Documentation - Basic Tutorials
The Basics - Level 1
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Creating Our First Model (Training, Prediction And Debugging)
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Saving And Loading Model Parameters (Training And Prediction Continuation)
The Basics - Level 2
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Using Solvers (Training Methods)
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Using Optimizers (Training Speed-Up)
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Using Regularizers (Dirty / Noisy Data Handling)
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Using Training Modifiers (Data Subset Training)
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Using Model Checker (Testing And Validation)
The Basics - Level 3
- Distributed Training (Server-To-Server, Client-To-Server And Client-To-Client Training)
Data Transformation
The Neural Networks
Learning AIs - Level 1
Learning AIs - Level 2
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Using Random Network Distillation (Simulating Curiosity)
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Using Eligibility Traces (Improving Action-Reward Association For Discrete Action Space)