This one picture shows what areas of calculus and linear algebra are most useful for data scientists.

If you read any article worth its salt on the topic Math Needed for Data Science, you'll see calculus mentioned. Calculus (and it's closely related counterpart, linear algebra) has some very narrow (but very useful) applications to data science. If you have a decent algebra background (which I'm assuming you do, if you're a data scientist!) then you can learn…

Introduction

I had this conversation some time ago with an Engineer who came from a traditional background.

By that I mean, he had been in the same industry (heavy engineering) for 30 years.

Of these, he had been in the same company for 25 years (and this was his second job).

After understanding from me about data science, he said that as an engineer, he did…

Summary:  An update and observations about China’s plan to become the world leader in AI. 

Whether you get your news from Facebook or from the Wall Street Journal you can’t help having heard that China is out to displace the US as the world leader in AI.  Variously you may have heard that…

Dropout means to drop out units which are covered up and noticeable in a neural network. Dropout is a staggeringly in vogue method to overcome overfitting in neural networks.

Deep Learning framework is now getting further and more profound. With these bigger networks, we can accomplish better prediction exactness. However, this was not the case a few years ago. Deep…

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…

P-values and critical values are so similar that they are often confused. They both do the same thing: enable you to support or reject the null hypothesis in a test. But they differ in how you get to make that decision. In other words, they are two different approaches to the same result. This picture sums up the p value vs critical value approaches.…

Table of contents:

• What is Deep Learning?
  
  
• Supervised DL: The KISS Pathway That Leads To The Expected
  
  
• Unsupervised Deep Learning: An Exploratory Journey To Figuring Out the Unknown
  
  
• Decision Time: If You Knew Then What You Know…

Scaling AI with Dynamic Inference Paths in Neural Networks

Introduction

IBM Research, with the help of the University of Texas Austin and the University of Maryland, has tried to expedite the performance of neural networks by creating technology, called BlockDrop. Behind the design of this technology lies the objective and promise of speeding up convolutional neural network operations without…

Use of SecureSVM, Boosting, Bagging, Clustering, LSTM, CNN, GAN in Retail with BlockChain

Introduction

This blog highlights different ML algorithms used in blockchain transactions with a special emphasis on bitcoins in retail payments. This blog is structured as follows:

• Overview of the role of blockchain in the retail industry.
• Different traditional…

Anomaly Detection from Head and Abdominal Fetal ECG — A Case study of IOT anomaly detection using Generative Adversarial Networks

Unsupervised Anomaly Detection via Variational Auto-Encoder for Seasonal…

Motivation

There are five types of traditional time series models most commonly used in epidemic time series forecasting, which includes

• Autoregressive (AR),
• Moving Average (MA),
• Autoregressive Moving Average (ARMA),
• Autoregressive Integrated Moving Average (ARIMA), and
• Seasonal Autoregressive Integrated Moving Average (SARIMA) models.

AR models express the current value of the time series linearly in…

Traditional vs Deep Learning Algorithms in the Telecom Industry — Cloud Architecture and Algorithm Categorization

Google Cloud Architecture for Machine Learning Algorithms in the Telecom Industry

Introduction

The unprecedented growth of mobile devices, applications, and services have placed the utmost demand on mobile and wireless networking infrastructure. Rapid research…

I can't find anymore where this chart, featuring relations between distributions, was first published. I remember seeing it on the Cloudera blog.

Another shorter one featuring the most useful one for statistical analysis, can be found…

In the era of the Internet, the ability to crunch large amounts of data and process it with speed and efficiency has become essential for businesses to survive. But you try sitting down and going through all that data by hand: you'll get done sometime in the year 2050 if you're lucky.

That's where machine learning comes to the rescue. But…

Earlier this week, I was speaking at an event on AI for Real Estate where I showed an example from a BBC clip which said that “central London is now a ghost town” (due to COVID 19)

A few months ago, this headline would have been laughable

In London, central London and the London underground are a key fabric of daily…

Overview:

This post is about improving the effectiveness of the data science team and improving collaboration between data scientists and stakeholders for better outcomes.  

Aligning goals:

Regardless of the specific project, agreeing on the expected outcomes and goals before beginning the work is a best practice. But with the advent of machine learning (ML) models, it’s for both sides to discuss the critical measures of success for the…

Overview:

As a data scientist, have you ever been frustrated that your stakeholders don’t see the value that you bring to the table? You may ask yourself, “How far should I go in explaining the work I do or what my models are doing?” If that sounds like you, then pay close attention to this post and the next, as they are all about improving collaboration between data scientists and other stakeholders. 

This is a two-part post: This article…

Summary:  Advances in very low cost compute and Model Based Reinforcement Learning make this modeling technique that much closer to adoption in the practical world.

We keep asking if this is the year for reinforcement learning (RL) to finally make good on its many promises.  Like flying cars and jet packs the…

Over the last couple of years, there has been a lot of hype around robotic process automation. This makes a lot of sense if you consider that in 2018 Gartner was already labeling it “…

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…