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.
Open Data Science Conference just released 80% of their schedule and the first round of speakers for ODSC East 2018 in Boston, May 1-4. Learn, train and engage with 200+ world class experts. Save 50% with code KDNUGGETS.
The most common complaints we see from candidates who have faced rejection are lack of experience, education level requirements, lack of opportunities for Freshers, overly demanding and confusing job role requirements.
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.
Predictive Analytics World for Industry 4.0 is coming to Munich, 12-13 Jun 2018. Find the latest trends and technologies in machine & deep learning for the era of Internet of Things and artificial intelligence. Super Early Bird Rates end Mar 2.
Big Data revolution led to the explosion in Data Centers, which are consuming energy at increasingly higher rate. This blog reviews 2 standard methods for improving data center efficiency and argues that 3rd method - Machine Learning - is the best solution.
Read Gartner 2018 Magic Quadrant for Data Science and Machine Learning Platforms, courtesy of Domino, and learn which data science platform is right for your organization and why Domino was named a Visionary.
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.
Join us in San Francisco or London this Spring the next chapters of the World-renowned Data & Analytics Innovation Summits. Bringing together the top minds in Big Data and Analytics industry. Use code KD200 to get £/$200 off any two-day pass.
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.
Predictive Analytics World and Deep Learning World conferences are coming to Caesars Palace in Las Vegas, Jun 3-7. It's not too late to save with the Early Bird and attend the biggest PAW mega-event ever.
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
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.
The IEEE Big Data conference series started in 2013 has established itself as the top tier research conference in Big Data. We invite industrial, government, and academic organizations to submit proposals to organize a Data Challenge for the 2018 IEEE International Conference on Big Data.
At AnacondaCON 2018, Apr 8-11 in Austin, our passionate community of data scientists, IT professionals, analysts, developers, and business leaders will come together for 45+ talks on data science, machine learning, AI, and more. Early Bird offer - 2 tickets for price of one before Feb 28!
Also: Transfer Learning using differential learning rates; Which #MachineLearning #Algorithm be used in year 2118? We explain why it will be #Regression; 5 Fantastic Practical #MachineLearning Resources
Join MADS: Marketing Analytics and Data Science in San Francisco, Apr 11-13, hear first-hand from industry thought leaders and experts on how to navigate the challenges and succeed at making data work for YOU! Save 20% with VIP Code MADS18KDN
Understanding where and how AI will impact insurance is crucial to deliver growth and true business value. Download the whitepaper “Insurance AI – The Roadmap to Impact” which includes partners' AI plans for 2020 and which steps they are taking today to achieve this vision.
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.
This project is second phase of my popular project - Is Google Tensorflow Object Detection API the easiest way to implement image recognition? Here I extend the API to train on a new object that is not part of the COCO dataset.
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.
AI was compared to the discovery of fire, but its impact hinges on how creative we are with the technology—just like it did for early humans employing fire. Here are four diverse examples of applied AI to get your creative juices flowing.
Also: Introduction to Python Ensembles; 5 Machine Learning Projects You Should Not Overlook; Top 15 Scala Libraries for Data Science in 2018; Fast.ai Lesson 1 on Google Colab (Free GPU); The Doing Part of Learning Data Science
Also: Obfuscated Gradients Give a False Sense of Security: Circumventing Defenses to Adversarial Examples; Why Generalists Dominate #DataScience - an inconvenient truth; #ICYMI A Beginner’s Guide to Data Engineering – Part I
Designed specifically by, and for, senior software engineers, architects, and technical engineering managers, QCon.ai is a dedicated conference for AI and machine learning. Use code KDnuggets by Feb 17 to save.
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.
DataScience: Elevate will be held Feb 22 in San Francisco. Register now for the livestream to tune into a full day of panels and presentations from people and companies at the forefront of data science.
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.
Think 2018 is for those who seek inspiration and education, reinvention and innovation, want to connect with experts and seek progress. It is for understanding what is going on in the world around AI, Cloud, Data, Security, and Systems and discovering what’s possible. Use code TK18CAC to save.
Also: The 8 Neural Network Architectures Machine Learning Researchers Need to Learn; Avoid Overfitting with Regularization; Understanding Learning Rates and How It Improves Performance in Deep Learning; Comparing Machine Learning as a Service
This article explains overfitting which is one of the reasons for poor predictions for unseen samples. Also, regularization technique based on regression is presented by simple steps to make it clear how to avoid overfitting.
Join DataRobot on Feb 15 to discover how Automated Machine Learning provides the ability to develop and refresh Customer 360 predictive models, the ability to deploy models with a click of a button, 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.