In June 2018, Las Vegas will host the largest Predictive Analytics World ever, with PAW Business, Financial, Healthcare, and Manufacturing, and Deep Learning World. Get SEB discount till Dec 22.
In this third part of this series of posts the contributions of InfoGAN will be explored, which apply concepts from Information Theory to transform some of the noise terms into latent codes that have systematic, predictable effects on the outcome.
The way most Machine Learning models work on Spark are not straightforward, and they need lots of feature engineering to work. That’s why we created the feature engineering section inside the Optimus Data Frame Transformer.
Also #DeepLearning Specialization by Andrew Ng - 21 Lessons Learned; How (and Why) to Create a Good Validation Set; Predicting Cryptocurrency Prices With #DeepLearning
Feature selection is a very important technique in machine learning. In this post we discuss one of the most common optimization algorithms for multi-modal fitness landscapes - evolutionary algorithms.
We show how to build a deep neural network that classifies images to many categories with an accuracy of a 90%. This was a very hard problem before the rise of deep networks and especially Convolutional Neural Networks.
Data science needs fast computation and transformation of data. NumPy objects in Python provides that advantage over regular programming constructs like for-loop. How to demonstrate it in few easy lines of code?
In this webinar, Dec 12, DataRobot outlines Multichannel Marketing Attribution with Automated Machine Learning, demonstrating how automated machine learning offers the shortest path to success. Space is limited, so sign up now!
Introducing the Natural Language Processing Library for Apache Spark - and yes, you can actually use it for free! This post will give you a great overview of John Snow Labs NLP Library for Apache Spark.
Insurance claims is standing on the brink of transformation with new technology uncovering opportunities to process claims more efficiently and provide a superior customer experience. Learn about the oportunities in this Dec 14 Webinar.
One of the main principles I learned during my time at Google Brain was that unit tests can make or break your algorithm and can save you weeks of debugging and training time.
Also: New Poll: Data Science / Machine Learning methods you used; The amazing predictive power of conditional probability in Bayes Nets; The 10 Statistical Techniques Data Scientists Need to Master.
We compare survival analysis to other predictive techniques, and provide examples of how it can produce business value, with a focus on Kaplan-Meier and Cox Regression methods which have been underutilized in business analytics.
This is a visualization of the inter- and intra-continental migration of scientific researchers based on ORCID (Open Researcher and Contributor ID) data. It is best seen as a directional sample of all researchers, and tracks their earliest/latest countries with research activities as well as their PhD countries.
The course is for developers and architects who want to transition their career to Enterprise AI, but also has strategic (non-coding) version. The course starts in Jan 2018 and will take 3 months for the content and up to 3 months for the team project.
Also: Estimating an Optimal Learning Rate For a Deep Neural Network; Automated Feature Engineering for Time Series Data; How (and Why) to Create a Good Validation Set; Building a Wikipedia Text Corpus for Natural Language Processing; The 10 Statistical Techniques Data Scientists Need to Master
I found all 3 courses extremely useful and learned an incredible amount of practical knowledge from the instructor, Andrew Ng. Ng does an excellent job of filtering out the buzzwords and explaining the concepts in a clear and concise manner.
The definitions of training, validation, and test sets can be fairly nuanced, and the terms are sometimes inconsistently used. In the deep learning community, “test-time inference” is often used to refer to evaluating on data in production, which is not the technical definition of a test set.
At the heart of this reproducibility problem is the statistical inference methods used to validate research findings—specifically the concept of “statistical significance.”
This blog post is targeted towards people who have experience with machine learning, and want to get a better intuition on the different objective functions used to train neural networks.
Wikipedia is a rich source of well-organized textual data, and a vast collection of knowledge. What we will do here is build a corpus from the set of English Wikipedia articles, which is freely and conveniently available online.
Python has a ton of plotting libraries—but which ones should you use? And how should you go about choosing them? This webinar shows you key starting points and demonstrates how to solve a range of common problems.
Although NLP and text mining are not the same thing, they are closely related, deal with the same raw data type, and have some crossover in their uses. Let's discuss the steps in approaching these types of tasks.
Chief Data & Analytics Officer Sydney event has assembled an outstanding speaker line up to address all things data and analytics. Special KDnuggets discount.
This year, the ODSC West was held at the Hyatt Regency San Francisco Airport, from November 2 to 4. I am, attempting here, to give you a snapshot tour of what I experienced.
If you are a developer or data scientist interested in big data, Spark is the tool for you. Download this ebook to learn why Spark is a popular choice for data analytics, what tools and features are available, and much more.
Download this whitepaper from NVIDIA DGX Systems, and gain insight into the engineering expertise and innovation found in pre-optimized deep learning frameworks available only on NVIDIA DGX Systems and learn how to dramatically reduce your engineering costs using today’s most popular frameworks.
