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

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