# Search results for "Value At Risk"

## Probability for Machine Learning

Probability for Machine Learning Discover How To Harness Uncertainty With Python Machine Learning DOES NOT MAKE SENSE Without Probability What is Probability?…it’s about handling uncertainty Uncertainty involves making decisions with incomplete information, and this is the way we generally operate in the world. Handling uncertainty is typically described using everyday words like chance, luck, and […]

## What Is Probability?

Uncertainty involves making decisions with incomplete information, and this is the way we generally operate in the world. Handling uncertainty is typically described using everyday words like chance, luck, and risk. Probability is a field of mathematics that gives us the language and tools to quantify the uncertainty of events and reason in a principled […]

## How to Develop a Horizontal Voting Deep Learning Ensemble to Reduce Variance

Predictive modeling problems where the training dataset is small relative to the number of unlabeled examples are challenging. Neural networks can perform well on these types of problems, although they can suffer from high variance in model performance as measured on a training or hold-out validation datasets. This makes choosing which model to use as […]

## 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 […]

## Use Weight Regularization to Reduce Overfitting of Deep Learning Models

Neural networks learn a set of weights that best map inputs to outputs. A network with large network weights can be a sign of an unstable network where small changes in the input can lead to large changes in the output. This can be a sign that the network has overfit the training dataset and […]

## How to Develop Multivariate Multi-Step Time Series Forecasting Models for Air Pollution

Real-world time series forecasting is challenging for a whole host of reasons not limited to problem features such as having multiple input variables, the requirement to predict multiple time steps, and the need to perform the same type of prediction for multiple physical sites. The EMC Data Science Global Hackathon dataset, or the ‘Air Quality […]

## How to Develop Baseline Forecasts for Multi-Site Multivariate Air Pollution Time Series Forecasting

Real-world time series forecasting is challenging for a whole host of reasons not limited to problem features such as having multiple input variables, the requirement to predict multiple time steps, and the need to perform the same type of prediction for multiple physical sites. The EMC Data Science Global Hackathon dataset, or the ‘Air Quality […]

## 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 […]

## 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 […]

## 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 […]