DeepWeeds
          • References
          • License
        • Defects in metal casting
          • References
          • License
        • Fashion-MNIST
          • License
          • Download dataset
        • Fine food reviews
          • References
          • License
        • Flower photos
          • References
          • License
        • Freesound Audio Tagging
          • References
          • License
        • Fruits 360
          • References
          • License
        • German Traffic Sign Recognition Benchmark (GTSRB)
          • References
          • License
        • Grocery store
          • References
          • License
        • Imagenette
          • License
        • IMDB
          • References
          • License
        • Industrial machinery operating conditions
          • References
          • License
        • MNIST
          • License
          • Download dataset
        • News headlines
          • References
          • License
        • Most downloaded public domain books
          • License for mirror
          • Download dataset
        • Pneumonia detection
          • References
          • License
        • Oxford 102-category flowers
          • References
          • License
        • Oxford IIT Pet
          • References
          • License
        • PlantVillage
          • References
          • License
        • Pokemon images
          • License
        • Sign language for alphabets
          • License
        • Sign language MNIST
          • License
        • Skin lesion segmentation
          • References
          • License
          • Download dataset
        • Spoken verbs
          • License
        • Stack Overflow Tags
          • License
        • Stanford Online Products
          • References
          • License
        • Tagger
          • Download dataset
        • Tencent ML-Images
          • References
          • License
      • Pretrained licenses
        • Snippets with weights licenses
        • VGG
          • References
          • License
        • DenseNet
          • References
          • License
        • ResNet
          • References
          • License
        • MobileNetV2
          • References
          • License
        • EfficientNet
          • References
          • License
        • BERT
          • Bidirectional Encoder Representations from Transformers
          • References
          • License
        • XLM-R
          • XLM-RoBERTa
          • References
          • License
        • Universal sentence encoder
          • License
          • References
    • Troubleshooting
      • Error messages
        • Modeling view
        • Experiment
      • Known issues
    • FAQ
      • General questions
      • Datasets view
      • Modeling view
      • Deployment view
      • Subscription questions
      • Technical requirements
    • GitHub repositories
      • Sidekick
      • Community-code
  • Documentation /
  • Terms /
  • Dataset licenses

Dataset licenses

The Peltarion Platform provides datasets for use on the platform.

  • 17k Mobile strategy games

  • Book summaries

  • Calihouse

  • Car damage

  • Cifar-10

  • Deep learning yeast UTRs

  • DeepWeeds

  • Defects in metal casting

  • Fashion-MNIST

  • Flower photos

  • Freesound Audio Tagging

  • Fruits 360

  • German Traffic Sign Recognition Benchmark (GTSRB)

  • IMDB

  • Industrial machinery operating conditions

  • MNIST

  • Most downloaded public domain books

  • Oxford 102-category flowers

  • Oxford IIT Pet

  • PlantVillage

  • Pokemon images

  • Skin lesion segmentation

  • Stack Overflow Tags

  • Stanford Online Products

  • Tagger

TRY FOR FREE
  • Find us
    • Stockholm, Sweden
    • Contact: contact@peltarion.com
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    • Press releases
    • Press coverage
    • Press kit
  • Legal notice
    • Privacy policies
    • Terms
    • Copyright © 2021 Peltarion.
    • All rights reserved.