A lightweight machine learning library inspired by PyTorch
Project description
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.
Release history Release notifications | RSS feed
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Source Distribution
minitorch-0.1.0.tar.gz
(8.9 kB
view details)
Built Distribution
Filter files by name, interpreter, ABI, and platform.
If you're not sure about the file name format, learn more about wheel file names.
Copy a direct link to the current filters
File details
Details for the file minitorch-0.1.0.tar.gz.
File metadata
- Download URL: minitorch-0.1.0.tar.gz
- Upload date:
- Size: 8.9 kB
- Tags: Source
- Uploaded using Trusted Publishing? No
- Uploaded via: twine/4.0.0 CPython/3.12.11
File hashes
| Algorithm | Hash digest | |
|---|---|---|
| SHA256 |
a511923edd68db1a0b85e39efb61eb826c369227187162f6b43cd3a3440444dc
|
|
| MD5 |
17bfbc0cf13282569d3b8507501fa13c
|
|
| BLAKE2b-256 |
2d513cca1544f2a03e27d2e2f34a9069e095aa286e9ec4dbbf2a0910ccde6914
|
File details
Details for the file minitorch-0.1.0-py3-none-any.whl.
File metadata
- Download URL: minitorch-0.1.0-py3-none-any.whl
- Upload date:
- Size: 9.7 kB
- Tags: Python 3
- Uploaded using Trusted Publishing? No
- Uploaded via: twine/4.0.0 CPython/3.12.11
File hashes
| Algorithm | Hash digest | |
|---|---|---|
| SHA256 |
c4802be44b18d87afb04290f7dd95d991fcc3403ea590494623bbcc7be053f9c
|
|
| MD5 |
92d9c5b127230f6d943383cb4f2a8250
|
|
| BLAKE2b-256 |
6abc965257dfa5a4a02d7e5965d395d2a8989f6dca131e2fb0d15c50fd187c86
|
