​Described in this article are two example python functions to handle the post-training dynamic range quantization in Tensorflow. Finally, there is a prototype example inference API to help make quantized model deployments easier by making them plug-in-play with the Tensorflow Keras inference API.

The first function describes the conversion of the floating point model to a dynamic range quantized model and the second function describes the inference steps using the dynamic range quantized model.

​This helps reduce the memory footprint and the inference time for a model. It can achieve 4x reduction in model size, along with 2x to 3x speed-up of the inference performance. 

​Python generator is a stateful function that can return an intermediate result ("the next value”) to its caller. Since a Python generator maintains the function’s local state, this allows resuming the function from right where it is left off. 

In addition to the general purpose container datatypes: dict, list, set and tuple; Python also implements specialized container datatypes. One of the commonly used specialized container datatype in Python is the 'named tuple'. 

​A 'named tuple' can be created in Python using the collections module factory function namedtuple(). This function returns a tuple like object whose elements can be accessed using the corresponding attribute name. Hence the name of this tuple like object: 'named tuple'.

The attribute names or field_names for the 'named tuple' can be either a sequence of strings (Eg. ['x', 'y']) or a single string with each fieldname separated by whitespace and/or commas (Eg. 'x y' or 'x, y').

Of note here, while assigning a fieldname; they should not start with a digit or an underscore. However, both digits and underscores are allowed elsewhere in the fieldname.

A common application for the 'named tuple' is to create more readable vectors such as those representing the Cartesian coordinate space or for representing the latitude-longitude data from a GPS receiver.

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.