Ontotext live, online training designed to improve understanding of how Semantic Technology operates to help you make best use of it. Preparation starts Nov 30 and live class is Dec 7.
We introduce a general framework for developing time series models, generating features and preprocessing the data, and exploring the potential to automate this process in order to apply advanced machine learning algorithms to almost any time series problem.
Former U.S. Chief Data Scientist DJ Patil will be lending his expertise to DataScience.com’s product, engineering, and R&D teams as they expand the features of the company’s enterprise data science platform.
Also: A Day in the Life of a Data Scientist; Top 10 Videos on Deep Learning in Python; 8 Ways to Improve Your Data Science Skills in 2 Years; Machine Learning Algorithms: Which One to Choose for Your Problem; Top 10 Machine Learning Algorithms for Beginners
Sharing one platform has some obvious benefits for Data Science and Data Engineering teams, but technical, language and process challenges often make this a challenge. Learn how one company implemented single cloud platform for R, Python and other workloads – and some of the unexpected benefits they discovered along the way.
Black Friday/Cybermonday sale on best courses from Udemy, including Data Science, Machine Learning, Python, Spark, Tableau, and Hadoop - only $10 until Nov 28, 2017.
TDWI provides the in-depth, vendor-neutral training in business analytics, data science, and data management, including a certificate track. Save 30% thru Dec 15, 2017 with code KD30.
Playlists, individual tutorials (not part of a playlist) and online courses on Deep Learning (DL) in Python using the Keras, Theano, TensorFlow and PyTorch libraries. Assumes no prior knowledge. These videos cover all skill levels and time constraints!
If you develop methods for data analysis, you might only be conducting gentle tests of your method on idealized data. This leads to “fragile research,” which breaks when released into the wild. Here, I share 3 ways to make your methods robust.
Two years. Two years is the maximum amount of time you should spend focused on your learning, education and training. That’s exactly why this guide is focused on honing the most beneficial skills in two years.
RE•WORK are pleased to announce the launch of 'Expo Only Passes' for the upcoming San Francisco events, on January 25 from 14:00 - 18:00. Plus, save 20% on passes to all RE•WORK summits with the code KDNUGGETS.
If you follow AI you might have heard about the advent of the potentially revolutionary Capsule Networks. I will show you how you can start using them today.
PySpark is a Spark Python API that exposes the Spark programming model to Python - With it, you can speed up analytic applications. With Spark, you can get started with big data processing, as it has built-in modules for streaming, SQL, machine learning and graph processing.
Also: What is the difference between Bagging and Boosting?; Which #Python package manager should you use?; The Practical Importance of Feature Selection.
Feature selection is a key part of data science but is it still relevant in the age of support vector machines (SVMs) and Deep Learning? Yes, absolutely. We explain why.
Linear Regression is an excellent starting point for Machine Learning, but it is a common mistake to focus just on the p-values and R-Squared values while determining validity of model. Here we examine the underlying assumptions of a Linear Regression, which need to be validated before applying the model.
The author presents 10 statistical techniques which a data scientist needs to master. Build up your toolbox of data science tools by having a look at this great overview post.
Organizations are seeking top-notch, global talent that understand how to effectively leverage data to make more informed decisions. Just ask Deepesh Chandra, a recent graduate of of NYU Stern MS in Business Analytics.
The first comprehensive and objective survey of online Masters in Analytics / Data Science, including rankings, tuition, and duration of the education program.
This article will try to explain basic concepts and give some intuition of using different kinds of machine learning algorithms in different tasks. At the end of the article, you’ll find the structured overview of the main features of described algorithms.
Strata Data Conference is where thousands of innovators, leaders, and practitioners gather to develop new skills, share best practices, and discover how tools and technologies are evolving. Best rate ends Dec 8 - use code PCKDNG to save.
This article explains how Bayes Nets gain remarkable predictive power by their use of conditional probability. This adds to several other salient strengths, making them a preeminent method for prediction and understanding variables’ effects.
Also: TensorFlow: What Parameters to Optimize?; 7 Super Simple Steps From Idea To Successful Data Science Project; Tips for Getting Started with Text Mining in R and Python; Top 10 Machine Learning Algorithms for Beginners
Are you interested in what a data scientist does on a typical day of work? Each data science role may be different, but these five individuals provide insight to help those interested in figuring out what a day in the life of a data scientist actually looks like.
With our Online Data Mining Certificates, you’ll learn to guide important business decisions, become indispensable to your organization, and give your career a boost. Benefit from flexibility, world-class teaching and research, and a Stanford credential.
Are you using your customer data to its full advantage? Chances are the answer is no. Customer Analytics, Feb 26-Mar 1, from Wharton Executive Education gives you a deeper, actionable understanding of your data.
Bayes Nets have remarkable properties that make them better than many traditional methods in determining variables’ effects. This article explains the principle advantages.
