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Articles related to "step"


Natural Language Processing Pipeline

  • This step-by-step processing of text is known as a NLP pipeline.
  • The main components of a generic pipeline for modern-day, data-driven NLP system development are Data acquisition, Text cleaning, Pre-processing, Feature engineering, Modeling, Evaluation, Deployment, Monitoring, and model updating.
  • The first step in the process of developing any NLP system is to collect data relevant to the given task.
  • After cleaning, text data often has a lot of variations and needs to be converted into a canonical (principle or a pre-defined way) form.
  • In this article, we will discuss the first two steps of the NLP pipeline in detail with some code examples.
  • Text extraction is a standard data-wrangling step, and we don’t usually employ any NLP-specific techniques during this process.
  • In order to achieve better results, we have to choose appropriate data acquisition and text cleaning strategies according to our application.

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Simplify Machine Learning Workflows

  • A detailed Notebook which includes exploratory data analysis (EDA) can be found on my GitHub. The results of the tutorial is an example using Pipelines on the Kaggle Ames Housing dataset in which we get scores in the top 20% of the leaderboard!
  • Pipelines are an easy way to bundle data preprocessing, transformation, and modeling code allowing you to apply the “bundle” as if it were a single step.
  • This section presents an example of using a Scikit-Learn Pipeline with custom transformers to Cross-Validate and compare several models.
  • First, we define a helper function that takes a Pipeline and performs an X folds Cross-Validation for a particular model and return the Root Mean Square Error (RMSE) on the Log of the house Sale Price.
  • In this tutorial, we defined and created a Pipeline to preprocesses/transform the Kaggle Ames Housing dataset and evaluated the models with an X folds Cross-Validation RMSE score.

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Simplify Machine Learning Workflows

  • A detailed Notebook which includes exploratory data analysis (EDA) can be found on my GitHub. The results of the tutorial is an example using Pipelines on the Kaggle Ames Housing dataset in which we get scores in the top 20% of the leaderboard!
  • Pipelines are an easy way to bundle data preprocessing, transformation, and modeling code allowing you to apply the “bundle” as if it were a single step.
  • This section presents an example of using a Scikit-Learn Pipeline with custom transformers to Cross-Validate and compare several models.
  • First, we define a helper function that takes a Pipeline and performs an X folds Cross-Validation for a particular model and return the Root Mean Square Error (RMSE) on the Log of the house Sale Price.
  • In this tutorial, we defined and created a Pipeline to preprocesses/transform the Kaggle Ames Housing dataset and evaluated the models with an X folds Cross-Validation RMSE score.

save | comments | report | share on


Simplify Machine Learning Workflows

  • A detailed Notebook which includes exploratory data analysis (EDA) can be found on my GitHub. The results of the tutorial is an example using Pipelines on the Kaggle Ames Housing dataset in which we get scores in the top 20% of the leaderboard!
  • Pipelines are an easy way to bundle data preprocessing, transformation, and modeling code allowing you to apply the “bundle” as if it were a single step.
  • This section presents an example of using a Scikit-Learn Pipeline with custom transformers to Cross-Validate and compare several models.
  • First, we define a helper function that takes a Pipeline and performs an X folds Cross-Validation for a particular model and return the Root Mean Square Error (RMSE) on the Log of the house Sale Price.
  • In this tutorial, we defined and created a Pipeline to preprocesses/transform the Kaggle Ames Housing dataset and evaluated the models with an X folds Cross-Validation RMSE score.

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How to Deploy your Dash App with Heroku

  • You just spent weeks developing your Dash app.
  • This tutorial will show you how to do just that using Heroku.
  • Heroku is a cloud application platform that allows you to run your apps, completely free of charge.
  • You should also download 4 files, all available right here: https://github.com/francoisstamant/dash_heroku_deployment.
  • App.py is the file that contains your Dash appplication.
  • That’s the file that you should be changing if you want to update/improve the app.
  • Alright, now that you got everything needed, here is the step-by-step tutorial that will get you to deploy your Dash app for everyone else to see!
  • Basically, it will be https://name-of-your-app.herokuapp.com.
  • You will notice that if you go on your Heroku account now, you will see your app page.
  • To change the app, simply modify the app.py file.
  • Then, the following 4 commands will allow you to push the changes to your Heroku app.

