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Knowledge Center
Home
Getting started with the platform
Tutorials
Deploy an operational AI model
Predict California house prices
Classifying images of clothes
BERT movie review sentiment analysis
Predicting mood from raw audio data
Gene expression prediction
Classifying car damage
Skin lesion segmentation
Participating in a Kaggle competition with zero code
Denoising images
Classifying fruit
Documentation
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
Inspecting datasets
Collaborating with your team
Import files and data sources to the Platform
Requirements on imported datasets
Data formats supported by the Peltarion Platform
Csv file specifications
Npy file specifications
Png file specifications
Zip file specifications
Text file specifications
How to import a file or a data source
Combining data types into one dataset
Edit an imported dataset for use in experiments
Edit dataset workflow
Dataset features
Feature set
Feature encoding
Standardization
Min-max normalization
None
Categorical
Text (beta)
Subset of a dataset
Random % filter
Conditional filter
Combination subset with random % and conditional filters
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
Fashion MNIST dataset
MNIST dataset
Tagger dataset
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
Build an AI model
Create an 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
BERT - pretrained
Blocks
Input
Target
Dense
Activation
Scaling
Dropout
Batch normalization
2D Convolution block
2D Deconvolution block
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
Reshape
Embedding
Text embedding
BERT Encoder
Image augmentation
Image augmentation settings
How to augment images in a dataset
Activations
ReLU
Sigmoid
Softmax
Tanh
Hard sigmoid
Linear
Loss functions
Binary crossentropy
Categorical crossentropy
Mean absolute error
Mean squared logarithmic error
Mean squared error
Poisson
Squared hinge
Run a model
Before running a model
The Run settings section
The Run button
Optimizers and compiler options
Ball rolling in an artificial landscape
Gradient descent
Batch size
Learning rate
Momentum
What optimizer to bring to a deserted island
Optimizer test with Rosenbrock function
How optimizers behave at a saddle point
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
Classification loss metrics
Loss curve
Accuracy
AUC — Area under curve
Gradient norm
Precision
Recall
Averaging of metrics for multi-class classification models
Regression loss metrics
MAE — mean absolute error
MAPE — mean absolute percentage error
MSE — mean squared error
Model evaluation
Confusion matrix
Error distribution curve
Prediction scatterplot
Navigating the loss curve and metrics diagram
Checkpoints — best and last epoch
X-axis labels
Y-axis labels
Keyboard shortcuts
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
Deployment view
Enable deployment for requests
Batching
Token validity
Sidekick
Using the API with Curl
Test curl method with a deployed MNIST model
Deployment parameters
Access authentication
Basic access authentication
Bearer access authentication
Using the API in Python
Image classification
Tabular data
Images and tabular data
Image to image
Numpy to numpy
Sidekick
Test the deployment from a Python script
Python script example with deployed mnist model
Access authentication
Basic access authentication
Bearer access authentication
Using the API via Sidekick
Sidekick the name
Deploy to API limitations
Unsupported deployment scenarios
Request size
Latency
Input data
Download API spec
Interpreting the API output
Regression problem
Classification problem
Responses to batch requests
Error responses from the API
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
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
Problem formulation
Data preparation
Modeling
Image segmentation / mark a single object type within an image / cheat sheet
Problem formulation
Data preparation
Modeling
Evaluation
Snippet selector for image projects
Glossary
Product updates
September 12, 2019 - Updates
July 12, 2019 - Updates
June 27, 2019 - Updates
May 16, 2019 - Updates
April 18, 2019 - Updates
March 20, 2019 - Updates
March 11, 2019 - Updates
February 11, 2019 - Updates
January 29, 2019 - Updates
January 15, 2019 - Updates
November 27, 2018 - Updates
November 15, 2018 - Updates
October 30, 2018 - Updates
September 18, 2018 - Updates
August 31, 2018- Updates
August 09, 2018 - Updates
July 26, 2018 - Updates
Terms
Terms of use
Privacy notice
Dataset licenses
Pretrained licenses
Dataset licenses
Calihouse | Peltarion Platform
Cifar-10
Fashion-MNIST
MNIST
Tagger
Deep learning yeast UTRs
Skin lesion segmentation
Car damage
Pretrained licenses
Snippets with weights licenses
Word vectors library licenses
VGG
References
License
ResNet
References
License
fastText
English word vectors
BERT
Bidirectional Encoder Representations from Transformers
References
License
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
/
Pretrained licenses
Pretrained licenses
Snippets with weights licenses
Licenses for the following snippets with weights can be found here:
VGG
ResNet
BERT
Word vectors library licenses
fastText