Category: Articles

Imagine you have trained an image classification model whose performance seems a bit poor—did you know there’s more you can do to improve such a model and reduce its bias? You’ve done a lot in creating your model pipeline and then building a predictive model using neural networks, yet its not improving as you expected

Continue reading Overcoming overfitting in image classification using data augmentation

Deep learning and unsupervised feature learning have shown great promise in many practical applications. State-of-the-art performance has been reported in several domains, ranging from speech recognition and image recognition to text processing and beyond.

It’s also been observed that increasing the scale of deep learning—with respect to numbers of training examples, model parameters, or both—can drastically improve accuracy. These results have led to a surge of interest in scaling up the training and inference algorithms used for these models and in improving optimization techniques for both.

Continue reading The 4 Research Techniques to Train Deep Neural Network Models More Efficiently

This tutorial documents a GitHub project called GARI (Genetic Algorithm for Reproducing Images). The project is available here:

Before discussing the details of the project, let’s run through a quick overview of it.

The GARI project accepts an image as input. This image can have one or more channels (i.e. the image could be binary, gray, or color, such as RGB). RGB is the most popular color model that produces any color as a combination of the 3 color channels Red, Green, and Blue. Hence its abbreviation.

Continue reading Reproducing Images using a Genetic Algorithm with Python

Powering applications with the ability to understand the natural language of the text always amazes me. Apple made some significant strides with its Natural Language framework last year (2019). Specifically, the introduction of a built-in sentiment analysis feature can only help build smarter NLP-based iOS Applications.

Besides the improvements to the Natural Language framework, SwiftUI and Combine were the two biggies that were introduced during WWDC 2019.

Continue reading Sentiment Analysis on iOS Using SwiftUI, Natural Language, and Combine: Hacker News Top Stories

In this article, we’ll be learning the following:

Object detection can be defined as a branch of computer vision which deals with the localization and the identification of an object. Object localization and identification are two different tasks that are put together to achieve this singular goal of object detection.

Object localization deals with specifying the location of an object in an image or a video stream, while object identification deals with assigning the object to a specific label, class, or description. With computer vision, developers can flexibly do things like embed surveillance tracking systems for security enhancement, real-time crop prediction, real-time disease identification/ tracking in the human cells, etc.

Continue reading Real-time Object Detection using SSD MobileNet V2 on Video Streams

Managing user authentication flows in your mobile apps is often a fundamental requirement to allow only authorized users to access data. The react-navigation library in its latest version (version 5) allows you to implement a custom authentication flow in React Native apps.

In this tutorial, we’ll discuss one of the strategies to implement an authentication flow using react-navigation library, and react-native-firebase.

Continue reading How to manage authentication flows in React Native with react-navigation v5 and Firebase

A random forest is an ensemble model that consists of many decision trees. Predictions are made by averaging the predictions of each decision tree. Or, to extend the analogy—much like a forest is a collection of trees, the random forest model is also a collection of decision tree models. This makes random forests a strong modeling technique that’s much more powerful than a single decision tree.

Continue reading Random Forest Regression in Python Using Scikit-Learn