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How to Deploy your Dash App with Heroku

  • You just spent weeks developing your Dash app.
  • This tutorial will show you how to do just that using Heroku.
  • Heroku is a cloud application platform that allows you to run your apps, completely free of charge.
  • You should also download 4 files, all available right here: https://github.com/francoisstamant/dash_heroku_deployment.
  • App.py is the file that contains your Dash appplication.
  • That’s the file that you should be changing if you want to update/improve the app.
  • Alright, now that you got everything needed, here is the step-by-step tutorial that will get you to deploy your Dash app for everyone else to see!
  • Basically, it will be https://name-of-your-app.herokuapp.com.
  • You will notice that if you go on your Heroku account now, you will see your app page.
  • To change the app, simply modify the app.py file.
  • Then, the following 4 commands will allow you to push the changes to your Heroku app.

save | comments | report | share on


How to Deploy your Dash App with Heroku

  • You just spent weeks developing your Dash app.
  • This tutorial will show you how to do just that using Heroku.
  • Heroku is a cloud application platform that allows you to run your apps, completely free of charge.
  • You should also download 4 files, all available right here: https://github.com/francoisstamant/dash_heroku_deployment.
  • App.py is the file that contains your Dash appplication.
  • That’s the file that you should be changing if you want to update/improve the app.
  • Alright, now that you got everything needed, here is the step-by-step tutorial that will get you to deploy your Dash app for everyone else to see!
  • Basically, it will be https://name-of-your-app.herokuapp.com.
  • You will notice that if you go on your Heroku account now, you will see your app page.
  • To change the app, simply modify the app.py file.
  • Then, the following 4 commands will allow you to push the changes to your Heroku app.

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Curve Detecting Neurons

  • We found that practitioners generally had to choose between several algorithms, each with significant trade-offs such as robustness to different kinds of visual “noise” (for instance, texture), even in images much less complex than the natural images in ImageNet. For instance, this answer on StackOverflow claims “The problem [of curve detection], in general, is a very challenging one and, except for toy examples, there are no good solutions.” Additionally, many classical curve detection algorithms are too slow to run in real-time, or require often intractable amounts of memory..
  • Images that cause curve detectors to activate weakly, such as edges or angles, are a natural extension of the algorithm that InceptionV1 uses to implement curve detection.
  • Every time we use feature visualization to make curve neurons fire as strongly as possible we get images of curves, even when we explicitly incentivize the creation of different kinds of images using a diversity term.

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Disney Research neural face swapping technique can provide photorealistic, high resolution video

  • A new paper published by Disney Research in partnership with ETH Zurich describes a fully automated, neural network-based method for swapping faces in photos and videos – the first such method that results in high-resolution, megapixel resolution final results according to the researchers.
  • The researchers specifically intend this tech for use in replacing an existing actor’s performance with a substitute actor’s face, for instance when de-aging or increasing the age of someone, or potentially when portraying an actor who has passed away.
  • Also, there’s always the question of the ethical implication of any use of face-swapping technology, especially in video, since it could be used to fabricate credible video or photographic ‘evidence’ of something that didn’t actually happen.
  • Instead, it’s welcome that organizations like Disney Research are following the academic path and sharing the results of their work, so that others concerned about its potential malicious use can determine ways to flag, identify and protect against any bad actors.

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How to take a data science project from idea to production

  • Generally they are questions that can accurately gauge the candidates enthusiasm for the company’s data science problems or else test their practical experience.
  • One of these favourite interview questions is to ask a candidate to describe the process they would use to take a data science project from idea to production.
  • Therefore if a candidate skips the steps involved with working with other teams or stakeholders, I would conclude that they lack experience.
  • I will also provide some examples from my data science experience to illustrate the steps and why I think each are vitally important to a well-executed data science project.
  • In which case you may need to substantially design your experiment in advance in order to convince your company that access to this data and thus your results would provide them substantial benefit.

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