On May 9th, Figure Eight's co-founder Lukas Biewald will lead an Introduction to Deep Learning workshop in San Francisco. Don't miss this chance to learn from the best in AI. Use code KDNuggets30 to get 30% off the registration fee!
In this article, we describe seven key areas to take into account for successful operationalization and lifecycle management (MLOps) of your ML initiatives
Also: Choosing the Right Metric for Evaluating Machine Learning Models – Part 1; Top 16 Open Source Deep Learning Libraries and Platforms; Data Science Interview Guide; Why so many data scientists are leaving their jobs
I recently was a guest speaker at the Stanford AI Salon on the topic of accessiblity in AI, which included a free-ranging discussion among assembled members of the Stanford AI Lab. There were a number of interesting questions and topics, so I thought I would share a few of my answers here.
For workstation development platforms purpose-built for Tensorflow, PyTorch, Caffe2, MXNet, and other DL frameworks, the solution is BOXX. We're bringing deep learning to your deskside with the all-new APEXX W3!
Each machine learning model is trying to solve a problem with a different objective using a different dataset and hence, it is important to understand the context before choosing a metric.
It wasn’t until I wrote my own simple Blockchain, that I truly understood what it is and the potential applications for it. So without further ado, lets set up our 7 functions!
The Summits will bring together 550 experts and 60 speakers using AI and deep learning to improve operations in manufacturing, and creating the next generation of intelligent robots. Save 20% with code KDNUGGETS.
KDnuggets poll finds that Machine Learning Engineer, Researcher, and Data Scientist have the highest job satisfaction. Job satisfaction usually starts high, but drops significantly after 4 years on the job. Data professionals in Asia and Latin America are most unsatisfied.
Most people don’t realize, but the actual “fancy” machine learning algorithm is like the last mile of the marathon. There is so much that must be done before you get there!
IBM unveiled the updated Db2 Event Store platform and various features at its Think 2018 conference, tailored for those in the data industry, including data scientists, and application developers.
Learn how you can improve performance and optimize resources using Looker + AWS and Amazon Redshift with Looker extensive pre-built analytics models for AWS data. As a bonus, we will give 1K credit towards AWS data warehouse.
This article covers the transformation of public emotions, big news and blockchain data into signals which can provide us with a better understanding as well as instructions for investing.
Traditionally, Data Science would focus on mathematics, computer science and domain expertise. While I will briefly cover some computer science fundamentals, the bulk of this blog will mostly cover the mathematical basics one might either need to brush up on (or even take an entire course).
Understanding and quantifying a customer's journey - otherwise known as marketing attribution - is essential for marketers to analyze the ROI from campaigns. Get the latest guidebook to understand how its done!
We bring to you the top 16 open source deep learning libraries and platforms. TensorFlow is out in front as the undisputed number one, with Keras and Caffe completing the top three.
This Friday, April 27 is the regular pricing deadline for Mega-PAW 2018, June 3-7 in Las Vegas. Don't miss your chance to save up to $450.00 when you register for the 2018 event.
This article covers how an ever-increasing amount of data will trigger the evolution of a new ecosystem that will spur entrepreneurial activity, offering an opportunity to start a wide range of new businesses.
Also: Key Algorithms and Statistical Models for Aspiring Data Scientists; Why Deep Learning is perfect for NLP; Derivation of Convolutional Neural Network from Fully Connected Network Step-By-Step; Top 8 Free Must-Read Books on Deep Learning
This program gives data scientists a way to verify their proficiency, and organizations an independent standard for qualifying current and prospective data science experts. Register now!
Deep learning brings multiple benefits in learning multiple levels of representation of natural language. Here we will cover the motivation of using deep learning and distributed representation for NLP, word embeddings and several methods to perform word embeddings, and applications.
With the growth of AI systems and unstructured data, there is a need for an independent means of data curation, evaluation and measurement of output that does not depend on the natural language constructs of AI and creates a comparative method of how the data is processed.
Presto enables data scientists to run interactive SQL across multiple data sources. This open source engine supports querying anything, anywhere, and at large scale.
