Metadata-Version: 2.4
Name: dnn_tech_DS
Version: 0.1.0
Summary: A grab-bag of ready-to-copy example scripts for common deep-learning architectures (NN, CNN, ResNet, RNN/LSTM, GRU, AutoEncoder, VAE, text classification, GAN, DQN).
Author-email: LORD-MEGATRON <aryanjbagwe@gmail.com>
License: MIT
Keywords: deep-learning,neural-network,examples,tensorflow,pytorch
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Classifier: Intended Audience :: Developers
Classifier: Topic :: Software Development :: Libraries
Requires-Python: >=3.8
Description-Content-Type: text/markdown
License-File: LICENSE
Dynamic: license-file

# dnn-tech

A grab-bag of ready-to-copy example scripts covering common deep-learning
architectures: basic feed-forward NN, CNN, ResNet, RNN/LSTM, GRU sentiment
model, AutoEncoder, VAE image generation, text classification, GAN, and
DQN reinforcement learning — all on MNIST/toy data so they run out of the box.

Each example is stored as a string and printed to stderr when you call the
corresponding function, so you can quickly copy a working starting point for
a given architecture.

## Install

```bash
pip install dnn-tech
```

(For local development, from the repo root:)

```bash
pip install -e .
```

## Usage

```python
import dnn_tech

dnn_tech.commands()      # list every available example
dnn_tech.p2_CNN()        # print the CNN example script (goes to stderr)
dnn_tech.all_lib()       # print `pip install` commands for the libraries
                          # used across the examples (tensorflow, torch, ...)
```

Or from the command line:

```bash
dnn-tech                 # list available example functions
dnn-tech p2_CNN           # print the CNN example script
dnn-tech all_lib           # print pip install commands for dependencies
```

## Available examples

| Function                  | Architecture                          |
|----------------------------|----------------------------------------|
| `p1_BasicNN()`             | Basic feed-forward NN (Keras, MNIST)   |
| `p2_CNN()`                 | CNN (Keras, MNIST)                     |
| `p3_Resnet()`               | ResNet-50 (PyTorch, torchvision)       |
| `p4_RNN_LSTM()`             | RNN/LSTM (Keras, toy sequence data)    |
| `p42_RNN_LSTM()`            | RNN/LSTM (Keras, sine-wave forecasting)|
| `p5_LSTM()`                 | Char-level LSTM text generation        |
| `p6_GRU_Sentiment()`        | GRU sentiment classifier               |
| `p7_AutoEncoder()`          | AutoEncoder                            |
| `p8_vae_imggen()`           | Variational AutoEncoder image gen      |
| `p10_text_classification()` | Text classification                    |
| `p11_gan()`                 | GAN                                    |
| `p12_deepq()`               | Deep Q-Network (PyTorch, CartPole)     |
| `all_lib()`                 | Print pip install commands             |
| `commands()`                | List all available commands            |

> **Note:** `commands()` mentions a `p9_trans()` (Transformer example), but
> that function isn't implemented in the source yet — calling it will raise
> `AttributeError`. Add it to `src/dnn_tech/core.py` and re-export it from
> `__init__.py` when it's ready.

## License

MIT
