KDnuggets

NOVEMBER 22, 2019

If you are a new Data Scientists early in your professional journey, and you’re a bit confused and lost, then follow this advice to figure out how to best contribute to your company.

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Data Scientist 278

Analytics Vidhya

NOVEMBER 18, 2019

Overview Here’s a quick introduction to building machine learning pipelines using PySpark The ability to build these machine learning pipelines is a must-have skill. The post Want to Build Machine Learning Pipelines? A Quick Introduction using PySpark appeared first on Analytics Vidhya.

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Machine Learning Machine Learning Analytics Analytics 293

Dataconomy

NOVEMBER 21, 2019

The era of everything-as-a-service (XaaS) has provided both an opportunity and a challenge for companies across industries. The XaaS model, a subscription-based solution that makes cloud-based applications available on demand unlike the traditional license-based platforms of the past, delivers several noteworthy advantages over its predecessors. Between cost reductions and easier.

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Internet of Things 119

Smart Data Collective

NOVEMBER 21, 2019

Big data is transforming the daily realities of running a business. Companies can use big data to handle certain tasks more quickly and cost-effectively than ever. Vince Campisi, CIO of GE Software, Ash Gupta, an executive with American Express, and many other companies use big data to get a competitive advantage. Of course, big data also raises some new challenges.

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Big Data Big Data Predictive Analytics Analytics 111

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Many software teams have migrated their testing and production workloads to the cloud, yet development environments often remain tied to outdated local setups, limiting efficiency and growth. This is where Coder comes in. In our 101 Coder webinar, you’ll explore how cloud-based development environments can unlock new levels of productivity. Discover how to transition from local setups to a secure, cloud-powered ecosystem with ease.

KDnuggets

NOVEMBER 22, 2019

To demonstrate the implementation complexity differences along the AutoML highway, let's have a look at how 3 specific software projects approach the implementation of just such an AutoML "solution," namely Keras Tuner, AutoKeras, and automl-gs.

Machine Learning 282

Machine Learning Machine Learning Python 282

Analytics Vidhya

NOVEMBER 19, 2019

Overview The attention mechanism has changed the way we work with deep learning algorithms Fields like Natural Language Processing (NLP) and even Computer Vision. The post A Comprehensive Guide to Attention Mechanism in Deep Learning for Everyone appeared first on Analytics Vidhya.

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Deep Learning Deep Learning Natural Language Processing Algorithm 283

Smart Data Collective

NOVEMBER 22, 2019

Big data is transforming many facets of our lives. One of the ways consumers are looking to big data is with the student loan crisis. Big data advances could also make the government more understanding with its student loan forgiveness program. Big Data Could Turn the Student Loan Crisis on its Head. There are multiple applications of big data for solving the student loan crisis.

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Big Data Big Data 98

KDnuggets

NOVEMBER 21, 2019

This tutorial will take you through two options that have automated the geocoding process for the user using Python, Selenium and Google Geocoding API.

Python 295

Python 295

Explosion

NOVEMBER 21, 2019

In 2016 we trained a sense2vec model on the 2015 portion of the Reddit comments corpus, leading to a useful library and one of our most popular demos. That work is now due for an update. In this post, we present a new version of the library, new vectors, new evaluation recipes, and a demo NER project that we trained to usable accuracy in just a few hours.

Natural Language Processing 52

Natural Language Processing Data Scientist Machine Learning Machine Learning 52

Data Science 101

NOVEMBER 22, 2019

Here are this week’s news and announcements related to Cloud Data Science. Plus, there are some links for Videos and Tutorials. Announcements. Google Introduces Explainable AI Many industries require a level of interpretability for their machine learning models. Black box solutions are not always ok. Google is launching Explainable AI which quantifies the impact of the various factors of the data as well as the existing limitations.

