Category: Articles

In this post, we’re going to investigate the field of image compression and it’s applications in real world. We’ll explore various machine and deep learning techniques for image compression and inspect their pros and cons, and their practical feasibility in real-world scenarios.

So let’s get started!

Image compression refer to reducing the dimensions, pixels, or color components of an image so as to reduce the cost of storing or performing operations on them. Some image compression techniques also identify the most significant components of an image and discard the rest, resulting in data compression as well.

Continue reading Image Compression Using Different Machine Learning Techniques

Google Colab is a cloud-based service that allows the execution of Python code and includes the ability to use and install new libraries. Put simply, you can leverage Google Colab to create a Jupyter Notebook and execute Python code using either a CPU or a free GPU.

Continue reading Getting Started with Google Colab Notebooks

Recently, there has been a huge rise in the implementation of artificial intelligence solutions, with new deep learning architectures being built and deployed across various industries. This rise could be attributed to two important factors:

Deep learning works primarily because of the vast amount of input data on which the deep neural net is trained. Hence, having a good labeled training dataset marks the first step in developing a highly accurate AI solution.

Continue reading Image Manipulation for Machine Learning in R

There are over 5 billion mobile device users all over the world. Such users generate massive amounts of data—via cameras, microphones, and other sensors like accelerometers—which can, in turn, be used for building intelligent applications. Such data is then collected in data centers for training machine/deep learning models in order to build intelligent applications.

However, due to data privacy concerns and bandwidth limitations, common centralized learning techniques aren’t appropriate—users are much less likely to share data, and thus the data will be only available on the devices.

Continue reading Introduction to Federated Learning

Performance is the most important parameter in a mobile application—if it’s slow and/or buggy, then on the whole, it’s likely to be rejected by users. Then there’s a competitive factor. The better your app performs, the better the chances are for your app in the market.

Personally, I’d spend a couple of extra dollars for significantly better performance. Someone wise once said time is money, so let’s cut to the chase and look at how developers can optimize performance on Android.

Continue reading Increasing performance in an Android application

Object detection is a technology related to computer vision and image processing that deals with detecting and locating instances of semantic objects of a certain class (such as humans, buildings, or cars) in digital images and videos.

In this post, we’ll briefly discuss feature descriptors, and specifically Histogram of Oriented Gradients (HOG). We’ll also provide an overview of deep learning approaches to about object detection, including Region-based Convolutional Neural Networks (RCNN) and YOLO(you only look once).

Continue reading Introduction to basic object detection algorithms

Are you looking for a computationally cheap, easy-to-explain linear estimator that’s based on simple mathematics? Look no further than OLS!

OLS stands for ordinary least squares. OLS is heavily used in econometrics—a branch of economics where statistical methods are used to find the insights in economic data.

As we know, the simplest linear regression algorithm assumes that the relationship between an independent variable (x) and dependent variable (y) is of the following form: y = mx + c, which is the equation of a line.

Continue reading Implementing ordinary least squares (OLS) using Statsmodels in Python

The open-source library react-native-gesture-handler is a great way to add gestures to cross-platform React Native apps. Two of the main reasons I find the library useful are because it uses native support to handle gestures, and it performs better on each native platform than React Native’s built-in touch system Gesture Responder system.

Continue reading How to Use the Pinch to Zoom Gesture in React Native Apps

As an exercise, or even to solve a relatively simple problem, many of you may have implemented linear regression with one feature and one target. However, in the real world, most machine learning problems require that you work with more than one feature.

For example, to calculate an individual’s home loan eligibility, we not only need his age but also his credit rating and other features.

Continue reading Implementing Multiple Linear Regression Using sklearn