The tough thing about learning data is remembering all the syntax. While at Dataquest we advocate getting used to consulting the Python documentation, sometimes it's nice to have a handy reference, so we've put together this cheat sheet to help you out!
What are the advantages of ConvNets over FC networks in image analysis? How is ConvNet derived from FC networks? Where the term convolution in CNNs came from? These questions are to be answered in this article.
Also: Don’t learn #MachineLearning in 24 hours; Top 8 Free Must-Read Books on #DeepLearning; How Attractive Are You in the Eyes of Deep #NeuralNetwork?; Ten #MachineLearning Algorithms You Should Know to Become a #DataScientist
The WCAI annual conference, Successful Applications of Customer Analytics is dedicated to real-world applications that balance high-level rigor and business know-how, and to elevating the role of analytics in an organization strategic decision-making.
First part on a full discussion on how to do Distributed Deep Learning with Apache Spark. This part: What is Spark, basics on Spark+DL and a little more.
We offer a step-by-step guide to technical content and related assets that to help you learn Apache Spark, whether you're getting started with Spark or are an accomplished developer.
Insurance AI & Analytics USA Summit is bringing together 400+ insurance innovators and AI pioneers to discuss the operational advantages of rolling out AI in your business, and then delve into the specifics of pricing, marketing, claims and underwriting. Save with code KDNuggets.
It is vital to have a good understanding of the mathematical foundations to be proficient with data science. With that in mind, here are seven books that can help.
In this article, I will discuss the significance of IoT and gain insights on why this technology is becoming an integral part of the daily learning and teaching methodologies.
We propose a framework of "trust heatmap", show how the trust in machines depends on two key elements: their error rate and the costs of mistakes, and examine the automation frontier.
Build essential technical, analytical, and leadership skills for today's data-driven world in Northwestern’s online MASTER OF SCIENCE IN DATA SCIENCE program.
Also: Ten Machine Learning Algorithms You Should Know to Become a Data Scientist; Top 10 Technology Trends of 2018; 12 Useful Things to Know About Machine Learning
Don't miss the opportunity to witness keynote sessions by industry heavyweights at the upcoming Predictive Analytics World for Manufacturing conference in Las Vegas. Save hundreds by registering by April 27.
This article provides a summary of key algorithms and statistical techniques commonly used in industry, along with a short resource related to these techniques.
Libraries like Keras simplify the construction of neural networks, but are they impeding on practitioners full understanding? Or are they simply useful (and inevitable) abstractions?
In this article, we will focus on the modern trends that took off well on the market by the end of 2017 and discuss the major breakthroughs expected in 2018.
Machine Learning Engineer, with avg. salary of $136K and Data Scientist, with avg. salary $133K are among the top US jobs in 2018, according to job site Indeed.
Join us on Apr 19 for an interactive virtual event to hear from a panel of analytic experts as they dispel the myths and dive into the nitty-gritty of how AI and machine learning will impact analytic teams.
Machine Learning's popularity is continuing to grow and has engraved itself in pretty much every industry. This article contains lessons from a data scientist on how to unlock it's full potential.
This is a summary of 12 key lessons that machine learning researchers and practitioners have learned include pitfalls to avoid, important issues to focus on and answers to common questions.
Only small fraction of data is actually analyzed, with text, audio, and video largely unused. Drexel online MS in Business Analytics will teach you to analyze this overlooked data to give your company and yourself a competitive edge.
AI is completely transforming every corner of the healthcare industry. Don’t miss out on IEN’s 2nd Annual AI in Healthcare Summit, Jun 11-12, in San Francisco.
It's important for data scientists to have a broad range of knowledge, keeping themselves updated with the latest trends. With that being said, we take a look at the top 10 machine learning algorithms every data scientist should know.
PyTorch has emerged as a major contender in the race to be the king of deep learning frameworks. What makes it really luring is it’s dynamic computation graph paradigm.
Learn how to optimize your models by leveraging robust data sets that improve performance; avoiding endless feature engineering and overfitting; and other useful steps.
