Confidence intervals (CIs) tell you how much uncertainty a statistic has. The intervals are connected to confidence levels and the two terms are easily confused, especially if you're new to statistics. Confidence Intervals in One Picture is an intro to CIs, and explains how each part interacts with margins of error and where the different components come… Continue
Added by Stephanie Glen on May 17, 2019 at 10:00am —
Here is our selection of featured resources and articles posted since Monday:
Added by Vincent Granville on May 16, 2019 at 12:00pm —
By Ajit Jaokar and Dan Howarth. With contributions from Ayse Mutlu.
Exclusively for Data Science Central members, with free access. You can download this book (PDF) here.
This tutorial began as a series of weekend workshops created by Ajit Jaokar and Dan Howarth. The idea was to work with a specific (longish) program such that we explore as much of it… Continue
Added by Vincent Granville on May 16, 2019 at 8:30am —
I was given 3 GB of Machine Generated data being fed by 120 sensors (5 records every second) in an excel format. The task in hand was to mine out interesting patterns, if any, from the data.
I fed the data in R in my local machine and performed various descriptive and exploratory analysis to have some insights. Customer was also looking for some low cost maintenance mechanisms for their machines. So I thought if I could study the outliers and provide some information about system… Continue
Added by Dr. Moloy De on May 15, 2019 at 7:30pm —
Machine learning applications require powerful and scalable computing systems that can sustain the high computation complexity of these applications. Companies that are working on the domain of machine learning have to allocate a significant amount of their budget for the OpEx of machine learning applications whether this is done on cloud or on-prem.
Typical machine learning application… Continue
Added by Chris Kachris on May 14, 2019 at 11:30pm —
PyTorch is known for being a clean framework, and hence it was a challenging task to achieve the production capability and flexibility needed for research. I think that the major hurdle for pushing production support to the core was going out of Python's realm and moving the PyTorch model to a faster, thread-safe language that has multithreading capability. But then, that violated the Python-first principle that PyTorch had up to that… Continue
Added by Packt Publishing on May 14, 2019 at 9:48pm —
The supply chain of today is governed by the manufacturer, wholesaler, retailer, and businesses operating in between. Not only has the concept of a supply chain progressed by leaps and bounds during the last decade or so, but it is also one of the most critical…
Added by Ronald van Loon on May 14, 2019 at 9:11pm —
The Future of Investment Banking
In this article, we look at how technology such as robotic automation, artificial intelligence and machine learning is changing and benefiting the investment banking industry.
A quick guide to investment banking
Banks are made up of different areas. They have retail banking, which is about providing loans, bank accounts and other services to the general…
Added by Harrison Goode on May 14, 2019 at 8:49am —
This resource is part of a series on specific topics related to data science: regression, clustering, neural networks, deep learning, decision trees, ensembles, correlation, Python, R, Tensorflow, SVM, data reduction, feature selection, experimental design, cross-validation, model fitting, and many more. To keep receiving these articles, sign up on… Continue
Added by Vincent Granville on May 13, 2019 at 10:08pm —
Cross validation is a technique commonly used In Data Science. Most people think that it plays a small part in the data science pipeline, i.e. while training the model. However, it has a broader application in model selection and hyperparameter tuning.
Let us first explore the process of cross validation itself and then see how it applies to different parts of the data science pipeline
Cross-validation is a resampling procedure used to evaluate machine learning models on a… Continue
Added by ajit jaokar on May 13, 2019 at 11:00am —
Here I want to present my new book on advanced algorithms for data-intensive applications named "Probabilistic Data Structures and Algorithms in Big Data Applications" (ISBN: 9783748190486). The detailed information about the book you can find at its webpage and below I give you some introduction to the topic this book is about.…
Added by Andrii Gakhov on May 13, 2019 at 9:00am —
Summary: Especially in consumer goods and retail the value of AI/ML is only part of the story. AI/ML will increasingly need to integrate with helper technologies to deliver maximum value. Up your game in IoT, 5G, and robotics to ensure you’re giving your operating team all the best options for their investment.
Added by William Vorhies on May 13, 2019 at 8:06am —
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.
Added by Vincent Granville on May 12, 2019 at 2:00pm —
Practical path for learning data science for beginners....
✅ Step 1
Download and Install Anaconda
✅ Step 2
a. Learn the basics of Python (Lists, Tuples, Dictionaries, etc)
b. Understand the basics of data structures and algorithms
✅ Step 3
Do more practice problems in Python…
Added by Ajit Singh on May 12, 2019 at 7:00am —
This article was written by Alexandr Honchar.
People use deep learning almost for everything today, and the “sexiest” areas of applications are computer vision, natural language processing, speech and audio analysis, recommender systems and predictive analytics. But there is also one field that is unfairly forgotten in terms of machine learning — signal processing (and, of course,… Continue
Added by Andrea Manero-Bastin on May 10, 2019 at 6:30am —
As a teacher of Data Science (Data Science for Internet of Things course at the University of Oxford), I am always fascinated in cross connection between concepts. I noticed an interesting image on Tess Fernandez slideshare (which I very much recommend you follow) which talked of… Continue
Added by ajit jaokar on May 10, 2019 at 6:13am —
The Next Generation of Data Science
Quite literally, I am stunned.
I have just completed my survey of data (from articles, blogs, white papers, university websites, curated tech websites, and research papers all available online) about predictive analytics.
And I have a reason to believe that we are standing on the brink of a revolution that will transform everything we know about data science and predictive analytics.
But before we go there,… Continue
Added by Divya Singh on May 9, 2019 at 10:30pm —
How are artificial intelligence (AI) and Automation different? Don’t they both offer options of hands-free, robotic style design?
Often times we use automation and artificial intelligence interchangeably, but they are very different. We think of them all as just “robots” while not fully understanding what is powering the robot’s intelligence.
However, there is a pretty big difference between Artificial Intelligence and Automation. It’s almost like comparing apples to oranges.… Continue
Added by Harrison Goode on May 9, 2019 at 10:30pm —
Let us begin with machine learning
Machine learning means that machines can learn to use big data sets to learn rather than hard-coded rules. It is the core of artificial intelligence and the fundamental way to make computers intelligent. It mainly uses induction, synthesis rather than deduction. Machine learning allows computers to learn by themselves. This type of learning benefits from the powerful processing power of modern computers and can easily handle large data… Continue
Added by Ajit Singh on May 9, 2019 at 1:00pm —
Here is our selection of featured articles and technical resources posted since Monday.
Added by Vincent Granville on May 9, 2019 at 12:30pm —