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Machine Learning Mastery

Start Here with Machine Learning

Need Help Getting Started with Applied Machine Learning? These are the Step-by-Step Guides that You’ve Been Looking For! What do you want help with? The most common question I’m asked is: “how do I get started?” My best advice for getting started in machine learning is broken down into a 5-step process: Step 1: Adjust Mindset. […]

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Practical Recommendations for Deep Learning Neural Network Practitioners

Recommendations for Deep Learning Neural Network Practitioners

Deep learning neural networks are relatively straightforward to define and train given the wide adoption of open source libraries. Nevertheless, neural networks remain challenging to configure and train. In his 2012 paper titled “Practical Recommendations for Gradient-Based Training of Deep Architectures” published as a preprint and a chapter of the popular 2012 book “Neural Networks: […]

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A Gentle Introduction to Dropout for Regularizing Deep Neural Networks

A Gentle Introduction to Dropout for Regularizing Deep Neural Networks

Deep learning neural networks are likely to quickly overfit a training dataset with few examples. Ensembles of neural networks with different model configurations are known to reduce overfitting, but require the additional computational expense of training and maintaining multiple models. A single model can be used to simulate having a large number of different network […]

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Activation Regularization for Reducing Generalization Error in Deep Learning Neural Networks

A Gentle Introduction to Activation Regularization in Deep Learning

Deep learning models are capable of automatically learning a rich internal representation from raw input data. This is called feature or representation learning. Better learned representations, in turn, can lead to better insights into the domain, e.g. via visualization of learned features, and to better predictive models that make use of the learned features. A […]

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The Close Relationship Between Applied Statistics and Machine Learning

The Close Relationship Between Applied Statistics and Machine Learning

The machine learning practitioner has a tradition of algorithms and a pragmatic focus on results and model skill above other concerns such as model interpretability. Statisticians work on much the same type of modeling problems under the names of applied statistics and statistical learning. Coming from a mathematical background, they have more of a focus […]

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