Kevin and Koen may buy the same brand for the same reasons. On the other hand, they may buy the same brand for different reasons, or buy different brands for the same reasons, or even different brands for different reasons. The brands they purchase and the reasons why may vary by occasion, too.
Learning TensorFlow Core API, which is the lowest level API in TensorFlow, is a very good step for starting learning TensorFlow because it let you understand the kernel of the library. Here is a very simple example of TensorFlow Core API in which we create and train a linear regression model.
Also: One LEGO at a time: Explaining the #Math of How #NeuralNetworks Learn; 6 Books Every #DataScientist Should Keep Nearby; Direct from Sebastian Raschka #Python #MachineLearning book, new edition.
Also: Understanding Machine Learning Algorithms; Want to Become a Data Scientist? Read This Interview First; 6 Books Every Data Scientist Should Keep Nearby.
The demand for professionals that can build financial analytics programs is booming. We foresee two main objectives- to predict market movement for profit, and to protect customer assets of banks.
Open Source is the heart of innovation and rapid evolution of technologies, these days. Here we discuss how to choose open source machine learning tools for different use cases.
Ever had this great idea for a data science project or business? In the end you did not do it because you did not know how to make it a success? Today I am going to show you how to do it.
We analyze the results of Data Science / Machine Learning peak demand poll, examine the split between optimists and pessimists, and try to explain why predictions look so similar regardless of experience, affiliation, and region?
The 2018 Data Science & Marketing Analytics Conference, April 11-13, San Francisco, will focus on how Data can be used to drive specific business purposes. Exclusive Offer for KDnuggets Readers: Save 20% with VIP Code MADS18KDN.
Data Scientist is a very broad term and hiring a good fit data scientist for your project is challenging task. Here we discuss this important topic in details.
This post summarizes the contents of a recent O'Reilly article outlining a number of methods for interpreting machine learning models, beyond the usual go-to measures.
The advances in image classification, object detection, and semantic segmentation using deep Convolutional Neural Networks, which spawned the availability of open source tools such as Caffe and TensorFlow (to name a couple) to easily manipulate neural network graphs... made a very strong case in favor of CNNs for our classifier.
Learn how much value companies can get by adding AI to business applications and processes through AI and automation, how to architect a smart business with ubiquitous AI, and more.
Are you a data science leader, or aspiring to be one? Learn how industry leaders manage their data science initiatives as core capabilities that drive their company’s strategic objectives.
Also: Advice For New and Junior Data Scientists; 7 Steps to Mastering Deep Learning with Keras; Getting Started with Machine Learning in One Hour!; Top 10 Machine Learning Algorithms for Beginners
We need to create a sense of urgency around exploring and analyzing data. We also need to train and empower individuals to know how. This video covers the need for students to enter the workforce with analytics skills and why we need to give employees permission to fail.
Join us at TDWI Orlando, Dec 3-8, where we bring the future of data and analytics to life. KDnuggets Readers Save 20% when you register by November 17 with priority code KDSUN.
Once you’ve read this article, you will understand the basics of AI and ML. More importantly, you will understand how Deep Learning, the most popular type of ML, works.
Learn how to identify and manage operational risk, litigation risk and reputational risk. This course is brought to you by HarvardX in collaboration with GetSmarter, experts in online education for working professionals.
In the past years, several niche tools have appeared to mine organizational business processes. In this article, we’ll show you that it is possible to get started with “process mining” using well-known data science programming languages as well.
This article is simply a stream of consciousness on questions and problems I have been thinking and asking myself, and hopefully, it will stimulate some discussion.
This article is for people who are already in the field but are just starting out. My goal is to not only use this post as a reminder to myself about the important things that I have learned, but also to inspire others as they embark onto their DS careers!
Also Applied #AI Summit will give you the tools for your AI journey, 5-7 Feb, London;10 Free Must-Read Books for Machine Learning, Data Science; Ranking Popular #DeepLearning Libraries for #DataScience.
Coming soon: ODSC West, MLconf San Francisco, PAW Berlin, IEEE ICDM New Orleans, Data Marketing Toronto, Big Data & Analytics Innovation Summit Beijing, Chief Data Scientist San Francisco, and many more.
In this extract from “Python Machine Learning” a top data scientist Sebastian Raschka explains 3 main types of machine learning: Supervised, Unsupervised and Reinforcement Learning. Use code PML250KDN to save 50% off the book cost.
Here is a machine learning getting started guide which grew out of the author's notes for a one hour talk on the subject. Hopefully you find the path helpful.
Understanding Deep Convolutional Neural Networks with a practical use-case in Tensorflow and Keras
Why You Should Forget ‘for-loop’ for Data Science Code and Embrace Vectorization
The 10 Statistical Techniques Data Scientists Need to Master
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