Q-learning is one of the simplest algorithms to try reinforcement learning. Reinforcement learning, as the name suggests, focuses on learning (by an agent) in a reinforced environment. The agent performs an action, analyses the outcome, and gets a reward. The agent then learns to interact with its environment by taking into consideration the rewards which it will get by performing specific actions in a particular state.
Continue reading Q-Learning With The Frozen Lake Environment In Android
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.
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.
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
Mobile developers have a lot to gain from revolutionary changes that on-device machine learning can offer. This is because of the technology’s ability to bolster mobile applications—namely, allowing for smoother user experiences capable of leveraging powerful features, such as providing accurate location-based recommendations or instantaneously detecting plant diseases.
Continue reading Machine Learning and the Future of Mobile App Development
As a university student, I (and many of my peers) think a lot about exams. Sometimes I catch myself thinking: The human brain works 24/7 for 365 days, right from our birth, until…we step into the examination hall 🙂
How many times in your life you have wondered whether you will pass or fail an exam of some kind? Maybe you know more about yourself, your habits and tendencies, etc., so you can more accurately predict your result. But how many times you have wondered whether a friend of yours will pass the same exam or not?
Continue reading Logistic Regression in Python Using Scikit-learn
TensorFlow is a well-established player in the deep learning framework field. First, it works pretty damn well. Second, it’s a Google product that’s easily found throughout their own ecosystem (including Android, Chrome, and a slew of apps). Lastly, the TensorFlow team been very respectful of and responsive to both the researcher community as well as the commercial/production community.
