Using SAGEConv in PyTorch Geometric module for embedding graphs
Graph representation learning/embedding is commonly the term used for the process where we transform a Graph data structure to a more structured vector form. This enables the downstream analysis by providing more manageable fixed-length vectors. Ideally, these vectors should incorporate both graph structure (topological) information apart from the node features…
Manipulation, Utility and Advantages of Graph Neural Networks
Graph neural networks (GNN) are gaining popularity due to the ubiquitous nature of the Graph Data structure. Graphs enable us to model many different problems in science in fields such as (but not limited to) biology, sociology, ecology, vision, education, economics, etc. …
Introduction to variational autoencoders and their application Bioinformatics/Metagenomics analysis
Autoencoders, in general, intend to learn a lower-dimensional representation of data. One of the main advantages of autoencoders is that they are capable of learning much complex lower dimensions whereas PCA-like decompositions are limited by their linear nature. Feel free to have a look at my article about auto-encoders.
As…
An in-depth exploration of sequence vectorization and applications
In one of my previous articles, I mentioned how and what is vectorization. In gist, the process enables us to convert variable-length nucleotide sequences into fixed-length numeric vectors.
For all the programming lovers, the link is below;
However, in this article, I will rather focus on utility and research importance…
Super-fast and parallel file writing with memory mapped IO
What is a Memory Mapped File?
A memory mapped file is a segment in the virtual memory. Virtual memory is an abstraction on physical memory which is provided to the process by the operating system (OS). Should the OS run out of memory or see a process idling for long, such virtual memory segments will be…
A tool to generate annotated images for computer vision
In computer vision, object detection is an interesting field with numerous application. Like any other supervised machine learning task, we need annotation or labels for this very task as well. However, annotation of images can be a tedious time-consuming task for many of us (lazy ones?). …
Object detection with AIoT: An edge computing example
A few weeks back when I was window shopping at AliExpress I came across the wonderful Maixduino device. It was claimed to carry RISC V architecture along with a KPU (KPU is a general-purpose neural network processor). Contrasting specs of the board were as follows;
- CPU: RISC-V Dual Core 64bit…
Building an MQTT enabled door sensor using ESP32 and PlatformIO
This article is motivated by my previous article on ESPHome. Previously, we built a door sensor using ESPHome library. However, using an off-the-shelf platform grants us with little to no flexibility when it comes to micro-optimizations. …
Create simple home automation artefacts using ESP modules and HomeAssistant
Home automation can be expensive and complex. However, with the arrival of IoT platforms from vendors such as Tuya, Phillips, etc have made home automation a little simpler. However, the cost still remains a little higher and the customisations are far limited. Last but not least, none of the mentioned…
Build your own Smart TV with the help of a Raspberry Pi Zero device
Are you bored with your ordinary TV? Do you want to expand your media experience to a whole new level? Yet you are limited with the budget? Like me? Well, this would be the perfect Smart TV solution!
Disclaimer: This tutorial might not be complete and can contain differences. Google…
