Features
- Tensor Operations: Automatic differentiation for addition, multiplication, subtraction, division, and summation.
- Neural Network Layers: Linear, LeakyReLU, ReLU, Sigmoid, Tanh, Softmax, Dropout, BatchNorm1D, Sequential.
- Loss Functions: MSE, BCE, Cross Entropy.
- Optimizer: Adam with learning rate scheduling.
- Pipeline: Training pipeline with early stopping and metrics (accuracy, R², F1, MAE).
- Synthetic Datasets: Mixed, classification, quadratic, and nonlinear data generation.
1 maintainer
Metadata
1 maintainer
Release files for minitorch 0.1.0
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
|---|---|---|---|
| minitorch-0.1.0.tar.gz | 8.9 kB | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| minitorch-0.1.0-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 18.6 kB
Release files / minitorch-0.1.0.tar.gz
| Download URL | minitorch-0.1.0.tar.gz |
|---|---|
| Size | 8.9 kB |
| Tags | Source |
|
SHA-256 checksum How to use checksums |
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No |
| Uploaded via |
twine/4.0.0 CPython/3.12.11
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Release files / minitorch-0.1.0-py3-none-any.whl
| Download URL | minitorch-0.1.0-py3-none-any.whl |
|---|---|
| Size | 9.7 kB |
| Tags | Python 3 |
|
SHA-256 checksum How to use checksums |
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Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
twine/4.0.0 CPython/3.12.11
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