Data Science

An easy way to understand the most contributing features for anomaly detection

Illustration by author


A graphical display to better understand and interpret your dataset

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Illustration by Author
  1. Pytorch Tutorial for Beginners
  2. Understand Tensor Dimensions in DL models
  3. CNN & Feature visualizations
  4. Hyperparameter tuning with Optuna
  5. K Fold Cross Validation (this post)
  6. Convolutional Autoencoder
  7. Denoising Autoencoder
  8. Variational Autoencoder


Natural Language Processing

A way to improve the classic Word Cloud with more robust features

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Compare implementation with Scikit-Learn’s TfidfVectorizer

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  1. Text pre-processing in Python
  2. Bag-of-Words with an Example in Python
  3. TF-IDF with an Example in Python
  4. Word Clouds with TF-IDF


Data Visualization

It’s time to build your first web analytic application

Photo by Myriam Jessier on Unsplash


They will help you to improve the effectiveness and the quality of your articles

Photo by Arnel Hasanovic on Unsplash

1. Write for publications

  • you can publish it on your profile.
  • submit the draft of your story in a publication


Machine Learning

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  1. Pytorch Tutorial for Beginners
  2. Understand Tensor Dimensions in DL models
  3. CNN & Feature visualizations
  4. Hyperparameter tuning with Optuna (this post)
  5. K Fold Cross-Validation
  6. Convolutional Autoencoder
  7. Denoising Autoencoder
  8. Variational Autoencoder


Manipulate time series data

Chart
Chart by the author.

Overview

  • Introduction to time series analysis
  • pandas time series indexing
  • Visualizations with plotly express
  • pandas functions, such as resample, rolling, and shift

Introduction

Eugenia Anello

Data Science student| Top 1500 writer on Medium | I like to share the concepts I learn everyday| https://www.linkedin.com/in/eugenia-anello-545711146

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