Author: Fritz

Have you always wanted to make a game but don’t know where to start? Or are you just interested in game technology?

In this article series, we’ll demonstrate how to use Unreal Engine 4 to make a side-scroller game and package it for Android. This game will have various elements like collecting pickups/coins, a health system, and also enemy AI. If you’ve played Mario in past then you’ll love it. We’ll try to build our game along those lines only— even if you don’t have any prior programming experience.

Continue reading Creating a side-scroller mobile game with Unreal Engine 4—with no prior experience

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.

Continue reading How to Track People Using Deep Learning

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.

Continue reading Linear Regression using TensorFlow 2.0

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.

Continue reading Machine Learning at the Edge — μML