Search results for "Value At Risk"

Depiction of CNN Model for Accelerompter Data

Deep Learning Models for Human Activity Recognition

Human activity recognition, or HAR, is a challenging time series classification task. It involves predicting the movement of a person based on sensor data and traditionally involves deep domain expertise and methods from signal processing to correctly engineer features from the raw data in order to fit a machine learning model. Recently, deep learning methods […]

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Why Initialize a Neural Network with Random Weights?

Why Initialize a Neural Network with Random Weights?

The weights of artificial neural networks must be initialized to small random numbers. This is because this is an expectation of the stochastic optimization algorithm used to train the model, called stochastic gradient descent. To understand this approach to problem solving, you must first understand the role of nondeterministic and randomized algorithms as well as […]

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The Role of Randomization to Address Confounding Variables in Machine Learning

The Role of Randomization to Address Confounding Variables in Machine Learning

A large part of applied machine learning is about running controlled experiments to discover what algorithm or algorithm configuration to use on a predictive modeling problem. A challenge is that there are aspects of the problem and the algorithm called confounding variables that cannot be controlled (held constant) and must be controlled-for. An example is […]

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A Gentle Introduction to Effect Size Measures in Python

A Gentle Introduction to Effect Size Measures in Python

Statistical hypothesis tests report on the likelihood of the observed results given an assumption, such as no association between variables or no difference between groups. Hypothesis tests do not comment on the size of the effect if the association or difference is statistically significant. This highlights the need for standard ways of calculating and reporting […]

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Statistical Significance Tests for Comparing Machine Learning Algorithms

Statistical Significance Tests for Comparing Machine Learning Algorithms

Comparing machine learning methods and selecting a final model is a common operation in applied machine learning. Models are commonly evaluated using resampling methods like k-fold cross-validation from which mean skill scores are calculated and compared directly. Although simple, this approach can be misleading as it is hard to know whether the difference between mean […]

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Basics of Mathematical Notation for Machine Learning

Basics of Mathematical Notation for Machine Learning

You cannot avoid mathematical notation when reading the descriptions of machine learning methods. Often, all it takes is one term or one fragment of notation in an equation to completely derail your understanding of the entire procedure. This can be extremely frustrating, especially for machine learning beginners coming from the world of development. You can […]

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How to Use Small Experiments to Develop a Caption Generation Model in Keras

How to Use Small Experiments to Develop a Caption Generation Model in Keras

Caption generation is a challenging artificial intelligence problem where a textual description must be generated for a photograph. It requires both methods from computer vision to understand the content of the image and a language model from the field of natural language processing to turn the understanding of the image into words in the right […]

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