With traditional TV viewing on the decline, we discuss several ways Big Data and Machine Learning can assist with online video, including redefining user recommendations, improving video buffering and leveraging MAM orchestration.
What follows is then an effort to draw an architecture to access knowledge on AI and follow emergent dynamics, a gateway of pre-existing knowledge on the topic that will allow you to scout around for additional information and eventually create new knowledge on AI.
Join data and analytics leaders at CAO Fall in Boston, Oct 8-11, the platform to guide you through transformation and help you innovate within your business. KDnuggets readers save $100 on your pass using discount code KDNUGGETS100.
A detailed comparison between self-service data preparation tools and enterprise-level solutions, covering business strategy, accessible tools and solutions and more.
Career fairs are a great way to get your feet wet if you’re just starting your data science career, or to be exposed to newer trends and emerging organizations if you’re already established. What other ways are career fairs beneficial?
A well-known model that learns vectors or words from their co-occurrence information is GlobalVectors (GloVe). While word2vec is a predictive model — a feed-forward neural network that learns vectors to improve the predictive ability, GloVe is a count-based model.
Find out how to serve your scikit-learn model in an auto-scaling, serverless environment! Today, we’ll take a trained scikit-learn model and deploy it on Cloud ML Engine.
The World's Biggest Deep Learning Summit is returning to San Francisco in January 2019. Use code SUMMER for an additional 25% off the Super Early Bird Ticket rate by September 7.
Information on how to download this whitepaper, which provides a view into how streaming data analytics is different from traditional analytics and thus have unique data processing needs that translate into absolute must-haves for the streaming analytics platform.
In this blog, we’ll try to understand the different interpretations of this “distant” notion. We will also look into the outlier detection and treatment techniques while seeing their impact on different types of machine learning models.
I reported that you can multiply the speed of common (fast) random number generators such as PCG and xorshift128+ by a factor of three or four by vectorizing them using SIMD instructions. Is this actually useful in practice?
We discuss the key considerations in selecting the optimal AI infrastructure required to train deep neural networks for safe self-driving systems, including data requirements and computing performance needed, and how to use NVIDIA DGX-1 for training autonomous vehicles.
Predictive Analytics World for Government, Sep 18-19, Washington DC, is a practically-focused, vendor neutral conference that highlights case studies and emerging trends of how government agencies are currently using data analytics to solve real world problems.
Also: Why Automated Feature Engineering Will Change the Way You Do Machine Learning; Interpreting a data set, beginning to end; Auto-Keras, or How You can Create a Deep Learning Model in 4 Lines of Code; Emotion and Sentiment Analysis: A Practitioners Guide to NLP
We examine what's important for data scientists in their careers, including challenging work, networking with peers, foreseeing their career path and creating a good work-life balance.
The vast majority of text classification articles and tutorials on the internet are binary text classification such as email spam filtering and sentiment analysis. Real world problem are much more complicated than that.
Open-source Dash lets you wrap a GUI around that analytical code, without leaving the familiarity of Python. Explore your data with rich, interactive drop-down menus, sliders, and other components, all in the web browser.
Core principles for successful data visualization, including tips on how to reduce clutter, preattentive processing and how to integrate text within the graph.
Sentiment analysis is widely used, especially as a part of social media analysis for any domain, be it a business, a recent movie, or a product launch, to understand its reception by the people and what they think of it based on their opinions or, you guessed it, sentiment!
DynamoDB vs. Cassandra: have they got anything in common? If yes, what? If no, what are the differences? We answer these questions and examine performance of both databases.
There is a need to compare different APIs to understand key pros and cons they have and when it is better to use one API instead of the other. Let us proceed with the comparison.
A summary of the key points from the Google Cloud Next in San Francisco, "What’s New with TensorFlow?", including neural networks, TensorFlow Lite, data pipelines and more.
Both athletes and machines deal with inter-twined complex systems (where the interactions of one complex system can have a ripple effect on others) that can have significant impact on their operational effectiveness.
Learn more about the hottest trends that are shaping the future and beyond at Big Data Summits in London and Barcelona. Deep dive into the topics that will shake up your industry and encourage innovation at your company. Enjoy £250 off all two-day events with code KD250.
This comprehensive cheat sheet will assist Docker users, experienced and new, in getting containers up-and-running quickly. We list commands that will allow users to install, build, ship and run Docker containers.
