By Derrick Mwiti, Data Analyst

Change R Package Name (changer)Changing the name of an existing R package is annoying but common task especially in the early stages of package development. This package (mostly) aut …

A career in data science is hyped as the hottest job of the 21st century, but how do you become a data scientist? How should you, as an aspiring data scientist, or a student who aims at a data science job, prepare? What are the skills you need? What must you do? Fret not – this article will answer all your questions and give you links with which you can jump-start a new career in data science!

In this blog post, similar to a previous blog postI am going to show you how we can go from an Excel workbook that contains data to flat file. I willtaking advantage of the structure of the tables inside the Excel sheets by writing a functionthat extracts the tables and then mapping it to each sheet!

In between client work, John and I have been busy working on our book, Practical Data Science with R, 2nd Edition. To demonstrate a toy example for the section I’m working on, I needed scatter plots of the petal and sepal dimensions of the iris data, like so:

Dynamical Atoms Network (DYAN) The ability to anticipate the future is essential when making real time critical decisions, provides valuable information to understand dynamic natural scenes, and can help unsupervised video representation learning. State-of-art video prediction is based on LSTM recursive networks and/or generative adversarial network learning. These are complex architectures that need to learn large numbers of parameters, are potentially hard to train, slow to run, and may produce blurry predictions. In this paper, we introduce DYAN, a novel network with very few parameters and easy to train, which produces accurate, high quality frame predictions, significantly faster than previous approaches. DYAN owes its good qualities to its encoder and decoder, which are designed following concepts from systems identification theory and exploit the dynamics-based invariants of the data. Extensive experiments using several standard video datasets show that DYAN is superior generating frames and that it generalizes well across domains. …

Bidirectional Conditional Generative Adversarial Network Conditional variants of Generative Adversarial Networks (GANs), known as cGANs, are generative models that can produce data samples ($x$) conditioned on both latent variables ($z$) and known auxiliary information ($c$). Another GAN variant, Bidirectional GAN (BiGAN) is a recently developed framework for learning the inverse mapping from $x$ to $z$ through an encoder trained simultaneously with the generator and the discriminator of an unconditional GAN. We propose the Bidirectional Conditional GAN (BCGAN), which combines cGANs and BiGANs into a single framework with an encoder that learns inverse mappings from $x$ to both $z$ and $c$, trained simultaneously with the conditional generator and discriminator in an end-to-end setting. We present crucial techniques for training BCGANs, which incorporate an extrinsic factor loss along with an associated dynamically-tuned importance weight. As compared to other encoder-based GANs, BCGANs not only encode $c$ more accurately but also utilize $z$ and $c$ more effectively and in a more disentangled way to generate data samples. …

Personalized Embedding Propagation: Combining Neural Networks on Graphs with Personalized PageRank

Causal inference and Bayesian network structure learning from nominal data