Cloud Data 99

Cloud Data Data Science Azure AWS 99

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Large enterprises face unique challenges in optimizing their Business Intelligence (BI) output due to the sheer scale and complexity of their operations. Unlike smaller organizations, where basic BI features and simple dashboards might suffice, enterprises must manage vast amounts of data from diverse sources. What are the top modern BI use cases for enterprise businesses to help you get a leg up on the competition?

Business Intelligence

Shreyansh Singh

NOVEMBER 19, 2019

A bit late for writeups, but still here are the solutions to the challenges I solved during the CTF. The CTF was from 15 Nov. 2019, 22:30 IST — Mon, 18 Nov. 2019, 10:30 IST. It was a decent CTF with quality challenges, from both beginner to advanced level. Update : The scripts to solve and the flags are present in this repo. I’ll do the writeups category-wise - Crypto pre-legend — 100 pts 9EEADi^⁸:E9F3]4@>⁴=2J32==^D@>6E9:?

Database 52

Database Python 52

KDnuggets

NOVEMBER 19, 2019

This post will be dedicated to explaining the maths behind Bayes Theorem, when its application makes sense, and its differences with Maximum Likelihood.

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Explosion

NOVEMBER 21, 2019

In 2016 we trained a sense2vec model on the 2015 portion of the Reddit comments corpus, leading to a useful library and one of our most popular demos. That work is now due for an update. In this post, we present a new version and a demo NER project that we trained to usable accuracy in just a few hours.

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Data Science 101

NOVEMBER 18, 2019

After receiving some interest, I have decided to open up the posting more to the data science community. There are more details on the Contribute Page. If there is enough interest, I will be posting community contributed posts on Wednesdays and sponsored posts on Thursdays. What is the difference? Community contributed posts are free and are intended for individuals.

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Data Science 93

Speaker: Mike Rizzo, Founder & CEO, MarketingOps.com and Darrell Alfonso, Director of Marketing Strategy and Operations, Indeed.com

Though rarely in the spotlight, marketing operations are the backbone of the efficiency, scalability, and alignment that define top-performing marketing teams. In this exclusive webinar led by industry visionaries Mike Rizzo and Darrell Alfonso, we’re giving marketing operations the recognition they deserve! We will dive into the 7 P Model —a powerful framework designed to assess and optimize your marketing operations function.

DataCentric podcast

NOVEMBER 19, 2019

Arm's Mohamed Awad, as VP in Arm's Infrastructure group, is front-and-center in their architecture's invasion into enterprise compute. Let's look at where ARM stands in the enterprise today: Nearly every tier-1 OEM has an Arm server offering Every major public cloud vendor offers Arm instances NVIDIA, Marvell, Fujitsu, & Ampere jointed announced an Arm-based Super Computer reference platform at SC19 this week As we look forward towards a 5G-enabled edge, Arm is finding itself

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KDnuggets

NOVEMBER 21, 2019

This blog shows how text data representations can be used to build a classifier to predict a developer’s deep learning framework of choice based on the code that they wrote, via examples of TensorFlow and PyTorch projects.

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Deep Learning Deep Learning Data Preparation 231

DataRobot

NOVEMBER 22, 2019

The recent uproar surrounding the credit scoring algorithm employed by the Apple Card presents an opportunity to review the manifold types of biases that can affect AI algorithms, the possible consequences of neglecting such biases, and the best practices for allowing companies to develop processes to ensure that AI bias problems are adequately addressed.

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AI AI Algorithm 11

Data Science 101

NOVEMBER 19, 2019

Introduction to Probability by Joseph Blitzstein and Jessica Hwang is available as a free PDF download.

Data Science 89

Data Science 89

Speaker: Jay Allardyce, Deepak Vittal, and Terrence Sheflin

As we look ahead to 2025, business intelligence and data analytics are set to play pivotal roles in shaping success. Organizations are already starting to face a host of transformative trends as the year comes to a close, including the integration of AI in data analytics, an increased emphasis on real-time data insights, and the growing importance of user experience in BI solutions.

