Practical overview
When it fits
Use it as an educational reference when you want to study machine-learning implementations at the NumPy level, particularly neural-network training concepts.
Who it is for
- Learners studying how neural networks and other machine-learning models are implemented.
- Readers seeking NumPy-based examples of model concepts and training methods.
Documented uses
- Explore how neural-network layers, weights, biases and activations work together.
- Follow explanations of forward propagation, loss measurement, gradient descent and backpropagation.
- Browse implementation material covering transformers and distributed models.
Requirements & limits
The repository is public on GitHub and identifies its license as MIT.
- The README describes implementations using only NumPy.
- The available README material explains neural-network concepts and names transformer and distributed-model sections.
- The project is a notebook repository rather than a documented hosted service or API.
Check current requirements in the official documentation. Source-based guidance does not mean OpenViST has run or independently tested this project.
Understand the neural-network training workflow
Follow the README’s conceptual sequence for computing outputs and describing how weights and biases are adjusted to reduce loss.
Before you start
- Familiarity with the README’s neural-network concepts: layers, activations, weights, and biases.
Steps
For each layer after the input layer, compute each neuron’s weighted sum from the previous layer’s activations and add its bias.
Apply an activation function; the README’s example uses ReLU.
Measure output error with the described mean squared error loss.
Use backpropagation to calculate gradients and gradient descent to update weights and biases toward lower loss; repeat.
Expected outcome
A conceptual outline of the README’s forward-propagation and training process, including loss measurement and parameter updates.
Guide limitations
- This describes the workflow, not executable code or a tested training procedure.
- The README lists transformer and distributed-model sections, but this recipe covers only the neural-network workflow.
Sources & review
Source record details
- Source availability
- Active
- Sources checked
- 13 October 2026
- Original website title
- DorsaRoh/Machine-Learning · Official homepage title · retained in its source language