By: Ajay Arunachalam — Senior Data Scientist & Researcher (AI)
Hello, friends. I hope this will be a nice reading experience for you. I will try to keep this post limited & more focused on practical and applied demonstration rather than just the theoretical explanations. Through this blog post, we will go through the State-Of-The-Art (SOTA) model — “transformer”. First we will go through the transformer model basics quickly to give you a brief overview. We will conclude with a hands-on demo example covering a practical computer vision problem by implementing a vision transformer network architecture.
Meta-Self-Ensemble Learner Package (pip install meta-self-learner) — https://github.com/ajayarunachalam/meta-self-learner
Hello, friends. In this blog post, a meta-learner ensemble design is presented. The meta-ensemble learning model aims to fit any complex data better, lowering the uncertainty in estimation. The two self-learner algorithms aim to find the optimal weights that minimize the objective function.
USP of this package:-
“Meta-Self-Learn” provides several ensemble learners functionality for quick predictive modeling prototyping. Generally, the predictions…
An interpretable prototype of unsupervised deep convolutional neural network & lstm autoencoders based real-time anomaly detection from high-dimensional heterogeneous/homogeneous time series multi-sensor data
What’s new in MSDA v1.10.0?
MSDA is an open source
low-code Multi-Sensor Data Analysis library in Python that aims to reduce the hypothesis to insights cycle time in a time-series multi-sensor data analysis & experiments. It enables users to perform end-to-end proof-of-concept experiments quickly and efficiently. The module identifies…
Hello, friends. In this blog post, I will take you through an use case application scenario of the algorithms with my package “msda” for the time-series sensor data. More details can be found here & refer to my previous blog post here
Here, we will see an example of unsupervised feature selection from time-series raw sensor data with my developed algorithms in the package MSDA, and further I also compare it with other well-known unsupervised techniques like PCA & IPCA.
MSDA is an open-source multidimensional multi-sensor data analysis framework, written in Python.
Hello, friends. In this blog post, I will take you through my package “msda” useful for time-series sensor data analysis. A quick introduction about time-series data is also provided. The demo notebook can be found on here
One of the specific use case applications focused on “Unsupervised Feature Selection” using the package can be found in the blog post here.
Time series data is information taken at a particular duration. For instance, having a set of sensor data observed at particular equal paces, each sensor can be classified as time series. …
I always believe in democratizing AI and machine learning, and spreading the knowledge in such a way to cater the larger audiences in general to potentially exploit the power of AI.
One such attempt is the development of the R package for meta-level ensemble learning (Classification, Regression) that is fully-automated. It significantly lowers the barrier for the practitioners to apply heterogeneous ensemble learning techniques in an amateur fashion to their everyday predictive problems.
Before we dwell into the package details, let’s understand a few basic concepts.
Why Ensemble Learning?
Generally, predictions become unreliable when the input sample is out of…
I always believe in democratizing AI and machine learning, and spreading the knowledge in such a way, to cater the larger audiences in general, to harness the power of AI. An attempt inline to this is the development of the python package “regressormetricgraphplot” that is aimed to help users plot the evaluation metric graph with single line code for different widely used regression model metrics comparing them at a glance. …