Deep Learning is the newest trend coming out of Machine Learning, but what exactly is it? And how do I learn more? With that in mind, here's a list of 8 free books on deep learning.
Just like we discussed in the CBOW model, we need to model this Skip-gram architecture now as a deep learning classification model such that we take in the target word as our input and try to predict the context words.
Don't miss the opportunity to witness keynote sessions by industry heavyweights at the upcoming Predictive Analytics World for Financial conference in Las Vegas, Jun 3-7.
This book covers machine learning techniques from text using both bag-of-words and sequence-centric methods. The scope of coverage is vast, and it includes traditional information retrieval methods and also recent methods from neural networks and deep learning.
CRM/Consumer Analytics, Finance, and Banking are still the leading applications, but Health Care and Fraud Detection are gaining. Anti-spam, Manufacturing, and Social are the fastest growing sectors in 2017, while Oil / Gas / Energy and Social Networks analysis have declined.
Also: Supervised vs. Unsupervised Learning; Why Data Scientists Must Focus on Developing Product Sense; A Day in the Life of a Data Scientist: Part 4; How To Choose The Right Chart Type For Your Data
Despite the fact that an ETL task is pretty challenging when it comes to loading Big Data, there’s still the scenario in which you can load terabytes of data from Postgres into BigQuery relatively easy and very efficiently.
In an upcoming livestream on April 19, we’ll dig into how to build a foundation that supports AI and Machine Learning with industry experts and uncover what many companies are going through.
Data Scientists should focus on developing product sense to move fast and systematically, create models that are relevant and to able to know when to stop.
No other mean of data description is more comprehensive than Descriptive Statistics and with the ever increasing volumes of data and the era of low latency decision making needs, its relevance will only continue to increase.
Are automatic feature learning models (e.g. CNN) killing their previous manually engineered models? This is an important question that is to be answered in this article.
Udemy April $10.99 sale is now going on top courses from leading instructors and learn Machine Learning, Data Science, Python, Azure Machine Learning, and more.
Join DataRobot on Apr 26 at 1:00 pm EST for this webinar, in which industry expert Jen Underwood will show how you can use automated machine learning to quickly develop predictive models and advance your career beyond traditional business intelligence.
Each Netflix production is a logistical challenge that consumes and produces a vast amount of data. The tech giant is utilising this data to help them create new content and assist them at every stage, from pre-production to launching the show.
Get this eBook to learn key issues that hamper fragmented data science teams; how accelerate innovation via collaborative workspaces, and how top data science teams boosted productivity by up to 4x.
Rev is for data science leaders and practitioners, offering interactive sessions, stimulating conversations, and tutorials about how to run, manage, and accelerate data science as an organizational capability. Get early bird rates until April 15.
Coming soon: AnacondaCON Austin, QCon.ai SF, INFORMS Baltimore, AI Conference NYC, Data Science Salon Dallas, AI Expo Global London, ODSC Boston, and many more.
The power of charts to assist in accurate interpretation is massive and that's why it is vital to select the correct type when you are trying to visualize data.
In this article, we demonstrate the utility of using native NumPy file format .npy over CSV for reading large numerical data set. It may be an useful trick if the same CSV data file needs to be read many times.
Join DataRobot, Apr 19 at 2:00 pm ET/11:00 am PT, for a webinar on how to use Automated Data Preparation & Machine Learning to gain a competitive advantage, while quickly aligning your business operations to regulatory requirements.
Also: Using Tensorflow Object Detection to do Pixel Wise Classification; Understanding Feature Engineering: Deep Learning Methods for Text Data; Exploring DeepFakes; Top 20 Python AI and Machine Learning Open Source Projects
Here are the steps you need to obtain your first job in data science, including details on how to create a good portfolio, key networking tips, getting the right education and managing expectations.
Interested in what a data scientist does on a typical day of work? Each data science role may be different, but these contributors have insight to help those interested in figuring out what a day in the life of a data scientist actually looks like.
Choosing the Right Metric for Evaluating Machine Learning Models – Part 1
Blockchain Explained in 7 Python Functions
7 Books to Grasp Mathematical Foundations of Data Science and Machine Learning
| 
