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• Efficient Neural Architecture Search via Parameter Sharing
  • "We propose Efficient Neural Architecture Search (ENAS), a fast and inexpensive approach for automatic model design. In ENAS, a controller learns to discover neural network architectures by searching …

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• Visualizing Incomplete and Missing Data
  • "... But a lot of the time (most of the time?), the data you work with is not complete. There is missing data. ... What do you do when this happens? ... Here …

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• Multiple imputation utilising denoising autoencoder for approximate Bayesian inference
  • "Missing data is a widespread problem in machine learning. Bayesian inference is a robust solution to imputing missing values, particularly if multiple imputations are used to …

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• Gradient Boosting explained
  • "Gradient boosting (GB) is a machine learning algorithm developed in the late '90s that is still very popular. It produces state-of-the-art results for many commercial (and academic) applications. This page explains how …

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• Python module to perform under sampling and over sampling with various techniques
  • "imbalanced-learn is a python package offering a number of re-sampling techniques commonly used in datasets showing strong between-class imbalance. It is compatible with …

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• The 10 Statistical Techniques Data Scientists Need to Master
  • “data scientist is a person who is better at statistics than any programmer and better at programming than any statistician.”

• Unleash a faster Python on your …

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• Ultimate Guide To Statistical Significance Tests in R
  • Implement and interpret the commonly used statistical significance tests in R, the purpose, when to use and how to interpret the result.
• Word2Vec (skip-gram model)
  • The skip-gram neural …

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• CAN (Creative Adversarial Network) - Explained

  [caption id="attachment_2318" align="alignright" width="300"] CAN (Creative Adversarial Network) - Explained[/caption]

  • GANs (Generative Adversarial Networks), a type of Deep Learning networks, have been very successful in creating non-procedural content …

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• How to Apply Machine Learning to Event Processing
  • How do you combine historical Big Data with machine learning for real-time analytics? An approach is outlined with different software vendors, business use cases, and best practices.
• J …

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• Python Plotting for Exploratory Analysis
• What method can be used to detect seasonality in data?
  • A really good way to find periodicity in any regular series of data is to inspect its power spectrum after removing …

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