Filter methods select features from a dataset independently for any machine learning algorithm. These methods rely only on the characteristics of these variables, so features are filtered out of the data before learning begins.
These methods are powerful and simple and help to quickly remove features— and they are generally the first step in any feature selection pipeline.
Continue reading Hands-on with Feature Selection Techniques: Filter Methods
Hello readers! I will be discussing some cool stuff here, today. So, just wear some black goggles and follow along.
Of the modern world’s technological advancements, I’d argue that, most elegant of them all, are the strides we’ve made in allowing computers to have the power of human-like perception.
Yes, This brilliant idea of training a computer so that it learns like a human, behaves like a human, acts like a human, always seemed distant. But now, through advancements in neural networks and computational power, the dream is real.
This article is all about variable discretization, which is the process of transforming a continuous variable into a discrete one. It essentially creates a set of contiguous intervals that span the variable’s value range.
Binning is another name for discretization, where the bin is an alternative name for the interval.
There are multiple approaches to achieve this discretization. In this guide, we’ll explore the following methods:
Continue reading Hands-on with Feature Engineering Techniques: Variable Discretization
This article is the final part of our series covering techniques for feature selection in machine learning models. Since this is the end, I’d recommend circling back and checking out the rest of the articles in the series:
This post will be covering several advanced techniques for feature selection.
Dimensionality reduction isn’t quite the same as feature selection, even though both try to reduce the number of features. While feature selection selects and excludes some features without making any transformation, dimensionality reduction transforms features into a lower dimension.
Continue reading Hands-on with Feature Selection Techniques: Advanced Methods
The steady rise of mobile Internet traffic has provoked a parallel increase in demand for on-device intelligence capabilities. However, the inherent scarcity of resources at the Edge means that satisfying this demand will require creative solutions to old problems. How do you run computationally expensive operations on a device that has limited processing capability without it turning into magma in your hand?
Continue reading How TensorFlow Lite Optimizes Neural Networks for Mobile Machine Learning
Are you looking for a deep learning library that’s one of the most popular and widely-used in this world? Do you want to use a GPU and highly-parallel computation for your machine learning model training? Then look no further than TensorFlow.
Created by the team at Google, TensorFlow is an open source library for numerical computation and machine learning. Undoubtedly, TensorFlow is one of the most popular deep learning libraries, and in recent weeks, Google released the full version of TensorFlow 2.0.
With recent advances in both iPhone camera and AI-accelerated chip technology (i.e. the A13 Bionic in the iPhone 11 series), it’s no wonder that some of the most innovative, transformative use cases for machine learning on iOS come in the form of computer vision.
In this tutorial, you’ll get an introduction to deep learning using the PyTorch framework, and by its conclusion, you’ll be comfortable applying it to your deep learning models. Facebook launched PyTorch 1.0 early this year with integrations for Google Cloud, AWS, and Azure Machine Learning. In this tutorial, I assume that you’re already familiar with Scikit-learn, Pandas, NumPy, and SciPy. These packages are important prerequisites for this tutorial.
Continue reading Deep Learning with PyTorch: An Introduction
The definition of an edge device can vary greatly from application to application, and it includes devices ranging from smartwatches to self-driving cars and everything in between. Currently, the edge devices with the largest numbers, which also have a connection to a network, is likely the smartphone.
There are increasingly a lot of other devices with small MCU’s (microcontrollers) that aren’t connected to any network which can be used for applications like intelligent sprinkler system for home garden.
In previous tutorials in this series, we began the development of an Android chat application that allows users to do the following:
Register and verify user email addresses; login to the system Fetch chat conversations from other users Send and receive messages within the conversationsContinue reading “Instant Notifications for New Messages in an Android Chat Application”
