Adding Better Documentation and Modifying ReadME.

[kartikdutt18]

Since this repository currently focuses more on tutorials and example like code for beginners to help get started. I think some changes that need to be made:

1. Generalized tutorial in each folder.
   They need to be step by step format for anyone who reads the code so that one could apply the code here to his/her dataset. The tutorial should also explain in brief why some lines were added especially the shape change in dataset required for Time Series Forecasting.

2. Modifying readme to match the current aim of this repo. For more details refer Implementation of Tests? #57.

I think it would make sense to add these documentation for the person who also takes up the task of simplifying the model.

Suggestion: Rather than one person adding all tutorial maybe we could each person could add documentation for each folder namely vae, mnist and LSTMs (taking this up).

[codeboy5]

Since this repository currently focuses more on tutorials and example like code for beginners to help get started. I think some changes that need to be made:

1. Generalized tutorial in each folder.
   They need to be step by step format for anyone who reads the code so that one could apply the code here to his/her dataset. The tutorial should also explain in brief why some lines were added especially the shape change in dataset required for Time Series Forecasting.
2. Modifying readme to match the current aim of this repo. For more details refer Implementation of Tests? #57.

I think it would make sense to add these documentation for the person who also takes up the task of simplifying the model.

Suggestion: Rather than one person adding all tutorial maybe we could each person could add documentation for each folder namely vae, mnist and LSTMs (taking this up).

I would love to work on the mnist model.
Thanks

[kartikdutt18]

Hey @codeboy5, This is how I imagined the tutorial would go like:

1. Intro (Getting Started with mnist or anyother dataset).
2. Steps to process the dataset and why we processed it the way we did it in the code.
3. Creating a model (Generalized set of instructions), more details about the model and code in the folder of the model implementation.
4. Training and inference.
5. The results we got.

This should be a step by step readme, with the main aim that user should be copy the code, understand it from the readme and use for anything he wants to get started.

I don't think we need to include why convolution works the way it does or for any other algorithm, Just a brief about why we choose this layer / model and what it does should be sufficient.

[kartikdutt18]

This is really good as well. https://yashk2810.github.io/Applying-Convolutional-Neural-Network-on-the-MNIST-dataset/

[codeboy5]

So do we want the documentation in separate README file ?
The plan you proposed sounds good to me, pretty comprehensive

[kartikdutt18]

I think each folder will have a readme. The main readme (whoever takes it up will have flow of the repo, Might take this if no one else does). Then in each folder like mnist we would have another readme, here we would focus on the data and results. The third readme would be inside folder of the model (Describing why and how of the model). Hopefully this makes sense and is not too overwhelming. Thanks.

[codeboy5]

@kartikdutt18 So in the current folder format, there's this Kaggle Folder and different models inside them.
So I would place the README about the dataset in this folder and then README related to different models inside their respective folders e.g. DigitRecognizerCNN.
Does this sound okay?

[kartikdutt18]

That sounds great. Yes that's what I thought it would go like.

[mlpack-bot]

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