R Correlation Tutorial

In this tutorial, you explore a number of data visualization methods and their underlying statistics. Particularly with regard to identifying trends and relationships between variables in a data frame. That’s right, you’ll focus on concepts such as correlation and regression! First, you’ll get introduced to correlation in R. Then, you’ll see how you can plot correlation matrices in R, using packages such as ggplot2 and GGally. Lastly, you’ll see what types of correlations exist and how they matter for your further analysis. If you’re interested in diving deeper into this topic, consider taking DataCamp’s Correlation and Regression course.

Statistical Modeling: A Primer

As I briefly explain in A Model’s Many Faces, I often find it helpful to classify models as conceptual, operational or statistical. In this post we’ll have a closer look at the last of these, statistical models. First, it’s critical to understand that statistical models are simplified representations of reality and, to paraphrase the famous words of statistician George Box, they’re all wrong but some of them are useful. So why do we use statistical models? We use them because we need to better understand something we don’t understand very well or because we wish to predict something – sales, for instance.

Real-Time Big Data Analytics: Platform Requirements and Architecture Best Practice

#1: Capacity and Velocity of Front-End Servers
#2: Real Request-Processing Capacity, Not Just Bandwidth
#3: Don’t Underestimate The Need for Speed
Sum it Up – Best Practice:
The introduction of real-time big-data analytics requires a fresh look at your platform choices and infrastructure capacity. To build a great real-time big data analytics system, you need:
1. At the front-end, high performance, low latency, and highly scalable servers, that can keep up with high-velocity data and events from a large number of concurrently active clients.
2. At the analytics/business logic tier, high-performance data server and middleware that can keep up with real-time data, in both latency and throughput.
3. High-performance network infrastructure – look for real packet handling capacity and low latency, in addition to bandwidth (Gbit) specifications.

Distill: Supporting Clarity in Machine Learning

Science isn’t just about discovering new results. It’s also about human understanding. Scientists need to develop notations, analogies, visualizations, and explanations of ideas. This human dimension of science isn’t a minor side project. It’s deeply tied to the heart of science. That’s why, in collaboration with OpenAI, DeepMind, YC Research, and others, we’re excited to announce the launch of Distill, a new open science journal and ecosystem supporting human understanding of machine learning. Distill is an independent organization, dedicated to fostering a new segment of the research community.

More on 3rd Generation Spiking Neural Nets

Here’s some background on how 3rd generation Spiking Neural Nets are progressing and news about a first commercial rollout. Recently we wrote about the development of AI and neural nets beyond the second generation Convolutional and Recurrent Neural Nets (CNNs / RNNs) which have come on so strong and dominate the current conversation about deep learning. Our research shows that the next generation of neural nets is most likely to be led by Spiking Neural Nets (SNNs) that are a return to the ‘strong’ AI tradition and closely mimic actual brain function.

The Four Stages of a Chatbot’s Business Intelligence Evolution

Stage 1) Single Numeric Response Question
Stage 2) Multiple Numeric Response Question
Stage 3) Predictive Numeric Response Question
Stage 4) Predictive Question Generation