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# Agenda | ||
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In the next few sections we will build a simple natural language processing | ||
(NLP) project from scratch. If you'd like to get the final result or have some | ||
issues along the way, you can download the fully reproducible | ||
[GitHub project](https://github.com/iterative/example-get-started) with: | ||
In the next few sections we'll build a simple natural language processing (NLP) | ||
project from scratch. If you'd like to get the final result or have any issues | ||
along the way, you can download the fully reproducible | ||
[GitHub project](https://github.com/iterative/example-get-started) by running: | ||
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```dvc | ||
$ git clone https://github.com/iterative/example-get-started | ||
``` | ||
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Otherwise, bear with us and we will introduce the basic DVC concepts and get to | ||
the same result together! | ||
Otherwise, bear with us and we'll introduce some basic DVC concepts to get the | ||
same results together! | ||
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The idea of the project is a simplified version of the | ||
[Tutorial](/doc/tutorials/deep). It explores the NLP problem of predicting tags | ||
for a given StackOverflow question. For example, we want a classifier that can | ||
predict posts about the Python language by tagging them `python`. | ||
The idea of the project is a simplified version of our | ||
[Deep Dive Tutorial](/doc/tutorials/deep). It explores the NLP problem of | ||
predicting tags for a given StackOverflow question. For example, we might want a | ||
classifier that can classify (or predict) posts about Python by tagging them | ||
with `python`. | ||
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![](/static/img/example-flow-2x.png) | ||
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Do not let the NLP nature of the example discourage you from using DVC in other | ||
Data Science areas. There was no strong reason behind picking the NLP area. On | ||
contrary, DVC is designed to be agnostic of frameworks, languages, etc. If you | ||
have data files or datasets and/or you produce other data files, models, | ||
datasets and you want to: | ||
This is a natural language processing context, but NLP isn't the only area of | ||
data science where DVC can help. DVC is designed to be agnostic of frameworks, | ||
languages, etc. If you have data files or datasets and/or you produce data | ||
files, models, or datasets and you want to: | ||
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- Capture and save those <abbr>data artifacts</abbr> the same way we capture | ||
- Capture and save those <abbr>data artifacts</abbr> the same way you capture | ||
code | ||
- Track and switch between different versions of the data easily | ||
- Be able to answer the question of how data artifacts (e.g. ML models) were | ||
built in the first place | ||
- Be able to compare them | ||
- Bring best practices to your team and get everyone on the same page | ||
- Track and switch between different versions of data easily | ||
- Understand how data artifacts (e.g. ML models) were built in the first place | ||
- Be able to compare models to each other | ||
- Bring software best practices to your team and get everyone on the same page | ||
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Then you are in a good place! Click the `Next` button below to start ↘ | ||
Then you're in the right place! Click the `Next` button below to start ↘ |