Realizing that there is a legitimate knowledge gap between UX Designers and Data Scientists, I have decided to attempt addressing the needs from the Data Scientist’s perspective.
At the most basic level, probability seeks to answer the question, "What is the chance of an event happening?" To calculate the chance of an event happening, we also need to consider all the other events that can occur.
Detailed knowledge of your data is key to understanding it! We review several important methods that to understand the data, including summary statistics with visualization, embedding methods like PCA and t-SNE, and Topological Data Analysis.
August is a popular time for vacation, and even hard-working AI may want to take a few epochs off from its training. KDnuggets Cartoon looks at how this might go.
Using the Python gradient boosting library LightGBM, this article introduces fraud detection systems, with code samples included to help you get started.
Auto-Keras is an open source software library for automated machine learning. Auto-Keras provides functions to automatically search for architecture and hyperparameters of deep learning models.
Named entity recognition (NER) , also known as entity chunking/extraction , is a popular technique used in information extraction to identify and segment the named entities and classify or categorize them under various predefined classes.
Northwestern’s MASTER OF SCIENCE IN DATA SCIENCE is a fully online, part-time program that helps students build essential analysis and leadership skills for today's data-driven world. Apply now!
The deadline to save up to £300 with Early Bird Prices for Predictive Analytics World in London October 17-18 is fast approaching! Book now to save your spot.
In this on-demand webinar, you’ll get a general introduction to working with Tensorflow and its surrounding ecosystem, general problem classes, where you can get big acceleration, and why you should be running on a CPU.
An introduction to Project Hydrogen: how it can assist machine learning and AI frameworks on Apache Spark and what distinguishes it from other open source projects.
In this post, I will explore the implementation of reinforcement learning in trading. The Financial industry has been exploring the applications of Artificial Intelligence and Machine Learning for their use-cases, but the monetary risk has prompted reluctance.
This is an overview of some basic functionality of the MXNet ndarray package for creating tensor-like objects, and using the autograd package for performing automatic differentiation.
Basic Statistics in Python: Descriptive Statistics; Top 12 Essential Command Line Tools for Data Scientists; WTF is a Tensor?!?; How GOAT Taught a Machine to Love Sneakers;
Check schedule for ODSC West (Oct 31 - Nov 3), fantastic keynotes for ODSC Europe (Sep 19-22), and get last remaining tix for ODSC India, Aug 30 - Sep 3.
In this post we’ll give an introduction to the exploratory and visualization t-SNE algorithm. t-SNE is a powerful dimension reduction and visualization technique used on high dimensional data.
Auto-Keras is an open source "competitor" to Google’s AutoML, a new cloud software suite of Machine Learning tools. It’s based on Google’s state-of-the-art research in Neural Architecture Search (NAS).
In this post, we'll walk through how to set up a data science environment on Google Cloud Platform (GCP). Because of the economy of scale that cloud hosting companies provide, individuals or teams can affordably access powerful computers.
Learn a process for discovering the data and analytics needs of your users using user stories, use cases and mapping to data sources; Strategies for balancing priorities and managing expectations, and more.
Read this eBook to learn: How deep learning enables image classification, sentiment analysis, and other advanced analysis techniques and get a a starter workflow for building and training deep learning models.
Docker is an increasingly popular way to create and deploy applications through virtualization, but can it be useful for data scientists? This guide should help you quickly get started.
Around twenty million people worldwide suffer from drug-resistant epilepsy and the unpredictability of seizures is one of the major factors affecting the quality of life of people with epilepsy.
Whether you're a novice data science enthusiast setting up TensorFlow for the first time, or a seasoned AI engineer working with terabytes of data, getting your libraries, packages, and frameworks installed is always a struggle. Learn how datmo, an open source python package, helps you get started in minutes.
Also: Only Numpy: Implementing GANs and Adam Optimizer using Numpy; Understanding Language Syntax and Structure; Eight iconic examples of data visualisation; 5 Data Science Projects That Will Get You Hired in 2018; Seven Practical Ideas For Beginner Data Scientists
Win KDnuggets pass to AI Conference in San Francisco, where you'll join the leading minds in AI: Kai-Fu Lee, Meredith Whittaker, Peter Norvig, Dawn Song, David Patterson, Huma Abidi, Matt Wood, and more. Enter by Aug 18.
