Real-time face detection and emotion/gender classification; Top 20 #Python #MachineLearning Open Source Projects, updated; Stanford CS231n lecture slides: #DeepLearning software; #DataScience platforms are on the rise
This post summarizes and links to the individual tutorials which make up this introductory look at data science for newbies, mainly focusing on the tools, with a practical bent, written by a software engineer from the perspective of a software engineering approach.
Customer Analytics from Wharton Executive Education gives you a deeper, actionable understanding of your data by delving into specific collection methodologies and patterns for predictive behavior, empowering you to make impactful decisions that drive success throughout your company.
Nuts-ml is a new data pre-processing library in Python for GPU-based deep learning in vision. It provides common pre-processing functions as independent, reusable units. These so called ‘nuts’ can be freely arranged to build data flows that are efficient, easy to read and modify.
The influence of a Twitter user goes beyond the simple number of followers. We also want to examine how effective are tweets - how likely they are to be retweeted, favorited, or the links inside clicked upon. What exactly is an influential user depends on the definition.
Attend the leading event for predictive analytics case studies, expertise, and resources. With conferences strategically scheduled around the globe, you are sure to find a PAW event that will fit your calendar and specific needs. Sign up with code PATIMES17 for 15% off two day and combo passes.
With an explosive growth in the number of transactions, detecting fraud cannot be done manually and Machine Learning-based methods are required. We examine what are the main challenges for using Machine Learning for Trust.
New Leader, Trends, and Surprises in Analytics, Data Science, Machine Learning Software Poll; Machine Learning Crash Course: Part 1; Text Mining 101: Mining Information From A Resume; Data science platforms are on the rise and IBM is leading the way; An Introduction to the MXNet Python API
This post is the first in a series of tutorials for implementing machine learning workflows in Python from scratch, covering the coding of algorithms and related tools from the ground up. The end result will be a handcrafted ML toolkit. This post starts things off with data preparation.
News, sentiment, and emotion drive markets - consumer markets and financial markets, making text and sentiment analysis essential tools for research and insights. Using code KDNUGGETS to save - early reg by May 31.
This post outlines an entire 6-part tutorial series on the MXNet deep learning library and its Python API. In-depth and descriptive, this is a great guide for anyone looking to start leveraging this powerful neural network library.
Download the 2017 Gartner Magic Quadrant for Data Science Platforms today to learn why IBM is named a leader in data science and to find out why data science, analytics, and machine learning are the engines of the future.
Data Science projects involve iterative processes and may need changes in data at every iteration. But Data versioning, data pipelines and data workflows make Data Scientist’s life easy, let’s see how.
A meticulously compiled list as extensive as possible of every accelerator, incubator or program the author has read or bumped into over the past months. It looks like there are at least 29 of them. An interesting read for a wide variety of potentially interested parties - far beyond only the investor.
Successful analytics at the organizational-level starts with immersive, interactive training and goal-driven strategy. TMA’s live online and classroom training spans all skill levels and analytic team roles to build analytic leaders. Live Online in June, Seattle in July and Wash-DC in October.
This post, the first in a series of ML tutorials, aims to make machine learning accessible to anyone willing to learn. We’ve designed it to give you a solid understanding of how ML algorithms work as well as provide you the knowledge to harness it in your projects.
The Saint Mary's College Master of Science in Data Science program will prepare you to enter into the data analysis process at any stage, from the initial formulation of the question, to visualizing data, to interpreting the results and drawing conclusions.
Check the new “AI, Analytics and GDPR Survey 2017”, where Insurance Nexus quizzed 250 of the brightest minds in insurance, and learn the latest trends in analytics, AI and GDPR to help you adjust your strategy.
Getting Into Data Science: What You Need to Know; The Best Python Packages for Data Science; HDFS vs. HBase : All you need to know; What are common data quality issues for Big Data and how to handle them?; Teaching the Data Science Process
This report is the second in a series analyzing data science related topics. This time around, specifically, we rank 15 top Python data science packages, hopefully with results of use to the data science community.
This upcoming 45-minute webinar explores efficient methods to explore and organize complex data, how to marry multiple datasets for feature engineering, and optimal target selection and how to address information leakage.
Moving to Hadoop is not without its challenges—there are so many options, from tools to approaches, that can have a significant impact on the future success of a business’ strategy. Data management and data pipelining can be particularly difficult.
Understanding the process requires not only wide technical background in machine learning but also basic notions of businesses administration; here I will share my experience on teaching the data science process.
More than 400 of the sharpest minds in the industry will meet at Postgres Vision June 26-28 in Boston. The goal is to envision the future for enterprises striving to harvest greater strategic value and actionable insight from their data.
Data science projects may often fail due to a lack of clear definition of the business goal and not because data scientists technical abilities. We examine the connection between data science and research design to help address this issue.
Hadoop Distributed File System (HDFS), and Hbase (Hadoop database) are key components of Big Data ecosystem. This blog explains the difference between HDFS and HBase with real-life use cases where they are best fit.
New Poll: What software you used for Analytics, Data Mining, Data Science, Machine Learning projects in the past 12 months?; Using Deep Learning To Extract Knowledge From Job Descriptions; Deep Learning Past, Present, and Future; The Guerrilla Guide to Machine Learning with R
The courses cover topics such as Neural Networks and Deep Learning, Bayesian Networks, Big Data with Apache Spark, Bayesian Inference, Text Mining and Time Series, and each has theoretical as well as practical classes, done with R or Python. Early bird till June 5.
In short, you reach different resting placing with different SGD algorithms. That is, different SGDs just give you differing convergence rates due to different strategies, but we do expect that they all end up at the same results!
