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
Neural network implementation became a lot easier since the advent of transfer learning in accessible libraries. So much so that deep learning code that previously required hours to write can be written today in just 2 lines — No kidding !
Let me demonstrate what transfer learning is through the most basic example — our daily lives.
Remember when you last purchased a new phone — and didn’t even have to spend a day learning how it works ? As humans, we learn based on previous experiences. When we transitioned to our second smartphone, we already had mental and behavioral models to bring us up-to-date with the new device. This is what is known as transfer learning in the domains of data science and machine learning . So, what is this transfer learning we practice so much and know so little about?
Continue reading Image Segmentation with Transfer Learning [PyTorch]
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.
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
Detecting objects, classifying them, and then keeping track of them is the biggest advancement in the deep learning area. There are many use cases of object detection in our day-to-day life; some examples are judging human behavior, activity trackers, some use in public sectors, crime detection, and many more.
In this article, I will help you deploy your own custom object detection based recommendation system to the web. In the end, you can interact with your real-time object detection and recommendation model using your web interface. Finally, you can host it over the cloud to access it everywhere, even with your mobile devices.
Continue reading Modeling & Deploying Object Detection Based Recommenders to the Web
