A key strength of NLP (natural language processing) is being able to process large amounts of texts and then summarise them to extract meaningful insights.
In this example, a selection of economic bulletins in PDF format from 2018 to 2019 are analysed in order to gauge economic sentiment. The bulletins in question are sourced from the European Central Bank website. tf-idf is used to rank…Continue
Added by Michael Grogan on July 11, 2019 at 12:28pm — No Comments
The use of Docker in conjunction with AWS can be highly effective when it comes to building a data pipeline.
Let me ask you if you have ever had this situation before. You are building a model in Python which you need to send over to a third-party, e.g. a client, colleague, etc. However, the person on the other end cannot run the code! Maybe they don't have the right libraries installed, or their system is not configured correctly.
Whatever the reason, Docker alleviates this…Continue
Added by Michael Grogan on July 5, 2019 at 8:30am — No Comments
The housing market has undergone quite a change in the past decade, with more stringent lending criteria for housing having been enforced.
A key objective of financial institutions is to minimise the risk of mortgage lending by ensuring that the debtor is ultimately able to repay the loan.
In this example, multilevel modelling techniques are used to analyse data from the Federal Home Loan Bank…Continue
Added by Michael Grogan on July 3, 2019 at 3:01am — No Comments
Hotel cancellations can cause issues for many businesses in the industry. Not only is there the lost revenue as a result of the customer cancelling, but this can also cause difficulty in coordinating bookings and adjusting revenue management practices.
Data analytics can help to overcome this issue, in terms of identifying the customers who are most likely to cancel – allowing a hotel chain to adjust its marketing strategy accordingly.
To investigate how machine learning can…Continue
Added by Michael Grogan on July 2, 2019 at 3:00am — No Comments
Are you looking to learn python for data science but have a time crunch? Are you making your career shift into data science and want to learn python? In this blog, we will talk about learning python for data science in just 30 days. Also, we will look at weekly schedules and topics to cover in python.
Before directly jumping to python, let us understand about the usage of python in data…Continue
There is a library called threading in Python and it uses threads (rather than just processes) to implement parallelism. This may be surprising news if you know about the Python's Global Interpreter Lock, or GIL, but it actually works well for certain instances without violating the GIL. And this is all done without any overhead -- simply define…Continue
Python and R are the two most commonly used languages for data science today. They are both fully open source products and completely free to use and modify as required under the GNU public license.
But which one is better? And, more importantly, which one should you learn?
Both are widely used and are standard tools in the hands of every data scientist.
The answer may surprise you – because as a professional data scientist, you should be ready to deal with…Continue
There has been much hype surrounding deep learning and data science learning in recent times, and one of the cornerstones of deep learning is the neural network. In this article, we will look at what a neural network is and get familiar with the relevant terminologies.
In simplest terms, a neural network is an interconnection of neurons. Now the question arises, what is a neuron? To understand neurons in deep learning, we first…Continue
Added by Divya Singh on September 20, 2018 at 4:00am — No Comments
The second edition (fully revised, extended, and updated) of Machine Learning Algorithms has been published (Packt).
From the back cover:
Machine learning has gained tremendous popularity for its…Continue
Added by Giuseppe Bonaccorso on September 2, 2018 at 7:18am — No Comments
Artificial Intelligence with Python
By Prateek joshi
Build real-world Artificial Intelligence applications with Python to intelligently interact with the world around you
What you will…Continue
Pandas dataframe is making life a lot easier if you are working with data. A lot of data comes in CSV format. It's possible to read CSV files to a Pandas dataframe. In fact, it's quite easy using read_csv. In the video below we will learn the basics of just loading a CSV file to a Pandas dataframe object. …Continue
Added by Erik Marsja on July 17, 2018 at 6:01am — No Comments
Recurrent Neural Nets (RNN) detect features in sequential data (e.g. time-series data). Examples of applications which can be made using RNN’s are anomaly detection in time-series data, classification of ECG and …Continue
Added by Ahmet Taspinar on July 5, 2018 at 11:48am — No Comments
From the back cover:
Machine learning is a subset of AI that aims to make modern-day computer systems smarter and more…Continue
R Deep Learning Essentials
By Joshua F. Wiley
Get everything you need to know to enter the world of deep learning when it comes to R with this book. Get started from the packages you need to have for your side,…Continue
Added by Packt Publishing on May 15, 2018 at 10:00pm — No Comments
I’d like to introduce a series of blog posts and their corresponding Python Notebooks gathering notes on the Deep Learning Book from Ian Goodfellow, Yoshua Bengio, and Aaron Courville (2016). The aim of these notebooks is to help beginners/advanced beginners to grasp linear algebra concepts underlying deep learning and machine learning. Acquiring these skills can boost your ability to understand and apply various data science algorithms. In…Continue
In a previous blog post we have seen how to build Convolutional Neural Networks (CNN) in Tensorflow, by building various CNN architectures (like LeNet5, AlexNet, VGGNet-16) from scratch and training them on the MNIST, CIFAR-10 and Oxflower17 datasets.
It starts to get interesting when you start thinking about the practical applications of CNN and other Deep Learning methods. If you have been following the latest technical developments you probably know that CNN’s are…Continue
Added by Ahmet Taspinar on December 4, 2017 at 5:00am — No Comments
Added by dataperspective on November 15, 2017 at 1:30am — No Comments
Michael Li is founder and CEO at The Data Incubator. The company offers curriculum based on feedback from corporate and government partners about the technologies they are using and learning, for masters and PhDs.
Below is a ranking of 23 open-source deep learning libraries that are useful for Data Science, based on Github and Stack Overflow activity, as well as Google search results. The…Continue
Added by Michael Li on October 17, 2017 at 12:00pm — No Comments
Technology has remarkably changed the way we live today, there is no denial to it. Compared with our ancestors, we stand far away from them in using different technologies for our day-to-day works.
So many technologies are developed in the past couple of years that have revolutionized our lives, and it’s impossible to list each of them. Though technology changes fast with time, we can observe the trends in which it changes. Last year, 2016 had bought so many fresh…Continue
Added by Venkatesan M on October 6, 2017 at 9:00pm — No Comments
The numpy, scipy, and statsmodels libraries are frequently used when it comes to generating regression output. While these libraries are frequently used in regression analysis, it is often the case that a user might choose different libraries depending on the data in question, among other considerations. Here, we will go through how to use each of the above to generate regression output.
Added by Michael Grogan on August 26, 2017 at 6:30am — No Comments