The Model Management white paper, based on our experience working with hundreds of model-driven organizations, describes the reasons most organizations have not yet unlocked the transformative potential of models and provides a framework for success.
In this post I will try to explain, in a very simplified way, how to apply neural networks and integrate word embeddings in text-based applications, and some of the main implicit benefits of using neural networks and word embeddings in NLP.
In anticipation of his upcoming conference co-presentation at Deep Learning World in Las Vegas, June 3-7, we asked Abbas Chokor, Staff Data Scientist at Seagate Technology, a few questions about his work in deep learning.
Geoffrey Hinton, one of the fathers of Deep Learning, will be back to share his most recent and cutting-edge research progressions, and will be joined by other top researchers. Save 20% on Early Bird passes when you sign up before 15 June w. code KDNUGGETS.
Also check Women in AI dinner series and get new white paper on Ethical implications of AI.
In anticipation of his upcoming conference presentation at Predictive Analytics World for Business Las Vegas, June 3-7, we asked Nishant Sharma, Director, Predictive Analytics at Charter Communications, a few questions about his work in predictive analytics.
CRISP-DM methodology is a must teach to explain analytics project steps. This article purpose it to complement it with specific chart flow that explain as simply as possible how it is more likely used in descriptive analytics, classic machine learning or deep learning.
Business users, decision-makers, and experts in predictive analytics will meet on 12-13 June 2018 in Munich to discover and discuss the latest trends and technologies in machine & deep learning for the era of Internet of Things and artificial intelligence.
R is a great choice for manipulating, cleaning, summarizing, producing probability statistics, and so on. In addition, it's not going away anytime soon, it is platform independent, so what you create will run almost anywhere, and it has awesome help resources.
Frequentist methods are sometimes described as “classical”, though most have only appeared in recent decades and new ones are under development as you read this. Whatever we call it, this branch of statistics is very much alive.
The traditional concept of ETL is changing towards ELT – when you’re running transformations right in the data warehouse. Let’s see why it’s happening, what it means to have ETL vs ELT, and what we can expect in the future.
Familiarize yourself with the tools, technologies, and techniques that help you derive value from data at TDWI Anaheim, Aug 5-10, with TDWI’s Hands-on Lab Series and the Data Science Bootcamp. Save up to $915 with code KD20.
What are the critical steps to get a job in data science? We share the proven formula that helped many data enthusiasts secure job offers as data scientist/analyst, data engineer and machine learning engineer.
Also: An Introduction to Deep Learning for Tabular Data; 9 Must-have skills you need to become a Data Scientist, updated; GANs in TensorFlow from the Command Line: Creating Your First GitHub Project; Complete Guide to Build ConvNet HTTP-Based Application
This article introduces a pip Python package called KernelML, created to give analysts and data scientists a generalized machine learning algorithm for complex loss functions and non-linear coefficients.
Watch over 20 hours of YouTube videos on databases and database design, Physical Data Storage, Transaction Management and Database Access, and Data Warehousing, Data Governance and (Big) Data Analytics - all free.
Optimization is a technique for finding out the best possible solution for a given problem for all the possible solutions. Optimization uses a rigorous mathematical model to find out the most efficient solution to the given problem.
Download the report Find the Right Accelerator for your Deep Learning Needs to learn how I&O leaders must deliver effective machine learning infrastructures that effectively balance performance, cost, and functionality while minimizing complexity.
Learn how data scientists across have influenced and changed analysis behaviors across their companies, and get helpful tips for integrating data science findings into your organization decision making process.
Also: 5 Reasons “Logistic Regression” should be the first thing you learn when becoming a Data Scientist; WTF is a Tensor?!?; 10 Free Must-Read Books for #MachineLearning and #DataScience; Annual KDnuggets Software Poll
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.
A new full day training workshop has been announced for Predictive Analytics World's Mega-PAW in Las Vegas, Jun 4: Deep Learning in Practice: A Hands-On Introduction. Mega-PAW is Jun 3-7. Register now!
In this tutorial, a CNN is to be built, and trained and tested against the CIFAR10 dataset. To make the model remotely accessible, a Flask Web application is created using Python to receive an uploaded image and return its classification label using HTTP.
Like Wikipedia, there are all kinds of data stored in Wikidata. As such, when you are looking for a specific dataset or if you want to answer a curious question, it can be a good start looking for that data at Wikidata first.
The agenda for Predictive Analytics World for Industry 4.0 is here! See the heavy-weights from companies like PWC, Uber, Siemens, Airbus and many more who will gather in Munich on 12-13 Jun for insightful sessions on the latest in industry trends and achievements.