We introduce a new library for doing distributed hyperparameter optimization with Scikit-Learn estimators. We compare it to the existing Scikit-Learn implementations, and discuss when it may be useful compared to other approaches.
Stanford Data Mining Courses and Certificates are designed to give you the skills you need to gather and analyze massive amounts of information, and to translate that information into actionable business strategies. Enroll until June 18.
Explore the latest advancements in deep learning and their applications in industry at the Deep Learning in Finance Summit and Deep Learning in Retail Summit in London, 1-2 June. Use discount code KDNUGGETS to save 20% off all tickets.
Cloud computing is the next evolutionary step in Internet-based computing, which provides the means for delivering ICT resources as a service. Internet-of-Things can benefit from the scalability, performance and pay-as-you-go nature of cloud computing infrastructures.
Learn how to get started with predictive modeling and overcome strategic and tactical limitations that cause data mining projects to fall short of their potential. Next webinars are May 16 and June 14.
This report, created by analyzing millions of job postings using advanced technology, divides Data Science and Analytics roles into 6 broad categories, and answers many questions, including cities, industries, job roles with most growth.
In this month's installment of Machine Learning Projects You Can No Longer Overlook, we find some data preparation and exploration tools, a (the?) reinforcement learning "framework," a new automated machine learning library, and yet another distributed deep learning library.
Learn how to master Machine Learning by understanding the theory behind. MLTrain also teaches the concepts and helpful tricks of key systems like TensorFlow and how to code machine learning algorithms using it.
We present a deep learning approach to extract knowledge from a large amount of data from the recruitment space. A learning to rank approach is followed to train a convolutional neural network to generate job title and job description embeddings.
Without knowing the ground truth of a dataset, then, how do we know what the optimal number of data clusters are? We will have a look at 2 particular popular methods for attempting to answer this question: the elbow method and the silhouette method.
Predictive Analytics World for Business focuses on concrete examples of deployed predictive analytics. Join us May 14-18 in San Francisco to learn how Fortune 500 analytics competitors and other top practitioners deploy predictive modeling and machine learning, and the kind of business results they achieve.
Data Insight Leaders Summit is 2 value packed days with the most senior speaker faculty of strictly Head’s of Data Science, Advanced Analytics and Business Intelligence, 18-19 October 2017 in Barcelona.
How to Learn Machine Learning in 10 Days; Deep Learning – Past, Present, and Future; Keep it simple! How to understand Gradient Descent algorithm; New Poll: What software you used for Analytics, Data Mining, Data Science, Machine Learning projects in the past 12 months?
Vote in KDnuggets 18th Annual Poll: What software you used for Analytics, Data Mining, Data Science, Machine Learning projects in the past 12 months? We will clean, analyze, visualize, and publish the results.
Check out this Python deep learning virtual machine image, built on top of Ubuntu, which includes a number of machine learning tools and libraries, along with several projects to get up and running with right away.
Why Momentum Really Works; O'Reilly's Hands-On Machine Learning with Scikit-Learn and TensorFlow; Implemented BEGAN and saw a cute face at iteration 168k; Self-driving car course; Exploring the mysteries of Go; DeepMind Solves AGI
RE•WORK would like to update KDnuggets readers on their upcoming European events, as discounted tickets end next week, and share their on-demand content and expert interviews! For 20% off pass prices for all RE•WORK events, use discount code KDNUGGETS.
This post summarizes nine creative ways to condemn almost any AI startup to bankruptcy. I focus on data science and machine learning startups, but the lessons on what to avoid can easily be applied to other industries.
Face Recognition with Python, in under 25 lines of code; Try #DeepLearning in #Python w. a fully pre-configured VM; Homo Bayesians #MachineLearning #humor #cartoon; The Most Popular Language For #MachineLearning, #DataScience Is ...
The top machine learning videos on YouTube include lecture series from Stanford and Caltech, Google Tech Talks on deep learning, using machine learning to play Mario and Hearthstone, and detecting NHL goals from live streams.
Coming soon: TDWI Chicago Conference, PAW San Francisco, #BAChicago, Apache: Big Data Miami, Train AI SanFran, Strata + Hadoop World London, Deep Learning Summit Boston, Spark Summit San Francisco, Postgres Vision Boston, and more.
Use code KDPV17 to save on Postgres Vision, June 26-28, 2017, at the Royal Sonesta Boston. Co-hosted by EnterpriseDB and MIT, the event sponsors include Amazon Web Services, Avnet, credativ, EnterpriseDB, IBM, Microsoft, MIT, NEC, Palisade Compliance, Quest, TechData, and The Executive Council.
Built for speed and scalability, DataRobot radically reduces the time of data science projects - from data to deployment, enabling organizations to bring products to market and react to changing conditions faster. Learn more in June 6 webinar and live demo.
There is a lot of buzz around deep learning technology. First developed in the 1940s, deep learning was meant to simulate neural networks found in brains, but in the last decade 3 key developments have unleashed its potential.
While programming languages will never be completely obsolete, a growing number of programmers (and data scientists) prefer working with frameworks and view them as the more modern and cutting-edge option for a number of reasons.
The Guerrilla Guide to Machine Learning with Python; How to understand Gradient Descent algorithm; Cartoon: Machine Learning – What They Think I Do; AI & Machine Learning Black Boxes: The Need for Transparency and Accountability; How to Build a Recurrent Neural Network in TensorFlow
For the third year in a row, CrowdFlower surveyed data scientists (nearly 200 this year) from all manner of organizations, which they have compiled into one free report which you can be downloaded now. This year, lots of insights into the word of AI are included.
10 days may not seem like a lot of time, but with proper self-discipline and time-management, 10 days can provide enough time to gain a survey of the basic of machine learning, and even allow a new practitioner to apply some of these skills to their own project.