Data Engineer and Business Analyst Might be the Best Data Science Opportunities

Not everyone wants to invest the time and money to become a data scientist, and if you’re mid-career the barriers are even higher. If you still want to be deeply involved in the new data-driven economy and well paid, the growth rate and opportunities as a data engineer or business analyst need to be on your radar screen.

Implementing a Soft-Margin Kernelized Support Vector Machine Binary Classifier with Quadratic Programming in R and Python

In this article, couple of implementations of the support vector machine binary classifier with quadratic programming libraries (in R and python respectively) and application on a few datasets are going to be discussed.

How to make Seaborn Pairplot and Heatmap in R (Write Python in R)

Before you judge me, let me confirm it that ggplot2 is amazing. But there are a couple of plots that I admire in Python’s modern Data Visualisation library Seaborn. It’s not just it produces high-quality visualization but also how easy and simple it is building that one. Those two plots are heatmap and pairplot. I’ve always missed them but I guess not anymore.

Hyperparameters Tuning With Polyaxon

hyperparameters tuning is very important concept in order to choose the optimal hyperparameters for a given algorithm, it is crucial for the success of a machine learning model or a deep learning architecture, since they heavily influence the behavior of the model learning. Often the search space of hyperparameters is fairly large for most machine learning and deep learning algorithms, that manually tuning is impossible.

Data Analytics, Algorithms & Machine Learning – Online Survey

This report, ‘Data Analytics, Algorithms & Machine Learning – Online Survey,’ was produced by Informa Engage on behalf of Dell EMC. The data was collected March 8, through March 26, 2018 from a wide cross section of industries. The goal was to investigate various issues around the current and future use of use of analytics, predictive analytics and machine learning, including:
◾ Satisfaction with data analytics activities
◾ Key benefits and deterrents associated with the use of machine learning
◾ Companies associated with machine learning

Top 16 Open Source Deep Learning Libraries and Platforms

1. TensorFlow
2. Keras
3. Caffe
4. Microsoft Cognitive Toolkit (Previously CNTK)
5. PyTorch
6. Apache MXnet
7. DeepLearning4J
8. Theano
9. TFLearn
10. Torch
11. Caffe2
12. PaddlePaddle (PArallel Distributed Deep LEarning)
13. DLib
14. Chainer
15. Neon
16. Lasagne

The current state of the Stan ecosystem in R

Last week I posted here about the release of version 2.0.0 of the loo R package, but there have been a few other recent releases and updates worth mentioning. At the end of the post I also include some general thoughts on R package development with Stan and the growing number of Stan users who are releasing their own packages interfacing with rstan or one of our other packages.