While many of the programming libraries encapsulate the inner working details of graph and other algorithms, as a data scientist it helps a lot having a reasonably good familiarity of such details. A solid understanding of the intuition behind such algorithms not only helps in appreciating the logic behind them but also helps in making conscious decisions about their applicability in real life cases. There are several graph based algorithms and most notable are the shortest path algorithms. Algorithms such as Dijkstra’s, Bellman Ford, A*, Floyd-Warshall and Johnson’s algorithms are commonly encountered. While these algorithms are discussed in many text books and informative resources online, I felt that not many provided visual examples that would otherwise illustrate the processing steps to sufficient granularity enabling easy understanding of the working details. As such, I had to use simple enough graphs to visualize the algorithmic flow for my own understanding and I wanted to share my examples along with the explanations through this article. Since there are many algorithms to illustrate, I decided to divide the article into several parts. In part 1, I have illustrated Dijkstra’s and Bellman-Ford algorithms. Before diving into algorithms, I also wanted to highlight salient points on the graph data structure.Content of this article:Quick Primer On Graph Data StructureDijkstra’s AlgorithmBellman-Ford AlgorithmMore Algorithms To CoverRead the full article here. Written by Murali Kashaboina, Tech. Executive, PhD Researcher AI/ML/DS, Data Scientist, Industry Speaker, Entrepreneur.See More

In 2019, 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. TensorFlow 2.0 promotes TensorFlow Keras for model experimentation and Estimators for scaled serving, and the two APIs are very convenient to use.Eager Execution: In TensorFlow 1.x. The writing of code was divided into two parts: building the computational graph and later creating a session to execute it. this was quite cumbersome, especially if in the big model that you have designed, a small error existed somewhere in the beginning. TensorFlow2.0 Eager Execution is implemented by default, i.e. you no longer need to create a session to run the computational graph, you can see the result of your code directly without the need of creating Session.Model Building and deploying made easy: With TensorFlow2.0 providing high level TensorFlow Keras API, the user has a greater flexibility in creating the model. One can define model using Keras functional or sequential API. The TensorFlow Estimator API allows one to run model on a local host or on a distributed multi-server environment without changing your model. Computational graphs are powerful in terms of performance, in TensorFlow 2.0 you can use the decorator tf.function so that the following function block is run as a single graph. This is done via the powerful Autograph feature of TensorFlow 2.0. This allows users to optimize the function and increase portability. And the best part you can write the function using natural Python syntax.Read the full article here. To access the author's books covering machine learning, Azure, Tensorflow, deep learning and related topics (free for DSC members), follow this link. See More

Summary: AI/ML itself is the next big thing for many fields if you’re on the outside looking in. But if you’re a data scientist it’s possible to see those advancements that will propel AI/ML to its next phase of utility. “The Next Big Thing in AI/ML is…” as the lead to an article is probably the most overused trope since “once upon a time”. Seriously, just how many ‘next big things’ can there be? Is your incredulity not stretched every time you read that?It’s tempting to say that writers starting an article in this way should be flogged …except that yours truly did recently start one with “the next most IMPORTANT thing in AI/ML…” Well that’s clearly different isn’t it – almost.If you label something ‘next big thing’ it’s evident you have a strong opinion – or your marketing department has no imagination. First of all, if you’re on the outside of AI/ML looking in, AI/ML clearly is the next big thing. Most next-big-thing articles are actually in this category, explaining how AI/ML can enhance everything from your dating life to your investment portfolio.But if you’re fortunate enough to be on the inside as our readers are then you know that the future of AI/ML is developing along many different paths and some of those should be more important than others. Some are technical, some are applications, and some are even social or philosophical. So how to tell what the next big thing is or at least what the rankings should be.Read the full article here. For more recent articles about AI, follow this link. See More

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There's no doubt about it, probability and statistics is an enormous field, encompassing topics from the familiar (like the average) to the complex (regression analysis, correlation coefficients and hypothesis testing to name but a few). If you want to be a great data scientist, you have to know some basic statistics. The following picture shows which statistics topics you must know if you're going to excel in data science.Read the full article here. For more concepts explained in one picture, follow this link. For articles about statistical and machine learning concepts explained in simple English, from the same author, follow this link. Or to download a book featuring many of these resources, click here (free, but available to DSC members exclusively.)From our SponsorsFuture-proof your path to Enterprise AI - Dataiku 6 Webinar RecordingSee More

At the time of writing, I'm a 52 year-old working in the fields of mathematics and data science. In mathematics, that makes me well-seasoned (and probably well-tenured, if I had chosen to continue in academia). In data science, some would consider me a dinosaur. In fact, many older people considering a career in data science might be put off by the thought that data science is tough to break into at a later age. But is that statement true? Should the over 50 crowd put down their textbooks and pick up their gardening tools?Is Math a Young Person's Game? MaybeAs far as the mathematics portion of my career, I didn't become a mathematician until I was in my mid-thirties. Before that I dabbled with whatever venture brought in a few bob to feed the kids: computer operator, Ebay entrepreneur, aviation electrician. I was 36 when I decided to go back to school to get my master's. If Alfred Adler is to be believed, my "mathematical life" had already long passed by the time I graduated.Work rarely improves after the age of twenty-five or thirty. If little has been accomplished by then, little will ever be accomplished. Read the full article by Stephanie Glen, here. For other articles by Stephanie Glen, follow this link. Sponsored AnnouncementBe Indispensable With a Master’s in Data Analytics. As technology and the marketplace change constantly, you want the skills to thrive. The UCLA Anderson Master of Science in Business Analytics is a 13-month program that will give you the tools to become a leader in this rapidly evolving field. Read more here. See More

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This article is by Jorge Castañón, Ph.D., Senior Data Scientist at the IBM Machine Learning Hub.Data visualization plays two key roles:1. Communicating results clearly to a general audience.2. Organizing a view of data that suggests a new hypothesis or a next step in a project.It’s no surprise that most people prefer visuals to large tables of numbers. That’s why clearly labeled plots with meaningful interpretation always make it to the front of academic papers.This post looks at the 10 visualizations you can bring to bear on your data — whether you want to convince the wider world of your theories or crack open your own project and take the next step:HistogramsBar/Pie chartsScatter/Line plotsTime seriesRelationship mapsHeat mapsGeo Maps3-D PlotsHigher-Dimensional PlotsWord cloudsRead the full article, with descriptions and illustrations for these visualizations, here.See More

Some original and very interesting material is presented here, with possible applications in Fintech. No need for a PhD in math to understand this article: I tried to make the presentation as simple as possible, focusing on high-level results rather than technicalities. Yet, professional statisticians and mathematicians, even academic researchers, will find some deep and fascinating results worth further exploring.Can you identify patterns in this chart? (see section 2.2. in the article for an answer)Let's start with Here the X(k)'s are random variable identically and independently distributed, commonly referred to as X. We are trying to find the distribution of Z.Contents1. Using a Simple Discrete Distribution for X2. Towards a Better ModelApproximate SolutionThe Fractal, Brownian-like Error Term3. Finding X and Z Using Characteristic FunctionsTest with Log-normal Distribution for XPlaying with the Characteristic FunctionsGeneralization to Continued Fractions and Nested Cubic Roots4. ExercisesRead this article here. See More