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 307

Machine Learning Machine Learning Python 307

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.

Machine Learning 305

Machine Learning Machine Learning Analytics Analytics 305

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.

Internet of Things 119

Internet of Things 119

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.

Big Data 113

Big Data Big Data 113

Speaker: Tamara Fingerlin, Developer Advocate

Apache Airflow® 3.0, the most anticipated Airflow release yet, officially launched this April. As the de facto standard for data orchestration, Airflow is trusted by over 77,000 organizations to power everything from advanced analytics to production AI and MLOps. With the 3.0 release, the top-requested features from the community were delivered, including a revamped UI for easier navigation, stronger security, and greater flexibility to run tasks anywhere at any time.

Analytics

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 305

Analytics Analytics 305

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.

Deep Learning 288

Deep Learning Deep Learning Natural Language Processing Algorithm 288

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

Python 301

Python 301

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

Speaker: Alex Salazar, CEO & Co-Founder @ Arcade | Nate Barbettini, Founding Engineer @ Arcade | Tony Karrer, Founder & CTO @ Aggregage

There’s a lot of noise surrounding the ability of AI agents to connect to your tools, systems and data. But building an AI application into a reliable, secure workflow agent isn’t as simple as plugging in an API. As an engineering leader, it can be challenging to make sense of this evolving landscape, but agent tooling provides such high value that it’s critical we figure out how to move forward.

AI

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.

Algorithm 11

Algorithm AI AI 11

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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298

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

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.

Data Science 93

Data Science 93

Speaker: Andrew Skoog, Founder of MachinistX & President of Hexis Representatives

Manufacturing is evolving, and the right technology can empower—not replace—your workforce. Smart automation and AI-driven software are revolutionizing decision-making, optimizing processes, and improving efficiency. But how do you implement these tools with confidence and ensure they complement human expertise rather than override it? Join industry expert Andrew Skoog as he explores how manufacturers can leverage automation to enhance operations, streamline workflows, and make smarter, data-dri

AI

DataRobot

NOVEMBER 20, 2019

The National Association of REALTORS® is America’s largest trade association, representing over 1.4 million members around the country. Members include brokers, salespeople, property managers, counselors, and others engaged in all aspects of the real estate industry. With such a large membership to serve, it’s important that the association deliver value and serve its members by making more optimal decisions.

AI 9

AI AI 9

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.

Data Scientist 297

Data Scientist 297

Explosion

NOVEMBER 21, 2019

New features and improvements Completely rewrite package from scratch. Replace built-in vector storage with spaCy's Vectors, making this package a pure Python package and allowing easy out-o.

Python 40

Python 40

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: Frank Taliano

Documents are the backbone of enterprise operations, but they are also a common source of inefficiency. From buried insights to manual handoffs, document-based workflows can quietly stall decision-making and drain resources. For large, complex organizations, legacy systems and siloed processes create friction that AI is uniquely positioned to resolve.

AI

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

40

40

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.

Python 277

Python 277

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.

40

40

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.

Big Data 139

Big Data Big Data Predictive Analytics Analytics 139

Speaker: Chris Townsend, VP of Product Marketing, Wellspring

Over the past decade, companies have embraced innovation with enthusiasm—Chief Innovation Officers have been hired, and in-house incubators, accelerators, and co-creation labs have been launched. CEOs have spoken with passion about “making everyone an innovator” and the need “to disrupt our own business.” But after years of experimentation, senior leaders are asking: Is this still just an experiment, or are we in it for the long haul?

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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40

KDnuggets

NOVEMBER 19, 2019

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

Data Science 268

Data Science Python 268

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.

Deep Learning 267

Deep Learning Deep Learning Data Preparation 267

KDnuggets

NOVEMBER 21, 2019

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

Python 262

Python 262

Speaker: Ben Epstein, Stealth Founder & CTO | Tony Karrer, Founder & CTO, Aggregage

When tasked with building a fundamentally new product line with deeper insights than previously achievable for a high-value client, Ben Epstein and his team faced a significant challenge: how to harness LLMs to produce consistent, high-accuracy outputs at scale. In this new session, Ben will share how he and his team engineered a system (based on proven software engineering approaches) that employs reproducible test variations (via temperature 0 and fixed seeds), and enables non-LLM evaluation m

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.

Deep Learning 235

Deep Learning Deep Learning 235

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.

Data Science 231

Data Science AI AI 231

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 223

Machine Learning Machine Learning 223

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.

Deep Learning 223

Deep Learning Deep Learning 223

Speaker: Tamara Fingerlin, Developer Advocate

In this new webinar, Tamara Fingerlin, Developer Advocate, will walk you through many Airflow best practices and advanced features that can help you make your pipelines more manageable, adaptive, and robust. She'll focus on how to write best-in-class Airflow DAGs using the latest Airflow features like dynamic task mapping and data-driven scheduling!