5 Reasons "Logistic Regression" should be the first thing you learn when becoming a Data Scientist; PyTorch Tensor Basics; Top 7 Data Science Use Cases in Finance; Detecting Breast Cancer with Deep Learning; To SQL or not To SQL: that is the question!
Join 4,000 of the top developers, data scientists, and business executives who will be tuning into the sessions and training at this year's Spark+AI Summit. Use code KDnuggets to save 30% when you register by May 18.
The data warehouse promised to deliver a single version of truth. But skeptics abound, saying a single version of truth is a mirage and not necessary. Join this webinar and learn from experts debating this question.
This article provides a short introductory guide for executives curious about data science or commonly used terms they may encounter when working with their data team. It may also be of interest to other business professionals who are collaborating with data teams or trying to learn data science within their unit.
We have prepared a list of data science use cases that have the highest impact on the finance sector. They cover very diverse business aspects from data management to trading strategies, but the common thing for them is the huge prospects to enhance financial solutions.
What will 2018's key trends for machine learning be? Read what Predictive Analytics World Founder Eric Siegel has to say on the subject. And don't forget to register for Mega-PAW in Las Vegas, Jun 3-7!
Breast cancer is the most common invasive cancer in women, and the second main cause of cancer death in women, after lung cancer. In this article I will build a WideResNet based neural network to categorize slide images into two classes, one that contains breast cancer and other that doesn’t using Deep Learning Studio.
Predictive Analytics World for Industry 4.0, the leading vendor-independent conference for applied predictive analytics is back, in Munich, 12-13 Jun. Discover and discuss the latest trends and technologies in machine & deep learning for the era of Internet of Things and artificial intelligence.
To help data science teams adopt Docker and apply DevOps best practices to streamline machine learning delivery pipelines, we open-sourced a toolkit based on the popular cookiecutter project structure.
Machine Learning Yearning is a book by AI and Deep Learning guru Andrew Ng, focusing on how to make machine learning algorithms work and how to structure machine learning projects. Here we present 7 very useful suggestions from the book.
I joined the competition a month before it ended, eager to explore how to use Deep Natural Language Processing (NLP) techniques for this problem. Then came the deception. And I will tell you how I lost my silver medal in that competition.
Join Forrester and Anaconda for a webinar on Thursday, May 17, at 2:00 PM CT, to learn best practices for scaling data science across your entire organization. Learn how to tackle five key challenges facing organizations today!
Also: 50+ Useful Machine Learning & Prediction APIs, 2018 Edition; 8 Useful Advices for Aspiring Data Scientists; Apache Spark : Python vs. Scala; 8 Useful Advices for Aspiring Data Scientists; Data Science vs Machine Learning vs Data Analytics vs Business Analytics
Check out the 55+ full and half-day courses in four core learning tracks plus five accelerated learning fast tracks, Aug 5-10 at TDWI Anaheim, and buckle up for a week of in-depth training in sunny SoCal! Save up to $915 with priority code KD20 before Jun 15.
When it comes to using the Apache Spark framework, the data science community is divided in two camps; one which prefers Scala whereas the other preferring Python. This article compares the two, listing their pros and cons.
Kurtosis and Skewness are very close relatives of the “data normalized statistical moment” family – Kurtosis being the fourth and Skewness the third moment, and yet they are often used to detect very different phenomena in data. At the same time, it is typically recommendable to analyse the outputs of both together to gather more insight and understand the nature of the data better.
I recently read Sebastian Gutierrez’s “Data Scientists at Work”, in which he interviewed 16 data scientists. I want to share the best answers that these data scientists gave for the question: "What advice would you give to someone starting out in data science?"
Studies have shown that only 1% or less of total users click on privacy policies, and those that do rarely actually read them. The GDPR requires clear succinct explanations and explicit consent, but that’s not the situation on the ground right now, and it’s hard to see that changing overnight on May 25th.
The aim of these notebooks is to help beginners/advanced beginners to grasp linear algebra concepts underlying deep learning and machine learning. Acquiring these skills can boost your ability to understand and apply various data science algorithms.
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.
spaCy is a Python natural language processing library specifically designed with the goal of being a useful library for implementing production-ready systems. It is particularly fast and intuitive, making it a top contender for NLP tasks.
The Jupyter Notebook is an incredibly powerful tool for interactively developing and presenting data science projects. Although it is possible to use many different programming languages within Jupyter Notebooks, this article will focus on Python as it is the most common use case.