Many researchers need access to multi-year historical repositories of online news articles. We identified three companies that make such access affordable, and spoke with their CEOs.
Knowledge about the structure and syntax of language is helpful in many areas like text processing, annotation, and parsing for further operations such as text classification or summarization.
Download Figure Eight's new ebook, The Essential Guide to Training Data, and you'll learn about the advantages of using more data, the differences between having lots of big data and having labeled data, and some great open datasets to bootstrap your model.
When you think of the perfect data science team, are you imagining 10 copies of the same professor of computer science and statistics, hands delicately stained with whiteboard marker? We hope not!
Cross-validation is frequently used to train, measure and finally select a machine learning model for a given dataset because it helps assess how the results of a model will generalize to an independent data set in practice.
At base, RL is a complex algorithm for mapping observed entities and measures into some set of actions, while optimizing for a long-term or short-term reward.
Key information regarding The Alteryx Analytics Revolution Summit roadshow in Australia, including dates, guest speakers, livestream information and how you can register for the roadshow closest to you.
Learn about MLOps –machine learning operationalization that breaks down the silos between data science and IT; Streamlines deployment and orchestration, and adds advanced functionality.
Download this very useful book chapter, and learn how to create derived variables, which allow the statistical and Data Science modeling to incorporate human insights.
Cover all things within the realm of Big Data Innovation and Data Visualization as you advance your learning, knowledge and understanding on areas including: Use code KD200 to save.
Embeddings are a fantastic tool to create reusable value with inherent properties similar to how humans interpret objects. GOAT uses deep learning to generate these for their entire sneaker catalogue.
As someone who has been there, I’d like to outline a few practical ideas to help junior data scientists get started at a small software company. The steps were drawn from my personal journey and that of others before me.
In this post, I'll go over the two mindsets most people switch between when doing programming work specifically for data science: the prototype mindset and the production mindset.
Springboard is hosting a special webinar to give you an inside look at what it means to be a data scientist. Learn from a practicing data scientist on Wed Aug 8, 12 PM PDT.
Companies like The Washington Post, Alibaba.com, ING and many more will be at Predictive Analytics World London, 17-18 Oct. Check out the newly released schedule now!
Also: Eight iconic examples of data visualisation; Selecting the Best Machine Learning Algorithm for Your Regression Problem; Intuitive Ensemble Learning Guide with Gradient Boosting; Eight iconic examples of data visualisation; Data Scientist Interviews Demystified
The AI Conference returns to San Francisco, Sept 4–7. Get a sweeping understanding of the rapidly advancing AI landscape. Save an extra 20% on most passes with code KDN20.
By using the within-cluster sum of squares as cost function, data points in the same cluster will be similar to each other, whereas data points in different clusters will have a lower level of similarity.
I will highlight some of the most important steps which are used heavily in Natural Language Processing (NLP) pipelines and I frequently use them in my NLP projects.
This event connects C-suite, Heads and Managers of Mine Operations and Mining Equipment, Technology and Services providers to debate and define the future mining landscape on a strategic level. Special KDnuggets discount.
In this live webinar (Aug 8, 1PM EST), discover research findings, best practices for AI adoption, use cases on the growth of machine learning, and how automated machine learning technologies make AI more accessible to organizations of all sizes.
Coming soon: TDWI Anaheim, JupyterCon NYC, VLDB Rio, ODSC India, KDD 2018 London, AI Conference San Francisco, Big Data Innovation Boston, Strata Data NYC, and many more.
We look at typical questions in a data science interview, examine the rationale for such questions, and hope to demystify the interview process for recent graduates and aspiring data scientists.
Also: Comparison of Top 6 Python NLP Libraries; Math for Machine Learning: Open Doors to Data Science and Artificial Intelligence; Building A Data Science Product in 10 Days; Data Scientist was the sexiest job of the 21st century until...; Automated Machine Learning vs Automated Data Science
We offer an interactive, decision tree-style tool, which examines the data you have and proposes a set of potentially appropriate visualizations to represent your dataset.
This article covers defining statistics, descriptive statistics, measures of central tendency, and measures of spread. This article assumes no prior knowledge of statistics, but does require at least a general knowledge of Python.
AI Knowledge Map: How To Classify AI Technologies
Topic Modeling with LSA, PLSA, LDA & lda2Vec
Data Visualization Cheat Sheet
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