Visualizations are a powerful way to simplify and interpret the underlying patterns in data. The first thing I do, whenever I work on a new dataset is to explore it through visualization. And this approach has worked well for me. Sadly, I don´t see many people using visualizations as much. That is why I thought I will share some of my ‘secret sauce’ with the world! Use of graphs is one such visualization technique. It is incredibly useful and helps businesses make better data-driven decisions. But to understand the concepts of graphs in detail, we must first understand it´s base – Graph Theory.
Online Statistics: An Interactive Multimedia Course of Study is a resource for learning and teachingintroductory statistics. It contains material presented in textbook format and as video presentations. Thisresource features interactive demonstrations and simulations, case studies, and an analysis lab.
I have started reading about Deep Learning for over a year now through several articles and research papers that I came across mainly in LinkedIn, Medium and Arxiv. When I virtually attended the MIT 6.S191 Deep Learning courses during the last few weeks (Here is a link to the course site), I decided to begin to put some structure in my understanding of Neural Networks through this series of articles.
Scalable Deep Learning services are contingent on several constraints. Depending on your target application, you may require low latency, enhanced security or long-term cost effectiveness. Hosting your Deep Learning model on the cloud may not be the best solution in such cases.
In a web application, routing is the process of using URLs to drive the user interface. Routing adds more possibilities and flexibility while building a complex and advanced web application, offering dividing app into separated sections.
Deep learning continues to be the hottest thing in data science. Deep learning frameworks are changing rapidly. Just five years ago, none of the leaders other than Theano were even around. I wanted to find evidence for which frameworks merit attention, so I developed this power ranking. The Python language is the clear leader for deep learning, so I focussed on frameworks compatible with it. I used 11 data sources across 7 distinct categories to gauge framework usage, interest, and popularity. Then I weighted and combined the data in this Kaggle Kernel.
In many decisive battles, battles lines are drawn, strategies are made, and a winner emerges. However, sometimes, pitifully, battles are lost because of utter ignorance?-?ignorance of even where the battle front is?-?and more sadly, self-deception. A brilliant piece of analysis from Forrester reports that 40 ‘insight-driven companies’ are going to grab $1.8 trillion by 2021?-?most likely a part of this is going to be carved out of market cap of your organization. In this list we have young companies that are less than 8 years old. What unifies them? Their obsession with data and AI. Broadly, with respect to AI adoption, organizations fall into one of the two categories: First, we have the ‘talkers’: there are organizations wetting their feet on what they typically call ‘AI initiatives’?-?taking small risk averse steps in organizational silos, getting tangled by bureaucracies and a minority few unfortunately focusing more on press coverage than actual outcome. Then we have ‘Do-ers’: These are the insights driven companies, that have integrated (or on a strong path to integrate) Analytics & AI into their organizational fabric. These organizations have a holistic approach to what I would like to call ‘AI enabled Value Chain’. Which one are you and where do you want to be?
Exploring data using natural language (‘plain English’) query expressions isn’t a new concept, but it has become more relevant and more feasible lately. People are used to search engines and like the metaphor as data querying experience. Products like Thoughtspot and Answer Rocket specialize in this teaming of search and data discovery. And the Q&A feature of Microsoft Power BI enables this, both for ad hoc queries in dashboards and even for use as an authoring tool when designing reports. Many natural language analytics products, however, require data to be moved into their own repositories or index structures. But today, Arcadia Data is announcing a new Search feature, in the latest release of its Arcadia Enterprise product, that adapts the natural language query paradigm to work directly on top of data lakes.
In this course, you will learn the foundations of A/B testing, including hypothesis testing, experimental design, and confounding variables. You will also be exposed to a couple more advanced topics, sequential analysis and multivariate testing. The first dataset will be a generated example of a cat adoption website. You will investigate if changing the homepage image affects conversion rates (the percentage of people who click a specific button). For the remainder of the course you will use another generated dataset of a hypothetical data visualization website.
This is part-2 in the feature encoding tips and tricks series with the latest Spark 2.3.0. Please refer to part-1, before, as a lot of concepts from there will be used here. As mentioned before, I assume that you have a basic understanding of spark and its datatypes. If not, spark has an amazing documentation and it would be great to go through. For background on spark itself, go here for a summary.
In a previous post, we explained how neural networks work to predict a continuous value (like house price) from several features. One of the questions we got is how neural networks can encode concepts, categories or classes. For instance, how can neural networks convert a number of pixels to a true/false answer whether or not the underlying picture contains a cat?