semvec/pypi-packages huggingface dataset:
microtinygrad 0.0.1
tiny nn library that allows you to train classification neural networks
Requires Python: >=3.7

"# microtinygrad" 

 Micro Tiny neural network library that allows training of simple neural networks through mini-batch gradient descent only using numpy and pandas. Currently only provides support for pandas dataframes. Useful if you want to train small neural networks and quick tuning of hyperaparameters. 
 # Motivation

 In order to learn what goes on under the hood of neural network's backpropagation, I decided to implement it myself. However, my algorithm provides a more analytical solution (Calculating closed form gradients), rather than the approaches used by other neural network libaries (Micrograds Value Tree). Thus, this only currently supports common neural network patterns Linear Activation/Regression amd SoftMax Cross Entropy.  
 

 # Simple Quick Start 

```javascript I'm tab B
nn = NeuralNetwork()
```
brease-sdk 1.2.2
Python Client SDK Generated by Speakeasy
Requires Python: >=3.9

# brease-sdk

<!-- Start SDK Installation -->
## SDK Installation

```bash
pip install brease-sdk
```
<!-- End SDK Installation -->

## SDK Example Usage
<!-- Start SDK Example Usage -->
```python
import sdk
from sdk.models import operations, shared

s = sdk.SDK()

req = operations.AddRuleRequest(
    add_rule_input=shared.AddRuleInput(
        rule=shared.ModelsRule(
            action='corrupti',
            description='provident',
            expression=shared.ModelsRuleExpression3(
                condition=shared.Condition(
                    base=shared.ConditionBase2(
                        ref=shared.ConditionBaseRef(
                            dst='unde',
                            src='nulla',
                        ),
                    ),
                    parameter=False,
                    type=shared.ConditionType.RGX,
                ),
            ),
            id='69a674e0-f467-4cc8-b96e-d151a05dfc2d',
            target=shared.ModelsTarget(
                target='at',
                target_value='maiores',
                type='molestiae',
            ),
        ),
    ),
    context_id='quod',
)

res = s.context_id.add_rule(req, operations.AddRuleSecurity(
    api_token="",
))

if res.api_add_rule_response is not None:
    # handle response
```
<!-- End SDK Example Usage -->

<!-- Start SDK Available Operations -->
## Available Resources and Operations


### [context_id](docs/contextid/README.md)

* [add_rule](docs/contextid/README.md#add_rule) - Adds a new rule to the context
* [evaluate_rules](docs/contextid/README.md#evaluate_rules) - Evaluate rules within a context on the provided object
* [get_all_rules](docs/contextid/README.md#get_all_rules) - Returns all rules with the context
* [remove_rule](docs/contextid/README.md#remove_rule) - Removes a rule from the context
* [replace_rule](docs/contextid/README.md#replace_rule) - Replaces an existing rule within the context
<!-- End SDK Available Operations -->

### Maturity

This SDK is in beta, and there may be breaking changes between versions without a major version update. Therefore, we recommend pinning usage
to a specific package version. This way, you can install the same version each time without breaking changes unless you are intentionally
looking for the latest version.

### Contributions

While we value open-source contributions to this SDK, this library is generated programmatically.
Feel free to open a PR or a Github issue as a proof of concept and we'll do our best to include it in a future release !

### SDK Created by [Speakeasy](https://docs.speakeasyapi.dev/docs/using-speakeasy/client-sdks)