Detecting Heart Arrhythmias with Deep Learning in Keras with Dense, CNN, and LSTM

From: https://towardsdatascience.com/detecting-heart-arrhythmias-with-deep-learning-in-keras-with-dense-cnn-and-lstm-add337d9e41f Let’s detect abnormal heart beats from a single ECG signal From: https://towardsdatascience.com/detecting-heart-arrhythmias-with-deep-learning-in-keras-with-dense-cnn-and-lstm-add337d9e41f Introduction Recently, I was reviewing Andrew Ng’s team’s work(https://stanfordmlgroup.github.io/projects/ecg/) on heart arrhythmia detector with convolutional neural networks (CNN). I found this quite fascinating especially with the emergence of wearable products (e.g. Apple Watch and portable EKG machines) that are capable of … Continue reading Detecting Heart Arrhythmias with Deep Learning in Keras with Dense, CNN, and LSTM →

DataOps is NOT Just DevOps for Data

From: https://medium.com/data-ops/dataops-is-not-just-devops-for-data-6e03083157b7 Figure 1: DevOps is often depicted as an infinite loop, while DataOps is illustrated as intersecting Value and Innovation Pipelines One common misconception about DataOps is that it is just DevOps applied to data analytics. While a little semantically misleading, the name “DataOps” has one positive attribute. It communicates that data analytics can achieve what software development attained … Continue reading DataOps is NOT Just DevOps for Data →

Everything a Data Scientist Should Know About Data Management*

(*But Was Afraid to Ask) From: https://towardsdatascience.com/everything-a-data-scientist-should-know-about-data-management-6877788c6a42 NIST Big Data Taxonomy (Source: WikiCommons) To be a real “full-stack” data scientist, or what many bloggers and employers call a “unicorn,” you’ve to master every step of the data science process — all the way from storing your data, to putting your finished product (typically a predictive model) … Continue reading Everything a Data Scientist Should Know About Data Management* →

Applied Topological Data Analysis to Deep Learning? Hands-on Arrhythmia Classification!

From: https://towardsdatascience.com/applied-topological-data-analysis-to-deep-learning-hands-on-arrhythmia-classification-48993d78f9e6 Healthcare is an exciting world to be working in. Every controlled performance enhancement somewhat means saving or improving lives. As a consequence, good enough generalization is not something you can get complacent about. Now comes the question of how to do it. Some do enhance their inference by augmenting the size of their … Continue reading Applied Topological Data Analysis to Deep Learning? Hands-on Arrhythmia Classification! →

An Easy Guide to Gauge Equivariant Convolutional Networks

From: https://towardsdatascience.com/an-easy-guide-to-gauge-equivariant-convolutional-networks-9366fb600b70 Geometric deep learning is a very exciting new field, but its mathematics is slowly drifting into the territory of algebraic topology and theoretical physics. This is especially true for the paper “Gauge Equivariant Convolutional Networks and the Icosahedral CNN” by Cohen et. al.(https://arxiv.org/abs/1902.04615), which I want to explore in this article. The paper uses … Continue reading An Easy Guide to Gauge Equivariant Convolutional Networks →

Deploying your first Deep Learning Model: MNIST in production environment

From: https://towardsdatascience.com/deploying-your-first-deep-learning-model-mnist-in-production-environment-510bfdc4808d How you can deploy your MNIST model in production environment MNIST Dataset is a hello world dataset for most of the ML Enthusiast likes us. At some point everyone who has started their journey in this field or willing to start will come across this dataset and get their hands on for sure. It … Continue reading Deploying your first Deep Learning Model: MNIST in production environment →

15 Open Datasets for Healthcare

From: https://medium.com/@ODSC/15-open-datasets-for-healthcare-830b19980d9 Machine Learning is exploding into the world of healthcare. When we talk about the ways ML will revolutionize certain fields, healthcare is always one of the top areas seeing huge strides, thanks to the processing and learning power of machines. There’s a good chance you either are or will soon be employed in … Continue reading 15 Open Datasets for Healthcare →

Create a Multipage Dash Application

From: https://towardsdatascience.com/create-a-multipage-dash-application-eceac464de91 Making a dashboard is a great way to communicate the value of data science projects to coders and non-coders alike! For those of us that work in data science, often times our skills with data come with a lack of skill in front end development. Dash, a library built by Plotly, offers simple boiler … Continue reading Create a Multipage Dash Application →

Simulating data with Bayesian networks

From: http://gradientdescending.com/simulating-data-with-bayesian-networks/ Bayesian networks are really useful for many applications and one of those is to simulate new data. Bayes nets represent data as a probabilistic graph and from this structure it is then easy to simulate new data. This post will demonstrate how to do this with bnlearn. Fit a Bayesian network Before simulating new … Continue reading Simulating data with Bayesian networks →

Visualizing the Emotional Arcs of Movie Scripts Using Rule-Based Sentiment Analysis

From: https://towardsdatascience.com/visualizing-the-emotional-arcs-of-movie-scripts-using-rule-based-sentiment-analysis-1016b4b1af5a How I used Python, D3 and Flask to create this interactive visualization Almost 72 years ago, acclaimed American writer Kurt Vonnegut came up with a novel method for graphing the plot lines of stories as part of his master’s thesis in anthropology. Although his work was ultimately rejected by the University of Chicago “because it was … Continue reading Visualizing the Emotional Arcs of Movie Scripts Using Rule-Based Sentiment Analysis →