semvec/pypi-packages huggingface dataset:
deepop 0.1.dev0
A mini Tensor framework for tensor operations on GPU.
Requires Python: >=3.8

# DeepOps
A Mini Deep learning library, accelerated on GPUs with PyCuda.

No no no.. I havent wrote this library on tensorflow or torch, it is a standalone machine. :P

Implemented backpropogations using reverse traversal, Support gradients and GPU operations, You can make a mini neural network (FNN) and use the in-house optimizer to train it on a dataset (e.g MNIST).

Tip: always give your tensor a funny name! :)

Note: Only for Educational Usage.

![alt text](https://cdn.pixabay.com/photo/2017/11/14/18/31/mushroom-2949539_960_720.jpg)

# Installation.
```
pip install deepops
```

# Tensor.
```
a = Tensor([1,2,3,4,5])
# deepop tensor 
```
# Attach to a cuda device.
```
a = Tensor([1,2,3,4,5])
a.device("gpu:0") # attach to gpu device.
```
# Check the Device.
```
a.where
# 'cpu'
```
# Addition.
```
a = Tensor([1.0,2.0])
print(a + a)
# GPU Operation
```
# Multiplication.
```
a = Tensor([1.0, 2.0])
print(a.mul(a))
print(a * a)
```
# Calculate Gradients.
```
Tensor = dp.Tensor
a1 = Tensor([1.0, 3.0, 1.0])
b1 = Tensor([7.0, 3.0, 5.0])

a2 = Tensor([4.0, 3.0, 1.0])
a3 = Tensor([3.0, 3.0, 1.0])
a4 = Tensor([7.0, 1.0, 6.0])
b2 = Tensor([1.0, 21.0, 12.0])

c = a1 * b1 + a3
d = a2 * b2 + a4
out = c * d

# backward
out.backward()

print(out.grad)
print(a1.grad)

```
# Run Tests.
```
python -m pytest -s

```

# Contribution is highly appreciated.
Please contribute to my work.

# TODOs
* write more tests...
* need a optimizer.
* support more operations.

# License
MIT
ML-pkg-MAD 0.0.1
A small package for quality analysis of machine learning models
Requires Python: >=3.7

# ML_pkg_MAD (Machine Learning package)

This is a small package for quality analysis of machine learning (ML) models. It provides an interface to an abstract ML model  for quality analysis as well as a k-nearest neaighbors (k-NN) model pre-trained on the MNIST dataset. 

## Installation

pip install ML_pkg_MAD