Reduce Dimensions for Single Cell

From: https://towardsdatascience.com/reduce-dimensions-for-single-cell-4224778a2d67 From Becht et al., Nature Biotechnology 2019, image source This is the eighth article in the column Mathematical Statistics and Machine Learning for Life Sciences where I try to cover analytical techniques common for Bioinformatics, Biomedicine, Genetics, Evolutionary Science etc. Today we are going to talk about dimension reduction techniques applied to single cell genomics data. Among others, we are … Continue reading Reduce Dimensions for Single Cell →

Physical Activity Monitoring Using Smartphone Sensors and Machine Learning

From: https://towardsdatascience.com/physical-activity-monitoring-using-smartphone-sensors-and-machine-learning-93f51f4e744a Sitting: One of the Most Dangerous Activities Sedentary behavior has become a major public health risk around the world. Experts tell us that a minimum amount of daily physical activity (PA) is necessary to maintain health and reduce the risk of chronic diseases such as diabetes, heart disease, and cancer. Some researchers have … Continue reading Physical Activity Monitoring Using Smartphone Sensors and Machine Learning →

Interactive and scalable dashboards with Vaex and Dash

From: https://medium.com/plotly/interactive-and-scalable-dashboards-with-vaex-and-dash-9b104b2dc9f0 The thing about dashboards… Creating dashboards is often an integral part of data science projects. Dashboards are all about communication: be it sharing the findings of data science teams with the executives, monitoring key business metrics, or tracking the performance of a model in production. Most importantly, a good dashboard should present meaningful … Continue reading Interactive and scalable dashboards with Vaex and Dash →

Graph Laplacian and its application in Machine learning

From: https://towardsdatascience.com/graph-laplacian-and-its-application-in-machine-learning-7d9aab021d16 This article highlights graphs, properties of its representations and its application in Machine learning to perform Spectral clustering. Introduction A graph is a data structure with nodes connected to each other through directed or undirected edges. The edges can have weights to represent for eg. the distance between 2 cities for a graph … Continue reading Graph Laplacian and its application in Machine learning →

Cluster-then-predict for classification tasks

From: https://towardsdatascience.com/cluster-then-predict-for-classification-tasks-142fdfdc87d6 K-means from https://scikit-learn.org/stable/auto_examples/cluster/plot_kmeans_digits.html Introduction Supervised classification problems require a dataset with (a) a categorical dependent variable (the “target variable”) and (b) a set of independent variables (“features”) which may (or may not!) be useful in predicting the class. The modeling task is to learn a function mapping features and their values to a target … Continue reading Cluster-then-predict for classification tasks →

Embed Interactive Plots in Your Slides with Plotly

From: https://towardsdatascience.com/embed-interactive-plots-in-your-slides-with-plotly-fde92a5865a Effective communication is essential for us data scientists, and Plotly’s interactive plots are a great tool for that. But when it comes to presenting our work in a traditional slide-styled presentation, those plots are hard to integrate in our daily tools like PowerPoint or Google Slides. In this post, we’ll get to know … Continue reading Embed Interactive Plots in Your Slides with Plotly →

Hidden Markov Model- A Statespace Probabilistic Forecasting Approach in Quantitative Finance

From: https://medium.com/analytics-vidhya/hidden-markov-model-a-statespace-probabilistic-forecasting-approach-in-quantitative-finance-df308e259856 Hidden Markov Models (HMM) are proven for their ability to predict and analyze time-based phenomena and this makes them quite useful in financial market prediction. HMM can be considered mix of Brownian movements consisting of hidden layers and observed layers and comprising of sequence of events. In quantitative finance, the states of a system can … Continue reading Hidden Markov Model- A Statespace Probabilistic Forecasting Approach in Quantitative Finance →

Genetic Algorithms

From: https://towardsdatascience.com/genetic-algorithms-terminology-implementation-with-a-base-10-genotype-in-python-and-a-f9b290cf86d4 In this article, I cover the terminology needed to get acquainted with genetic algorithms, walk through an example where we would like a high-precision estimator of the optima for a single-objective optimization problem, then discuss the importance of parameter tuning and the forces that drive the evolution of a genetic algorithm’s population. Genetic … Continue reading Genetic Algorithms →

Biological Inspiration of Convolutional Neural Network (CNN)

From: https://medium.com/@gopalkalpande/biological-inspiration-of-convolutional-neural-network-cnn-9419668898ac Do you have the question how the CNN is related to Human Visual System or Brain? If YES, then here is my try to answer it according to my study, understanding and mapping of concepts. In this blog I will try to connect human brain and the layers of CNN as Edge detection, … Continue reading Biological Inspiration of Convolutional Neural Network (CNN) →

The Fascinating No-Gradient Approach to Neural Net Optimization

From: https://towardsdatascience.com/the-fascinating-no-gradient-approach-to-neural-net-optimization-abb287f88c97 Gradient descent is one of the most important ideas in machine learning: given some cost function to minimize, the algorithm iteratively takes steps of the greatest downward slope, theoretically landing in a minima after a sufficient number of iterations. First discovered by Cauchy in 1847 but expanded upon in Haskell Curry for non-linear … Continue reading The Fascinating No-Gradient Approach to Neural Net Optimization →