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!
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!
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.
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.
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!
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
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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
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.