With recent developments in deep learning, neural networks are getting larger and larger. For example, in the ImageNet recognition challenge, the winning model, from 2012 to 2015, increased in size by 16 times. And in just one year, for Baidu’s Deep Speech model, the number of training operations increased by 10 times.
Continue reading The 5 Algorithms for Efficient Deep Learning Inference on Small Devices
This will be a practical, end-to-end guide on how to build a mobile application using TensorFlow Lite that classifies images from a dataset for your projects.
This application uses live camera and classifies objects instantly. The TFLite application will be smaller, faster, and more accurate than an application made using TensorFlow Mobile, because TFLite is made specifically to run neural nets on mobile platforms.
Continue reading Neural Networks on Mobile Devices with TensorFlow Lite: A Tutorial
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
As Computer Vision represents a relative understanding of visual environments and their contexts, many scientists believe the field paves the way towards Artificial General Intelligence due to its cross-domain mastery.
In this article, I want to share the 5 major computer vision techniques I’ve learned as well as major deep learning models and applications using each of them.
Continue reading 5 Computer Vision Techniques That Will Change How You See The World
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.
