Over the past decade, the field of artificial intelligence (AI) has seen striking developments. As surveyed in, there now exist over twenty domains in which AI programs are performing at least as well as (if not better than) humans.
We compare Gartner 2018 Magic Quadrant for Data Science, Machine Learning Platforms vs its 2017 version and identify notable changes for leaders and challengers, including IBM, SAS, RapidMiner, KNIME, Alteryx, H2O.ai, and Domino.
This article explains the synergy between DevOps and Machine Learning and their applications like tracking application delivery, troubleshooting and triage analytics, preventing production failures, etc.
Also: Want a Job in Data? Learn This; A Comparative Analysis of Top 6 BI and Data Visualization Tools in 2018; 5 Fantastic Practical Natural Language Processing Resources; Neural network AI is simple
Build enterprise-grade functionally rich Spark applications with the aid of an intuitive drag-and-drop user interface and a wide array of pre-built Spark operators.
Control structures allow you to specify the execution of your code. They are extremely useful if you want to run a piece of code multiple times, or if you want to run a piece a code if a certain condition is met.
This article provides a short overview of emerging data scientist types and their unique skillsets, as well as a guide for HR professionals and analytics managers who are looking to hire their first data scientists or build a data science team. Included are an overview of skills for each type and specific questions that can be asked to assess candidates.
In this article, we will compare the most commonly used platforms and analyze their main features to help you choose one or several platforms that will provide indispensable aid for your work communication.
We update the top AI and Machine Learning projects in Python. Tensorflow has moved to the first place with triple-digit growth in contributors. Scikit-learn dropped to 2nd place, but still has a very large base of contributors.
Since 2011, AI hit hypergrowth, and researchers have created several AI solutions that are almost as good as – or better than – humans in several domains, including games, healthcare, computer vision and object recognition, speech to text conversion, speaker recognition, and improved robots and chat-bots for solving specific problems.
Here are 5 useful things to know about Data Science, including its relationship to BI, Data Mining, Predictive Analytics, and Machine Learning; Data Scientist job prospects; where to learn Data Science; and which algorithms/methods are used by Data Scientists
What do Amazon, Facebook, Google, IBM, Microsoft and Twitter have in common? They're all adopters of graph databases - a hot technology that continues to evolve.
Interested in learning the concepts behind Logistic Regression (LogR)? Looking for a concise introduction to LogR? This article is for you. Includes a Python implementation and links to an R script as well.
We discuss supervised learning, unsupervised learning and reinforcement learning, neural networks, and 6 reasons that helped AI Research and Development to move ahead.
This post may come off as a rant, but that’s not so much its intent, as it is to point out why we went from having very few AI experts, to having so many in so little time.
In order to understand how to estimate LTV, it is useful to first think about evaluating a customer’s lifetime value at the end of their relationship with us.
A dazzling review of AI History, from Alan Turing and Turing Test, to Simon and Newell and Logic Theorist, to Marvin Minsky and Perceptron, birth of Rule-based systems and Machine Learning, Eliza - first chatbot, Robotics, and the bust which led to first AI Winter.
One of the gems that I felt needed to be written down from Ng's deep learning courses is his general recipe to approaching a deep learning algorithm/model.
Download a free copy of the white paper The 7 Steps to Driving a Successful Data Science POC for a detailed walk-through of the seven steps to running a successful POC.
CNBC recently quoted this KDnuggets interview which discussed how United States Olympic Committee uses Big Data, how athletes respond to Analytical insights, integration of sports medicine into sports performance and sports injury.
Google Colaboratory is a promising machine learning research platform. Here are 3 tips to simplify its usage and facilitate using a GPU, installing libraries, and uploading data files.
For your convenience, we have prepared a comprehensive overview of the most important libraries used to perform machine learning and Data Science tasks in Scala.
Change management may be seen as an opposite to data science, but in reality both are related. Without proper implementation, a data science project fails.
It's about that time again... 5 more machine learning or machine learning-related projects you may not yet have heard of, but may want to consider checking out!
In this post, I will demonstrate how to use Google Colab for fastai. You can use GPU as a backend for free for 12 hours at a time. GPU compute for free? Are you kidding me?
Automating feature engineering optimizes the process of building and deploying accurate machine learning models by handling necessary but tedious tasks so data scientists can focus more on other important steps.
This post presents 5 fantastic practical machine learning resources, covering machine learning right from basics, as well as coding algorithms from scratch and using particular deep learning frameworks.
This guide serves as a basic hands-on work to lead you through building a neural network from scratch. Most of the mathematical concepts and scientific decisions are left out.
Coming soon: TDWI Las Vegas, BI + Analytics Huntington Beach, Strata San Jose, IBM Think Las Vegas, Big Data & Analytics Singapore, KNIME Berlin, Nvidia GPU, and more.
Furthermore, the learning rate affects how quickly our model can converge to a local minima (aka arrive at the best accuracy). Thus getting it right from the get go would mean lesser time for us to train the model.
In this blog post, I want to share the 8 neural network architectures from the course that I believe any machine learning researchers should be familiar with to advance their work.
Gainers and Losers in Gartner 2018 Magic Quadrant for Data Science and Machine Learning Platforms
A Comparative Analysis of Top 6 BI and Data Visualization Tools in 2018
Top 20 Python AI and Machine Learning Open Source Projects
5 Fantastic Practical Machine Learning Resources
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