https://github.com/koushik-elite/tv-script-generation
TV Script Generation from Udacity Deep Learning Nanodegree
https://github.com/koushik-elite/tv-script-generation
deep-learning machine-learning nanodegree neural-network notebook notebook-jupyter numpy python pytorch recurrent-neural-networks regression-models rnn-pytorch
Last synced: 2 days ago
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TV Script Generation from Udacity Deep Learning Nanodegree
- Host: GitHub
- URL: https://github.com/koushik-elite/tv-script-generation
- Owner: koushik-elite
- Created: 2019-02-27T04:36:25.000Z (over 6 years ago)
- Default Branch: master
- Last Pushed: 2019-03-17T08:07:10.000Z (over 6 years ago)
- Last Synced: 2025-01-22T01:36:34.717Z (9 months ago)
- Topics: deep-learning, machine-learning, nanodegree, neural-network, notebook, notebook-jupyter, numpy, python, pytorch, recurrent-neural-networks, regression-models, rnn-pytorch
- Language: Jupyter Notebook
- Size: 1.18 MB
- Stars: 2
- Watchers: 2
- Forks: 1
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# TV Script Generation
In this project, you'll generate your own [Seinfeld](https://en.wikipedia.org/wiki/Seinfeld) TV scripts using RNNs. You'll be using part of the [Seinfeld dataset](https://www.kaggle.com/thec03u5/seinfeld-chronicles#scripts.csv) of scripts from 9 seasons. The Neural Network you'll build will generate a new ,"fake" TV script, based on patterns it recognizes in this training data.
Final Project [Notebook](/dlnd_tv_script_generation.ipynb)
## 1. Installation
Download Anaconda
| | Linux | Mac | Windows |
|--------|-------|-----|---------|
| 64-bit | [64-bit (bash installer)][lin64] | [64-bit (bash installer)][mac64] | [64-bit (exe installer)][win64]
| 32-bit | [32-bit (bash installer)][lin32] | | [32-bit (exe installer)][win32]
[win64]: https://repo.anaconda.com/archive/Anaconda3-2018.12-Windows-x86_64.exe
[win32]: https://repo.anaconda.com/archive/Anaconda3-2018.12-Windows-x86.exe
[mac64]: https://repo.anaconda.com/archive/Anaconda3-2018.12-MacOSX-x86_64.sh
[lin64]: https://repo.anaconda.com/archive/Anaconda3-2018.12-Linux-x86_64.sh
[lin32]: https://repo.anaconda.com/archive/Anaconda3-2018.12-Linux-x86.sh
**Install** [Anaconda](https://docs.anaconda.com/anaconda/install/) on your machine. Detailed instructions:
## 2. Create and Activate the Environment
Please go though this [doc](https://conda.io/projects/conda/en/latest/user-guide/tasks/manage-environments.html) before you creating an environment.
After that create a environment using following command
```
conda create --name deep-learning
```
Then activate the environment using following command
```
activate deep-learning
```
#### Git and version control
These instructions also assume you have `git` installed for working with Github from a terminal window, but if you do not, you can download that first with the command:
```
conda install git
```
**Now, you can create a local version of the project**
1. Clone the repository, and navigate to the downloaded folder. This may take a minute or two to clone due to the included image data.
```
git clone https://github.com/koushik-elite/TV-Script-Generation.git
cd TV-Script-Generation
```
2. Install PyTorch and torchvision; this should install the latest version of PyTorch.
- __Linux__ or __Mac__:
```
conda install pytorch torchvision -c pytorch
```
- __Windows__:
```
conda install pytorch -c pytorch
pip install torchvision
```
3. Install a few required pip packages, which are specified in the requirements text file (including OpenCV).
```
pip install -r requirements.txt
```
Or
```
conda install --yes --file requirements.txt
```
4. That's it!, Now run the project using following command, check you default browser and open dlnd_tv_script_generation.ipynb file
```
jupyter notebook
```