KNIME Fall Summit takes place Nov 6-9 in Austin, Texas. Registration is now open, and KDnuggets readers save 10% on top of early bird rates with code KDNUGGETS!
Understand the inner workings of neural network models as this post covers three related topics: histogram of weights, visualizing the activation of neurons, and interior / integral gradients.
The approach basically coincides with Chollet's Keras 4 step workflow, which he outlines in his book "Deep Learning with Python," using the MNIST dataset, and the model built is a Sequential network of Dense layers. A building block for additional posts.
We're bringing together 200+ leaders from the data & analytics industry for you to network, learn and to discuss the latest trends, topics & opportunities. Use code KD300 to save.
Microsoft announces Microsoft Research Open Data, datasets representing many years of data curation and research efforts by Microsoft that were published as research outcomes.
Neural Networks are powerful but complex and opaque tools. Using Topological Data Analysis, we can describe the functioning and learning of a convolutional neural network in a compact and understandable way.
Why is this distinction important? Because it’s critical to understanding how leading-organizations are investing in new data engineering skills that exploit advanced analytics to create new sources of business and operational value.
Most of the modern data warehouse solutions are designed to work with raw data. It allows to re-transform data on the fly without a need to re-ingest your data stored in a warehouse.
Also Cartoon: FIFA World Cup Football and Machine Learning; What is it like to be a #MachineLearning engineer in 2018? The 5 Clustering Algorithms Data Scientists Need to Know.
The 4th part of this series will help answer the following questions: “Should I improve something or make changes to the system? Can it work more effectively? Can I squeeze the lion’s share of it?”
Our selection actually contains more than 20 libraries, as some of them are alternatives to each other and solve the same problem. Therefore we have grouped them as it's difficult to distinguish one particular leader at the moment.
We examine the required elements to solve an RL problem, compare passive and active reinforcement learning, and review common active and passive RL techniques.
Join hundreds of your peers at PAW Government, 18-19 Sep in Washington, DC, and learn how goverment agencies are using predictive analytics and AI to optimize operations and reduce costs.
A portfolio of real-world projects is the best way to break into data science. This article highlights the 5 types of projects that will help land you a job and improve your career.
Gaussian distribution model, often identified with its iconic bell shaped curve, also referred as Normal distribution, is so popular mainly because of three reasons.
WSO2 Stream Processor is an open source, lightweight, Streaming SQL based platform that enables you to do running aggregations, to detect patterns, and to generate alerts on data streams in real-time.
Machine Learning Services in SQL Server eliminates the need for data movement - you can install and run R/Python packages to build Deep Learning and AI applications on data in SQL Server.
Also: What is it like to be a machine learning engineer in 2018?; 7 Simple Data Visualizations You Should Know in R; Choosing the Right Metric for Evaluating Machine Learning Models - Part 2; Data Lake - the evolution of data processing
Check out this collection of 30 ML, DL, NLP & AI resources for beginners, starting from zero and slowly progressing to the point that readers should have an idea of where to go next.
A single query optimization tip can boost your database performance by 100x. Although we usually advise our customers to use these tips to optimize analytic queries (such as aggregation ones), this post is still very helpful for any other type of query.
Detection of sarcasm is important in other areas such as affective computing and sentiment analysis because such expressions can flip the polarity of a sentence.
In this blog I am going to talk about the issues related to initialization of weight matrices and ways to mitigate them. Before that, let’s just cover some basics and notations that we will be using going forward.
Also: Google #AI principles; #Cartoon: FIFA #WorldCup #Football and #MachineLearning; Introduction to Game Theory; Top 20 Recent Research Papers on Machine Learning and Deep Learning
Find the top tools for 4 distinct industries, learn what do developers in different sectors say is the next big thing, and more. Also get any Packt book or video for just $10.
In this article we will expand on the knowledge learnt from the last article - The What, Where and How of Data for Data Science - and consider how data science is applied to predict the future.
Check out this collection of NLP resources for beginners, starting from zero and slowly progressing to the point that readers should have an idea of where to go next.
We all know correlation doesn’t equal causality at this point, but when working with time series data, correlation can lead you to come to the wrong conclusion.
Also: Cartoon: 5 Machine Learning Projects You Should Not Overlook, June 2018; FIFA World Cup Football and Machine Learning; The What, Where and How of Data for Data Science; Data Lake the evolution of data processing
When it comes to disciplined approaches to feature selection, wrapper methods are those which marry the feature selection process to the type of model being built, evaluating feature subsets in order to detect the model performance between features, and subsequently select the best performing subset.
In honor of 2018 FIFA World Cup in Football, we update our classic KDnuggets cartoon - what players can do when their moves are predicted by Machine Learning?
TDWI Anaheim takes place Aug 5-10. Register by Super Early Bird Deadline on Jun 22 to save up to $915 with priority code KD20. Teams save an EXTRA 10%. Register now!
I developed my first IoT project using my notebook as an IoT device and AWS IoT as infrastructure, with this "simple" idea: collect CPU Temperature from my Notebook running on Ubuntu, send to Amazon AWS IoT, save data, make it available for Machine Learning models and dashboards.
While KDnuggets takes no side, we present the informative and respectful back and forth as we believe it has value for our readers. We hope that you agree.
Learn how to find value and insight in outliers in the latest anomaly detection guidebook by Dataiku, which includes use cases, and step-by-step guidance (including code samples) to starting an anomaly detection project.
This post examines the evolution of data processing in data lakes, with a particular focus on the concepts, architecture and technology criteria behind them.
After reading this, you’ll be back to fantasies of you + PyTorch eloping into the sunset while your Recurrent Networks achieve new accuracies you’ve only read about on Arxiv.
