Search results for "Block Chain"

A Gentle Introduction to Markov Chain Monte Carlo for Probability

A Gentle Introduction to Markov Chain Monte Carlo for Probability

Probabilistic inference involves estimating an expected value or density using a probabilistic model. Often, directly inferring values is not tractable with probabilistic models, and instead, approximation methods must be used. Markov Chain Monte Carlo sampling provides a class of algorithms for systematic random sampling from high-dimensional probability distributions. Unlike Monte Carlo sampling methods that are […]

Continue Reading 8
Scatter Plot of Imbalanced Dataset Transformed by SMOTE and Random Undersampling

SMOTE for Imbalanced Classification with Python

Imbalanced classification involves developing predictive models on classification datasets that have a severe class imbalance. The challenge of working with imbalanced datasets is that most machine learning techniques will ignore, and in turn have poor performance on, the minority class, although typically it is performance on the minority class that is most important. One approach […]

Continue Reading 166
A Gentle Introduction to Joint, Marginal, and Conditional Probability

A Gentle Introduction to Joint, Marginal, and Conditional Probability

Probability quantifies the uncertainty of the outcomes of a random variable. It is relatively easy to understand and compute the probability for a single variable. Nevertheless, in machine learning, we often have many random variables that interact in often complex and unknown ways. There are specific techniques that can be used to quantify the probability […]

Continue Reading 30
Programming Computer Vision with Python

8 Books for Getting Started With Computer Vision

Computer vision is a subfield of artificial intelligence concerned with understanding the content of digital images, such as photographs and videos. Deep learning has made impressive inroads on challenging computer vision tasks and makes the promise of further advances. Before diving into the application of deep learning techniques to computer vision, it may be helpful […]

Continue Reading 21
A Gentle Introduction to the Challenge of Training Deep Learning Neural Network Models

A Gentle Introduction to the Challenge of Training Deep Learning Neural Network Models

Deep learning neural networks learn a mapping function from inputs to outputs. This is achieved by updating the weights of the network in response to the errors the model makes on the training dataset. Updates are made to continually reduce this error until either a good enough model is found or the learning process gets […]

Continue Reading 13
How to Prepare a French-to-English Dataset for Machine Translation

How to Prepare a French-to-English Dataset for Machine Translation

Machine translation is the challenging task of converting text from a source language into coherent and matching text in a target language. Neural machine translation systems such as encoder-decoder recurrent neural networks are achieving state-of-the-art results for machine translation with a single end-to-end system trained directly on source and target language. Standard datasets are required […]

Continue Reading 50