Discussed in this article are the steps to generate an endpoint server certificate using the intermediate certificate authority (CA) certificate.

Please feel free to check out the part 1 of the introduction to OpenSSL series where steps to generate a root CA certificate are described. In part 2 of the series, the steps to generate the intermediate CA certificate are described.

Also, please note that the passwords and the method to pass them to OpenSSL used here are for educational purposes alone. They are very weak and are visible using tools such as ```ps```.

It is important to update the passwords as well as the technique used for passing them accordingly, in production.

In this second part of the article series on using the OpenSSL library in linux, described here is the process of creating an intermediate certificate authority (CA) certificate for a self-signed certificate generation workflow.

Please note that the passwords and the method to pass them to OpenSSL used here are for educational purposes alone. They are very weak and are visible using tools such as ```ps```.

In production, please remember to update the passwords as well as the technique used for passing them accordingly.

Feel free to revisit the first part of this article series on creating a root CA certificate by following the link here. 

In this article, the OpenSSL library in linux is used to create a root certificate authority (CA) certificate for a self-signed certificate generation workflow.

Please note that the passwords and the method to pass them to OpenSSL used here are for educational purposes alone. They are very weak and are visible using tools such as ```ps```.

In production, it is very critical to set strong passwords as well as modify the technique used for passing them accordingly.

Described in this article here, is a novel deep convolutional generative neural network called the Deep-Satellite.  It was developed to predict the changes in cropland satellite imagery data for Sri Lanka. The goal of the Deep-Satellite model was to forecast the one time-step ahead satellite radar data.

Described in this article are the concepts behind the distance and similarity measures using trigonometric functions.  Specifically, the computation of cosine similarity, cosine distance, angular similarity and angular distance, using Python.

A quick overview on handling inverse trigonometric functions in Python using:

• Math
• Numpy
• Tensorflow

Tracking the versions of various machine learning libraries used in a particular machine learning project is important. For example, this version tracking helps understand and quantify the associated model drift.

Described in this post is a simple Python script to track the versions of commonly used machine learning libraries.

This code notebook describes the use of tensor slicing to build higher dimensional neural networks.

In a typical deep neural network architecture designed to handle image data, the input data is represented as three dimensional (3D) tensors.

The information represented in these tensors are the pixel values and the color channel information.

An example implementation of duplicate file detection using Python. This could be used as the backbone for a de-duplicated file system.

Described in this article is a simple deep convolutional network (CNN), with a state-of-the-art EfficientNet backbone and an attention attention mechanism, to classify the severity of diabetic retinopathy in retinal images. The model can be trained using the publicly available diabetic retinopathy dataset on Kaggle.