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Towards Data Science 10/22/2019 00:20
The purpose of this article is to guide through a set up process of an Apache Druid Cluster using GCP. Apache Druid (incubating). Druid is an open-source analytics data store designed for business intelligence () queries on event data. Druid provides low latency (real-time) data ingestion, flexible data exploration, and fast data aggregation. A Scalable Architecture. Requirements. For Basic Druid Cluster: 8vCPUS, 30GB RAM and 200GB disk size (E.G. custom-6–30720 or n1-standard-8) per node. Have a Google Cloud Storage enable. Have a MySQL instance or Cloud SQL active. Setting up. Log in with your GCP account and create three virtual machines with SO Debian. Donwload druid:. run for each node. wget https://www-us.apache.org/dist/incubator/druid/0.15.1.
Towards Data Science 10/22/2019 00:18
This is a series where I will provide real-world examples where mathematics was used to make more intelligent decisions. Illustration by Garry Killian. Dealing with uncertainty. We live in a dynamic world where events are influencing each other. Most real-world variables are stochastic and there is an uncertainty associated with the outcome of the variable. If this variable is something that will severely affect your business you would want to know anything possible about that liability. The company I work for is providing markets for trade of electricity in Europe. The bulk of trades go into day-ahead contracts where an obligation is struck the day before financial settlement. Since we are clearing large volumes of trades every day we need to.
Towards Data Science 10/22/2019 00:11
Looking at some of the shortcomings of the BERT language model. If you are interested in NLP, I have no doubt you have read extensively about BERT. by a group of researchers from Google, it has been remarkable seeing the performance of this new language model and thinking about its potential. It has for several language tasks and there is no shortage of tutorials here on Medium that offer insights into many language modeling projects that can be accomplished with BERT. It has been a fascinating year for the field, to say the least with developments like this. In the midst of all its praises though, I have been interested in where BERT may not hit the mark. There is a fascinating paper by Allyson Ettinger called that tests some of those lingu.
Towards Data Science 10/22/2019 00:02
k-Means Clustering — an Unsupervised Machine Learning Algorithm. This is an image and text article. To Access the Jupyter Notebook —. Introduction. Background. New York City is the most populous city in the United States, home to the headquarters of the United Nations and an important center for international diplomacy. It just might be the most diverse city on the planet, as it is home to over 8.6 million people and over 800 languages. As quoted in an article — “Traditional cuisine is passed down from one generation to the next. It also operates as an expression of cultural identity. Immigrants bring the food of their countries with them wherever they go and cooking traditional food is a way of preserving their culture when they move to new pl.
Towards Data Science 10/21/2019 23:52
The process that’s used to detect breast cancer is time consuming and small malignant areas can be missed. In order to detect cancer, a tissue section is put on a glass slide. A pathologist then examines this slide under a microscope visually scanning large regions, where there’s no cancer in order to ultimately find malignant areas. Because these glass slides can now be digitized, computer vision can be used to speed up pathologist’s workflow and provide diagnosis support. Some terminology. Histopathology This involves examining glass tissue slides under a microscope to see if disease is present. In this case, that would be examining tissue samples from lymph nodes in order to detect breast cancer. Whole Slide Image (WSI) A digitized high r.
Towards Data Science 10/21/2019 23:24
Photo by on. Have you ever wondered what is hidden behind the ‘artificial intelligence’ term? A glimpse of the possibilities that ‘machine learning’ brings to us can be visualized using online apps prepared by Experiments with Google. In general, it is a group of designers and engineers who create fun experiments as a way of introducing these concepts based on Google’s technology. Some of them are cute and quick web games, others are more advanced, but all of them were done to make understanding new technologies more accessible to other people. Quick, Draw! is an online game similar to word-guessing charade like Pictionary. The difference is that you are not playing it with your friends but with the computer. You have 20 seconds to draw a pi.
Towards Data Science 10/21/2019 23:20
When everything comes down to a spot kick, who can you trust? Chelsea beat Bayern Munich on penalty kicks to lift the ’12 Champion League title. Interestingly, Robben missed a penalty kick that would have sealed victory for Bayern during extra time. Arguably the most nervy moment of all during a football match, the penalty shot is psychological warfare: Two men, one ball and a clear chance to score a goal. A penalty is supposed to be finished at the professional level: period. Anything else is a failure. Such a task should only be required of players made with the iciest of veins. There are great penalty-takers, those we can always count on to deliver the ball to the back of the net with a staggering metronomic efficiency. And there are tho.
Towards Data Science 10/21/2019 15:05
Exploring Data Augmentation with Keras and TensorFlow. A guide for using Data Augmentation with your next Deep Learning Project! Photo by on. Data augmentation is a strategy used to increase the amount of data by using techniques like cropping, padding, flipping, etc. Data augmentation makes the model more robust to slight variations, and hence prevents the model from overfitting . It is neither practical nor efficient to store the augmented data in memory, and that is where the ImageDataGenerator class from Keras (also included in the TensorFlow’s high level api: tensorflow.keras) comes into play.
Towards Data Science 10/21/2019 15:03
Photo by on. Time series. Time series analysis is a statistical technique that deals with time series data, or trend analysis. Time series data means that data is in a series of particular time periods or intervals. TSA(Time series analysis) applications:. Pattern recognition. Earthquake prediction. Weather forecast. Financial statistics. and many more…. MXnet. Apache MXNet (incubating) is a deep learning framework designed for both efficiency and flexibility. It allows you to mix symbolic and imperative programming to maximize efficiency and productivity. At its core, MXNet contains a dynamic dependency scheduler that automatically parallelizes both symbolic and imperative operations on the fly. A graph optimization layer on top of that makes symbo.

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