Other Products
| Product | Description |
|---|---|
| DataPredict Neural | Object-Based Deep Learning Library (TensorFlow-Like API) |
| DataPredict Axon | Functional-Based Deep Learning Library (PyTorch-Like API) |
| DataPredict Nucleus | Remote DataPredict ML Model Control Dashboard Website |
| DataPredict Agent | Large Language Models (LLMs) Management System |
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Usage Preview
Credit to @Cffex for providing the original codes for AIs’ movement and sensing. Also thanks to @noisecooldeadpool362 for providing code improvements related to angle calculations (applies to version 5 and later versions).
Example Learning AI Codes
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Self-Learning Sword-Fighting AIs [Version 9] (Uses Release Version 2.40)
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Dynamic Difficulty Adjustment (Note that you will not be aware that you are having fun while having fun.)
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Retention (Note that you will not be aware that you are having fun while having fun.)
(DataPredict’s and TensorL-2D’s libraries terms and conditions apply due to source code containing these libraries.)
API Sample
-- Basic model construction.
local LinearRegression = DataPredict.Models.LinearRegression.new({maximumNumberOfIterations = 30})
local costArray = LinearRegression:train(featureMatrix, labelVector)
local predictedLabelVector = LinearRegression:predict(featureMatrix)
-- Saving and loading model parameters.
local ModelParameters = LinearRegression:getModelParameters()
LinearRegression:setModelParameters(ModelParameters)
Here Are What Other Users Have Made Using This Library!
Plugins And Scripts That Complements With This Library
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Parallel Luau (For speeding up the self-learning AIs’ training process through multi-threading.)
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StepPhysics Plugin API (For speeding up the self-learning AIs’ training process through speeding up the environment.)
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Graph Module (For plotting graphs.)
Please send bug reports here. It seems like the library got too large for me to maintain and bug hunting myself.
Overview
Machine Learning + Deep Learning + Reinforcement Learning
Library
Download Links
ModuleScripts For This Library:
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DataPredict Release Version 2.40 (Fully Tested Beta 2.42.0) - Added Early Nan (Not A Number) Value Check For Early Stopping
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TensorL-2D (Must Install)
Who Uses This Library?
- Senior game developers
- University students
- Researchers
- AI enthusiasts
(Some proof can be seen from the likes by other people on this first post.)
Quick Introduction
What is machine learning?
Machine learning is a way for computers to predict information based on the data we given to them. Machine learning can do three main tasks: Regression, Classification and Clustering.
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Regression: Generates a continuous value (e.g. -1.1, 2.09, 20) from given data.
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Classification: Generates a discrete value (e.g. 1, 2, 3), mainly for classifying given data.
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Clustering: Generates centroids (center of data) based on the given data and predict which centroids that a data belongs to.
What is deep learning?
Mainly covers the neural network models.
So how do we use this library?
In machine/deep learning, we mainly need to do training before we can predict things. To train, we need a lot of data and choose the correct models so that we can achieve very good results. Once training is done, you can use the model to predict values based on the data that was never seen before by the model.
Features
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Deep reinforcement learning models, tabular reinforcement learning, deep generative models and sequence models are included
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Optimizers, regularizers and a lot of utilities
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Distributed training
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Object-orientated library
What I Have Used It For
In one of my games (zero players, but somewhat released), it is mainly used for:
- Some of my AI Assistants (to help enhance gaming experience and variety)
- Sales analysis for better product pricing strategy (when the game becomes popular).
- AI players for player retention during durations of low players count.
If you are curious how my game looks like, you can have a look here:
https://www.roblox.com/games/11383260439/BETA-Terrae-Uprising-Pre-Mana
Join our official Discord server: DataPredict Community
Video Tutorials Playlist: DataPredict Video Tutorials Playlist
- Yes, immediately.
- Yes, but in the future.
- Not yet, just learning.
- No.
- Roblox games.
- Roblox plugins.
- External applications.
- Consulting.
- Research.
- Learning.
- Comparing with other libraries.
- Building competing libraries against existing libraries.
- Building competing libraries against DataPredict™.
- Yes, immediately.
- Yes, after more testing.
- No.
- Daily.
- Weekly.
- Monthly.
- Rarely.
- Once.
- 1
- 2
- 3
- 4
- 5
- Yes.
- No. I can already use DataPredict without any convincing.
- No. We’re already considering to use DataPredict in most of our projects.
- No. We’re currently not considering to use DataPredict for our games.
- No. I am not interested in DataPredict itself.
- The current ones that are listed on the DataPredict’s main documentation.
- Tiered flat fee USD subscription based on the individual projects’ revenue for that specific project’s usage.
- Tiered flat fee USD subscription based on the organization’s revenue for organization-wide usage.
- One-time large fee at 50K - 100K USD for organization-wide projects.
- One-time medium fee at 10K - 20K USD per project.
- Free for research usage. We’re often tight on budgets.
- Free for research usage. We have to deal with bureaucracy if it was paid.
- Free, because you shouldn’t monetize your 3 years of near-PhD hard work while your library make my games more advanced and makes me more money than usual.
