machine.academy
Numerical-Realization
machine.academy | Numerical-Realization | |
---|---|---|
1 | 3 | |
11 | 0 | |
- | - | |
7.6 | 0.0 | |
5 months ago | about 2 years ago | |
C# | C# | |
GNU General Public License v3.0 only | GNU General Public License v3.0 only |
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machine.academy
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Neural Network from Scratch
Nice! I made a gpu accelerated backpropagation lib a while ago to learn about NNs, if you are interested check it out here: https://github.com/zbendefy/machine.academy
Numerical-Realization
- A lightweight natural language generator (NLG) for numerical inputs I am developing to assist those working on AI or text-to-speech engines. This NLG enables English-based numerical realization to be performed on numbers as high as 999 centillion. No plans for localization at this time.
- A lightweight natural language generator (NLG) for numerical inputs I am developing to assist those working on AI or text-to-speech engines. This NLG enables English text generation for numbers as high as 999 centillion. No plans for localization at this time.
What are some alternatives?
ML-From-Scratch - Machine Learning From Scratch. Bare bones NumPy implementations of machine learning models and algorithms with a focus on accessibility. Aims to cover everything from linear regression to deep learning.
GazeTracker - Artificial intelligence tracker for OpenTrack
micrograd - A tiny scalar-valued autograd engine and a neural net library on top of it with PyTorch-like API
semantic-kernel - Integrate cutting-edge LLM technology quickly and easily into your apps
Catalyst - 🚀 Catalyst is a C# Natural Language Processing library built for speed. Inspired by spaCy's design, it brings pre-trained models, out-of-the box support for training word and document embeddings, and flexible entity recognition models.
Numerical-Realization - An English-based natural language generator for numerical inputs.
deepnet - Educational deep learning library in plain Numpy.
SoftGround - High performance runtime collider deformation in Unity
NNfSiX - Neural Networks from Scratch in various programming languages
CryptoNets - CryptoNets is a demonstration of the use of Neural-Networks over data encrypted with Homomorphic Encryption. Homomorphic Encryptions allow performing operations such as addition and multiplication over data while it is encrypted. Therefore, it allows keeping data private while outsourcing computation (see here and here for more about Homomorphic Encryptions and its applications). This project demonstrates the use of Homomorphic Encryption for outsourcing neural-network predictions. The scenario in mind is a provider that would like to provide Prediction as a Service (PaaS) but the data for which predictions are needed may be private. This may be the case in fields such as health or finance. By using CryptoNets, the user of the service can encrypt their data using Homomorphic Encryption and send only the encrypted message to the service provider. Since Homomorphic Encryptions allow the provider to operate on the data while it is encrypted, the provider can make predictions using a pre-t