How to Develop Optimization Models in Python

From: https://towardsdatascience.com/how-to-develop-optimization-models-in-python-1a03ef72f5b4 Determining how to design and operate a system in the best way, under the given circumstances such as allocation of scarce resources, usually requires leveraging on quantitative methods in decision making. Mathematical optimization is one of the main approaches for deciding the best action for a given situation. It consists of maximizing or minimizing the … Continue reading How to Develop Optimization Models in Python

How to Develop 1D Convolutional Neural Network Models for Human Activity Recognition

From: https://machinelearningmastery.com/cnn-models-for-human-activity-recognition-time-series-classification/ Human activity recognition is the problem of classifying sequences of accelerometer data recorded by specialized harnesses or smart phones into known well-defined movements. Classical approaches to the problem involve hand crafting features from the time series data based on fixed-sized windows and training machine learning models, such as ensembles of decision trees. The … Continue reading How to Develop 1D Convolutional Neural Network Models for Human Activity Recognition

Image Classification of Uploaded Files Using Streamlit’s Killer New Feature

From: https://towardsdatascience.com/image-classification-of-uploaded-files-using-streamlits-killer-new-feature-7dd6aa35fe0 It’s no secret I’m a massive fan of Streamlit. I think it’s the best way to share early versions of Data Science projects. It’s especially great for demoing your project to a less technical audience. Before investing in frontend developers to build you a proper UI, Streamlit allows you to get some features … Continue reading Image Classification of Uploaded Files Using Streamlit’s Killer New Feature

Cellular Automaton and Deep Learning

From: https://towardsdatascience.com/cellular-automaton-and-deep-learning-2bf7c57139b3 Cellular Automatons (CA) are evolving grid-based computational systems which model complexity growth. In simpler words, cellular automatons are evolving patterns that are unidirectional and chaotic. The rules of evolution are very elementary and simple however, the evolution gives rise to highly complex structures and behaviour. CA have been proposed as models of how … Continue reading Cellular Automaton and Deep Learning

Reinforcement Learning in Honor of John Conway

From: https://wordpress.com/block-editor/post/scieencerepository.data.blog Philosopher’s Football is a board game invented by the late and legendary mathematician John Conway (another victim of the virus). In his honor, I built a website where you can play and learn more about the game at philosophers.football. You can find someone else to play with online or locally and you can play against an … Continue reading Reinforcement Learning in Honor of John Conway

Dimensionality reduction for image and texture set compression

From: https://bartwronski.com/2020/05/21/dimensionality-reduction-for-image-and-texture-set-compression/ Teaser image: Example PBR Texture set compressed with presented technique.Textures credit cc0textures, Lennart Demes. In this blog post I am going to describe some of my past investigations on reducing the number of channels in textures / texture sets automatically and generally – without assuming anything about texture contents other than correspondence to … Continue reading Dimensionality reduction for image and texture set compression

How Modern Game Theory is Influencing Multi-Agent Reinforcement Learning Systems

From: https://medium.com/@jrodthoughts/how-modern-game-theory-is-influencing-multi-agent-reinforcement-learning-systems-2a64a3ba0c2c Most artificial intelligence(AI) systems nowadays are based on a single agent tackling a task or, in the case of adversarial models, a couple of agents that compete against each other to improve the overall behavior of a system. However, many cognition problems in the real world are the result of knowledge built by … Continue reading How Modern Game Theory is Influencing Multi-Agent Reinforcement Learning Systems

When Bayes, Ockham, and Shannon come together to define machine learning

From: https://towardsdatascience.com/when-bayes-ockham-and-shannon-come-together-to-define-machine-learning-96422729a1ad Acknowledgments Thanks to my CS7641 class at Georgia Tech in my MS Analytics program, where I discovered this concept and was inspired to write about it. Thanks to Matthew Mayo for editing and re-publishing this in KDnuggets. Introduction It is somewhat surprising that among all the high-flying buzzwords of machine learning, we don’t hear much about the one phrase … Continue reading When Bayes, Ockham, and Shannon come together to define machine learning

Performance Optimization in R: Parallel Computing and Rcpp

From: https://tutorial.guidotti.dev/pa78y/ The ‘parallel’ package Reference: https://bookdown.org/rdpeng/rprogdatascience/parallel-computation.html Many computations in R can be made faster by the use of parallel computation. Generally, parallel computation is the simultaneous execution of different pieces of a larger computation across multiple computing processors or cores. The parallel package can be used to send tasks (encoded as function calls) to each of the … Continue reading Performance Optimization in R: Parallel Computing and Rcpp

Dockerize Jupyter with the Visual Debugger

From: https://towardsdatascience.com/dockerize-jupyter-with-official-visual-debugger-enabled-cbce1840b7f Jupyter recently announced its first-ever public release of the much-awaited visual debugger. Though it is the first release it supports all the basic debugging requirements needed to debug and inspect variables, etc. The Data Science community is relied heavily on Jupyter Notebooks due to its ability to easily communicate and share the outcomes … Continue reading Dockerize Jupyter with the Visual Debugger