Want to generate text with little trouble, and without building and tuning a neural network yourself? Let's check out a project which allows you to "easily train your own text-generating neural network of any size and complexity on any text dataset with a few lines of code."
Read our eBook to learn how easy it is to build and scale ML models with a unified analytics platform, how to collaborate across data teams to uncover insights faster, and more. Free download.
KDnuggets poll compares Machine Learning Engineer, Researcher, Data Scientist and other professions and identifies one with the highest job satisfaction. Job satisfaction usually starts high, but drops significantly after 4 years on the job.
Statistics encourages us to think systemically and recognize that variables normally do not operate in isolation, and that an effect usually has multiple causes. Some call this multivariate thinking. Statistics is particularly useful for uncovering the Why.
Here is a new installment of 5 more machine learning or machine learning-related projects you may not yet have heard of, but may want to consider checking out!
With Anaconda Enterprise, National Grid was able to implement a more informed and cost-effective system that allowed for greater accuracy in modeling and predicting maintenance needs. Read the case study to learn more.
Apply online to The Institute for Statistics Education, the pioneer in online data science education. You can begin your online certificate right now - we offer rolling admission and introductory classes every month. Get started today!
This article examines the way you need to improve your training data and how it can be accomplished, including speech commands, choosing the right data, picking a model fast and more.
Also: Introduction to Game Theory (Part 1); Human Interpretable Machine Learning (Part 1) - The Need and Importance of Model Interpretation; DIY Deep Learning Projects; 10 More Free Must-Read Books for Machine Learning and Data Science
This tutorial will explain the steps required to package your Python projects, distribute them in distribution formats using steptools, upload them into the Python Package Index (PyPI) repository using twine, and finally installation using Python installers such as pip and conda.
See some of data’s most fascinating people, from data’s most successful companies, talking about data’s most intriguing problems, at Strata Data Conference in New York, Sep 11-13. Save an additional 20% with code KDNU.
Inspired by the great work of Akshay Bahadur in this article you will see some projects applying Computer Vision and Deep Learning, with implementations and details so you can reproduce them on your computer.
With Drexel University’s online MS in Business Analytics program, you’ll be able to effectively analyze this overlooked data to give your company and yourself a competitive edge.
Aspiring to master the command line should be on every developer’s list, especially data scientists. Learning the ins and outs of your terminal will undeniably make you more productive.
Also: Who Is Going To Make Money In #AI?; Generative Adversarial Networks (GANs) in 50 lines of code (PyTorch); Learning from Imbalanced Classes; 10 More Free Must-Read Books for Machine Learning and Data Science
Do your data visualizations need a reboot? Though data visualizations may be designed to facilitate understanding, not all graphs are effective. In this webcast, viewers will learn how to use best practices to give a graph a makeover.
We find 6 tools form the modern open source Data Science / Machine Learning ecosystem; examine whether Python declared victory over R; and review which tools are most associated with Deep Learning and Big Data.
Move your career forward in one of the fields with the largest demand. Business Analytics at Clark University will give you the skills employers demand by teaching you how to synthesize data into powerful information.
For Carlos Carcach, Professor & Director, Center for Public Policy at the Escuela Superior de Economía y Negocios (ESEN) in Santa Tecla, El Salvador, gangs are an object of intellectual curiosity and the subject of his research.
The process of audience segmentation is not about just statistics, it’s about finding your ideal clients and choosing the right way of interaction with them.
Check out this game theory basics post for an introduction to Two-player Sequential games — Dominant Strategies, Nash Equilibrium, and Cooperation vs. Defection.
NYU Stern MS in Business Analytics provides experienced professionals with a unique and valuable data-driven business perspective. This 1 year, part-time program is divided into 5 onsite modules with online independent study in between. Apply now.
The primary objective of this project was to handle data imbalance issue. In the following subsections, I describe three techniques I used to overcome the data imbalance problem.
Are you using your customer data to its full advantage? Chances are the answer is no. Customer Analytics from Wharton Executive Education, Sep. 17–21, 2018, Philadelphia, gives you a deeper, actionable understanding of your data.
The Big Data Toronto conference and expo is back for its 3rd edition on Jun 12-13, 2018 at the Metro Toronto Convention Centre. Big Data focuses on the skills, software and leadership needed to implement data insights & AI Toronto is dedicated to Toronto’s growing AI and deep learning communities.
A comprehensive list of resources for Women in Data Science and Machine Learning, including a list of useful tech groups and published lists for finding Women speakers.
In his book "Deep Learning with Python," Francois Chollet outlines a process for developing neural networks with Keras in 4 steps. Let's take a look at this process with a simple example.
Also: Descriptive analytics, machine learning, and deep learning viewed via the lens of CRISP-DM; On the contribution of neural networks and word embeddings in NLP; Improving the Performance of a Neural Network; Python eats away at R
Learn how an analytics leader enables results with priorities that include evangelizing the importance of data-driven decision-making; aligning analytics with a business value-driven approach; and developing an analytics competency to train and develop staff.
Coming soon: Mega-PAW Las Vegas, Spark + AI Summit SF, CogX London, Big Data Toronto Big Data Toronto Conference and Expo, ICDM/MLDM NYC, and many more.
In this webinar on Jun 5, 1:00 pm ET, analytics industry expert Jen Underwood will demonstrate how to visualize machine learning results with dashboard tools.
Judea Pearl has made noteworthy contributions to artificial intelligence, Bayesian networks, and causal analysis. These achievements notwithstanding, Pearl holds some views many statisticians may find odd or exaggerated.
In this post, we’ll use linear regression to build a model that predicts cherry tree volume from metrics that are much easier for folks who study trees to measure.
Top 20 Python Libraries for Data Science in 2018
5 Data Science Projects That Will Get You Hired in 2018
Detecting Sarcasm with Deep Convolutional Neural Networks
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