Introduction Probability distributions are cornerstones in the theory of machine learning. The entire field of machine learning is often theoretically viewed or explained from a probabilistic perspective. Compared to the linear algebraic point of view, the probabilistic perspective gives formulations of machine learning algorithms that are more impressive. Binomial distribution is one of the many
Binomial Distribution Read More »
The Jupyter Notebook is best suited for doing as well as documenting empirical research. It can contain text, code images, videos, interactive content, and also nicely typesetting equations. This makes anyone doing simulations or empirical research prototype their work and share it with their colleagues. The same Jupyter notebook can be used to visualize protein
Visualizing Protein Structures in Jupyter Notebook Read More »
Frequentist and Bayesian are two schools of thought in probability theory. Let us look at the difference between them. Bayesian and frequentist approaches are two major frameworks in statistics for analyzing data and making statistical inference. The frequentist approach is based on the idea of probability as the long-term frequency of an event in repeated
Frequentist and Bayesian: A Quick Comparison Note Read More »
Graphical User Interface (GUI) has been useful and easy to use for all types of computer users. We all use it every day to get things done. However, the computing history has the provision to do command-line instructions also to do the same things. Linux terminal is one such command line interface. Let us take
Linux Terminal Basics Read More »
This article is a reflection and articulation about the paper titled “Fair Active Learning” authored by Hadis Anahideh and Abolfazi Asudeh. The research paper is available on Arxiv and I am reviewing version 2 of it. The paper deal with bias in data and unfairness in the context of Active Learning. Learning algorithms do their
Paper Note: Fair Active Learning Read More »
In STEM fields it is often required to communicate ideas using graphs or infographics. Many times the dimensionality of data will be higher and require clever hacks to represent so that the reader gets the idea quickly. There have been systematic studies on how to do data visualization effectively. For data scientists or machine learning
Python Matplotlib Tip: Overlapping Curves and Multiple Axes Read More »
The culture and growth of scientific research in various domains are interesting to observe. For many decades traditional scientific research has been accessible or done in academia or research institutes. Each decade or century witnessed various sciences rising to the limelight. Once it was physical sciences, then it was biological science, the age of computers,
Open Research In Machine Learning Read More »
Dream for a computer storage device which performs as fast as static RAM (SRAM), delivers the cost effectiveness of dynamic RAM (DRAM), promises the non-volatility of flash memory, combined with added benefits of longevity, denser storage, durability and stability. Let’s discuss whether spin transfer torque based Random access Memory (STT-RAM) devices can help to realize
Spin Transfer Torque based Magnetic Memory Devices Read More »
The field of information theory defines entropy as a measure of average information a random variable takes. As we know the random variable is one whose possible outcome states are not deterministic. We cannot predict its behavior exactly in an analytical form. In such cases, we can only look at the patterns of its outcome
Entropy Simplified Read More »
This article is a tutorial on the algorithm called active Learning with uncertainty Sampling. Introduction Availability of mass quantities of digital data and feasible computing power brought to the creation of learning algorithms. These learning algorithms have been benchmarked to perform specialized tasks such as classification, object detection, image segmentation, etc. The key assumption here
Active Learning with Uncertainty Sampling from Scratch Read More »