The pixel values in images must be scaled prior to providing the images as input to a deep learning neural network model during the training or evaluation of the model. Traditionally, the images would have to be scaled prior to the development of the model and stored in memory or on disk in the scaled […]
A When should I standardize and normalize data? Standardization refers to scaling a variable that has a Gaussian distribution such that it has a mean of zero and a standard deviation of one. Normalization refers to scaling a variable that has any distribution so that all values are between zero and one. It is possible […]
Some machine learning algorithms will achieve better performance if your time series data has a consistent scale or distribution. Two techniques that you can use to consistently rescale your time series data are normalization and standardization. In this tutorial, you will discover how you can apply normalization and standardization rescaling to your time series data […]
Machine learning algorithms make assumptions about the dataset you are modeling. Often, raw data is comprised of attributes with varying scales. For example, one attribute may be in kilograms and another may be a count. Although not required, you can often get a boost in performance by carefully choosing methods to rescale your data. In […]
Data transformations enable data scientists to refine, normalize, and standardize raw data into a format ripe for analysis. These transformations are not merely procedural steps; they are essential in mitigating biases, handling skewed distributions, and enhancing the robustness of statistical models. This post will primarily focus on how to address skewed data. By focusing on […]
One of the pre-processing steps that are often carried out on images before feeding them into a machine learning algorithm is to convert them into a feature vector. As we will see in this tutorial, there are several advantages to converting an image into a feature vector that makes the latter more efficient. Among the […]
The Naive Bayes algorithm is a simple but powerful technique for supervised machine learning. Its Gaussian variant is implemented in the OpenCV library. In this tutorial, you will learn how to apply OpenCV’s normal Bayes algorithm, first on a custom two-dimensional dataset and subsequently for segmenting an image. After completing this tutorial, you will […]
Predictive modeling with deep learning is a skill that modern developers need to know. PyTorch is the premier open-source deep learning framework developed and maintained by Facebook. At its core, PyTorch is a mathematical library that allows you to perform efficient computation and automatic differentiation on graph-based models. Achieving this directly is challenging, although thankfully, […]
A single-layer neural network, also known as a single-layer perceptron, is the simplest type of neural network. It consists of only one layer of neurons, which are connected to the input layer and the output layer. In case of an image classifier, the input layer would be an image and the output layer would be […]
Recurrent neural network can be used for time series prediction. In which, a regression neural network is created. It can also be used as generative model, which usually is a classification neural network model. A generative model is to learn certain pattern from data, such that when it is presented with some prompt, it can […]
