Articles
Unlocking my apartment with my face
As part of a previous project, I installed an IoT lock on my front door, which allowed me to control it using my smartphone. However, having to take out my smartphone every time I want to unlock the door quickly becomes annoying. To improve on the situation, I installed an RFID reader on the lock, which I used in combination with an RFID ring which now serves as my key. To follow up on this trend of finding new ways to unlock my door, I decided to explore face recognition technologies.
Deployment of a TensorFlow model to Kubernetes
Let’s imagine that you’ve just finished training your new TensorFlow model and want to start using it in your application(s). One obvious way to do so is to simply import it in the source code of every application that uses it. However, it might be more versatile to keep your model in one place as standalone and simply have applications exchange data with it through API calls. This article will go through the steps of building such a system and deploy the result to Kubernetes.
Tensorflow 1.X basics
Here are the basics steps necessary to write TensorFlow code.
Vanilla javascript neural network
This simple web app written in JavaScript which trains a neural network on data provided as .csv file. The neural network is a fully connected network implemented from scratch and the structure of its hidden layers can be set by the user. The dimension of its inputs and outputs is automatically adjusted to fit the provided training data.
Serving a Keras model using Tensorflow serving and Docker
A Keras model can be created in various ways, for example using the <a href="https://keras.io/getting-started/sequential-model-guide/" rel="noopener noreferrer nofollow">sequential model</a>:
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