Business Intelligence

DataCentric podcast

NOVEMBER 18, 2019

As Hewlett Packard Enterprise launches its bare-metal container platform at KubeCon this week, Moor Insights & Strategy analysts Matt Kimball and Steve McDowell have a conversation with HPE VP and Chief Cloud Strategist Robert Christiansen. The guys talk about how HPE views cloud workloads, and how the power of Kubernetes and containers might just be the right answer for both cloud and edge.

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KDnuggets

NOVEMBER 22, 2019

We will focus here exactly on that part of the analysis that transforms words into numbers and texts into number vectors: text encoding.

Analytics 272

Analytics Analytics 272

DataRobot

NOVEMBER 21, 2019

In all segments of commercial banking competition is more intense and top line growth harder to achieve than ever before.

AI 15

AI AI 15

KDnuggets

NOVEMBER 21, 2019

This article aims to explain why this drive towards the use of notebooks in production is an anti pattern, giving some suggestions along the way.

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Python 265

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Cloud Development Environments (CDEs) are changing how software teams work by moving development to the cloud. Our Cloud Development Environment Adoption Report gathers insights from 223 developers and business leaders, uncovering key trends in CDE adoption. With 66% of large organizations already using CDEs, these platforms are quickly becoming essential to modern development practices.

KDnuggets

NOVEMBER 21, 2019

Autoencoders can be a very powerful tool for leveraging unlabeled data to solve a variety of problem, such as learning a "feature extractor" that helps build powerful classifiers, finding anomalies, or doing a Missing Value Imputation.

Machine Learning 213

Machine Learning Machine Learning 213

KDnuggets

NOVEMBER 18, 2019

When training a neural network in deep learning, its performance on processing new data is key. Improving the model's ability to generalize relies on preventing overfitting using these important methods.

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Deep Learning Deep Learning 216

KDnuggets

NOVEMBER 21, 2019

Also: Bring the scientific rigor of reproducibility to your Data Science projects; Neutrinos Lead to Unexpected Discovery in Basic Math ; The media gets really excited about AI. Maybe a bit too excited.

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Data Science AI AI 209

KDnuggets

NOVEMBER 20, 2019

Your spectacularly-performing machine learning model could be subject to the common culprits of class imbalance and missing labels. Learn how to handle these challenges with techniques that remain open areas of new research for addressing real-world machine learning problems.

Machine Learning 199

Machine Learning Machine Learning Data Preparation 199

Speaker: Nikhil Joshi, Founder & President of Snic Solutions

Is your manufacturing operation reaching its efficiency potential? A Manufacturing Execution System (MES) could be the game-changer, helping you reduce waste, cut costs, and lower your carbon footprint. Join Nikhil Joshi, Founder & President of Snic Solutions, in this value-packed webinar as he breaks down how MES can drive operational excellence and sustainability.

KDnuggets

NOVEMBER 19, 2019

As cornerstones of scientific processes, reproducibility and replicability ensure results can be verified and trusted. These two concepts are also crucial in data science, and as a data scientist, you must follow the same rigor and standards in your projects.

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Data Science Data Scientist 196

KDnuggets

NOVEMBER 19, 2019

In this article, we are going to talk about popular languages for Data Science and briefly describe each of them.

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Data Science Python 230

KDnuggets

NOVEMBER 20, 2019

In this blog I will be demonstrating how deep learning can be applied even if we don’t have enough data.

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Deep Learning Deep Learning 213

KDnuggets

NOVEMBER 21, 2019

Brush up on your Python basics with this post on creating, using, and manipulating tuples.

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Python 228

Speaker: Anindo Banerjea, CTO at Civio & Tony Karrer, CTO at Aggregage

When developing a Gen AI application, one of the most significant challenges is improving accuracy. This can be especially difficult when working with a large data corpus, and as the complexity of the task increases. The number of use cases/corner cases that the system is expected to handle essentially explodes. 💥 Anindo Banerjea is here to showcase his significant experience building AI/ML SaaS applications as he walks us through the current problems his company, Civio, is solving.

AI