You may have figured out already that statistics isn't exactly a science. Lots of terms are open to interpretation, and sometimes there are many words that mean the same thing—like "mean" and "average"—or sound like they should mean the same thing, like significance level and confidence level.  

Although they sound very similar, significance level and confidence level are in fact two completely different concepts. Confidence levels and confidence…

Introduction

Co-relation does not equal causation – is a mantra drilled into a Data Scientist from an early age

That’s fine ..

But very few talk of the follow-on question ..

How exactly do you determine causation?

This problem is…

Monday newsletter published by Data Science Central. Previous editions can be found here. The contribution flagged with a + is our selection for the picture of the week. To subscribe, follow this link.  

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• Earn a Data…

They say that the best ideas sometimes come to you while you are in the shower, and this idea of how to explain two important Neural Network concepts – Backpropagation and Stochastic Gradient Descent – actually did come to me as I was trying to set the perfect water temperature for my morning shower.

As I was struggling to adjust the two shower handles – one handle that controlled scolding hot and the other handle that controlled flash freezing – it occurred to me that I was a simple…

With intense urbanization, the transportation industry works round the clock to suffice the demands of…

Every industry in the world is moving towards data-driven decision making, then one of the most popular and …

Overview of changes TensorFlow 1.0 vs TensorFlow 2.0

Earlier this year, Google announced TensorFlow 2.0, it is a major leap from the existing TensorFlow 1.0. The key differences are as follows:

Ease of use: Many old libraries (example tf.contrib) were removed, and some consolidated. For example, in TensorFlow1.x the model could be made using Contrib, layers, Keras or estimators, so many options for the same task confused many new users.…

The new era of BigData and advances in technology have made significant transitions towards the high functionality of IoT devices. The popularity of IoT devices has led to more easier methods for BigData collection, analysis, and distribution at a rapid rate. According to a report by…

Bayesian Machine Learning (part -6)

Probabilistic Clustering – Gaussian Mixture Model

Continuing our discussion on probabilistically clustering of our data, where we left out discussion on part 4 of our Bayesian inference series. As we have seen the modelling theory of…

Even though Amazon is the leader in e-commerce marketplaces – eBay still has its fair share in the online retail industry. Brands selling online should be monitoring prices on eBay as well to gain a competitive advantage. 

Extracting data from eBay at a huge scale regularly is a challenging problem for data scientists. Here is an example of scraping eBay using python to identify prices of…

Introduction

Big data analytics has been applied to different spheres of human life. One of the best features of data analytics is its adaptability and wide application specter. We have come through the whole series of articles concerning data science application in various spheres that are…

This article was written by Soft Media Lab.

It has the following sections.

Contents

• What is Machine Learning?
• How to apply machine learning to…

This article was written by Elie Bursztein.

This blog post survey the attacks techniques that target AI (artificial intelligence) systems and how to protect against them.

At a high level, attacks against classifiers can be broken down into three types:

• Adversarial inputs, which are specially crafted inputs that have been developed with the aim of…

Summary:  Autonomous vehicles (AUVs) and many other systems that need to accurately perceive the world around them will be much better off when image classification moves from 2D to 3D.  Here we examine the two leading approaches to 3D classification, Point Clouds and Voxel Grids.

One of the well-known…

Organizations rarely regard data entry as a key strategic operation. In spite of the rise in automation, companies across the world rely on manual data entry for procurement. They employ staff to transfer data from requisition orders, purchase orders, and invoices into enterprise resource planning (ERP) systems such as SAP, Oracle, PeopleSoft, Netsuite, and many others. 

Transcription and transposition errors are…

I hate it when I hear senior executives state that they want to become data-driven, as if somehow having data is value in of itself.  Now, one can hardly blame the unenlightened executive whose only perspectives on data are associated with statements like “Data is really the new oil” (Wall Street Journal) or “…

According to Wikipedia, an ANN is based on a collection of connected units or nodes called artificial neurons, which loosely model the neurons in a biological brain. Each connection, like the synapses in a biological brain, can transmit a signal to other neurons. An artificial neuron that receives a signal then processes it and can signal neurons connected to it.

In ANN implementations, the "signal" at a connection is a real number, and the output of each neuron is computed by some…

One of the things that excite the most about the predictive analytics of today is how quickly and bluntly it surpasses the existing benchmarks. Moreover, it penetrates the industries and activities where human creativity has traditionally dominated, adding a futuristic touch to the music, fine arts, fashion or architecture. Following the popular trend, we decided…