Analytics Vidhya

SEPTEMBER 18, 2019

Overview Writing optimized Python code is a crucial piece in your data science skillset Here are four methods to optimize your Python code (with. The post 4 Unique Methods to Optimize your Python Code for Data Science appeared first on Analytics Vidhya.

Data Science 307

Data Science Python Analytics Analytics 307

KDnuggets

SEPTEMBER 17, 2019

We identify two main groups of Data Science skills: A: 13 core, stable skills that most respondents have and B: a group of hot, emerging skills that most do not have (yet) but want to add. See our detailed analysis.

Data Science 307

Data Science Data Visualization Deep Learning Deep Learning 307

Dataconomy

SEPTEMBER 17, 2019

Vice-President of XING’s Data Science team, Dr. Sébastien Foucaud, believes the time of blackbox AI is behind us. The market leader in the DACH region has a vision for its Machine Learning: it should be explainable to the boss, as well as to users. Focussing on German-speaking countries since 2012, The post A Simple and Transparent Machine Learning Approach Proves to Conquer the German Market appeared first on Dataconomy.

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Machine Learning Machine Learning Data Science AI 172

Smart Data Collective

SEPTEMBER 15, 2019

Big data is changing the financial industry in a truly astounding way. Countless financial professionals are looking towards machine learning and other new tools to improve the quality of the services that they offer to their customers. K. Hussain of Atos Spain published a white paper on the growing relevance of big data in the finance and insurance verticals.

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Generative AI is upending the way product developers & end-users alike are interacting with data. Despite the potential of AI, many are left with questions about the future of product development: How will AI impact my business and contribute to its success? What can product managers and developers expect in the future with the widespread adoption of AI?

AI

Analytics Vidhya

SEPTEMBER 16, 2019

Overview What is PyTorch? How can you get started with it from scratch? We’ll cover all of that in this article PyTorch is one. The post A Beginner-Friendly Guide to PyTorch and How it Works from Scratch appeared first on Analytics Vidhya.

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Analytics Analytics Deep Learning Deep Learning 307

KDnuggets

SEPTEMBER 17, 2019

Lately, varying improvements over BERT have been shown — and here I will contrast the main similarities and differences so you can choose which one to use in your research or application.

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Smart Data Collective

SEPTEMBER 19, 2019

Machine learning has played a very important role in the development of technology that has a large impact on our everyday lives. However, machine learning is also influencing the direction of technology that is not as commonplace. Text to speech technology is a prime example. Text to speech technology predates machine learning by over a century. However, machine learning has made the technology more reliable than ever.

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Machine Learning Machine Learning Algorithm Deep Learning 81

Analytics Vidhya

SEPTEMBER 15, 2019

Overview New to working with image data? The skimage module in Python is an ideal starting point Learn 8 simple yet powerful tricks for. The post 9 Powerful Tips and Tricks for Working with Image Data using skimage in Python appeared first on Analytics Vidhya.

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Python Analytics Analytics Deep Learning 269

KDnuggets

SEPTEMBER 17, 2019

The article contains a brief introduction of Bioinformatics and how a machine learning classification algorithm can be used to classify the type of cancer in each patient by their gene expressions.

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Machine Learning Machine Learning Algorithm Python 299

Data Science 101

SEPTEMBER 20, 2019

Just released this week, Nuts about Data , is a fun introductory book about the data science process. Meor Amer tells a witty story about squirrels, mining for nuts, teamwork, and survival. It brings together the entire data science lifecycle from asking questions to final storytelling. It is a quick read and really fun. I highly recommend it and hope you enjoy it.

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Data Science 80

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Many organizations today are unlocking the power of their data by using graph databases to feed downstream analytics, enahance visualizations, and more. Yet, when different graph nodes represent the same entity, graphs get messy. Watch this essential video with Senzing CEO Jeff Jonas on how adding entity resolution to a graph database condenses network graphs to improve analytics and save your analysts time.

Database

Smart Data Collective

SEPTEMBER 15, 2019

Big data has brought major changes to countless industries. Healthcare, finance, criminal justice, and manufacturing have all been touched by advances in big data. However, big data is also transforming other industries. The music industry is relying more on big data than ever. Darren Heitner wrote a great article on Inc. About the way that big data is revolutionizing the industry.

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Victor Zhou

SEPTEMBER 16, 2019

I finally started using Git more heavily a few years ago when I first began building some of my bigger side projects. Now, it’s true that typing git status and git push is pretty easy, but if you’ve got some Git experience you know some commands can get rather long. The one that always got me was: $ git commit --amend --no-edit This amends your staged changes into your most recent commit without changing its commit message (so Git won’t open a text editor!).

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KDnuggets

SEPTEMBER 16, 2019

The career path of the Data Scientist remains a hot target for many with its continuing high demand. Becoming one requires developing a broad set of skills including statistics, programming, and even business acumen. Learn more about one person's experience making this journey, and discover the many resources available to help you find your way into a world of data science.

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Data Scientist Data Science 298

DataRobot

SEPTEMBER 18, 2019

The truth is that the work of data scientists cannot generate value if the models never make it to production. For data scientists writing custom models in languages like Python and R, the number of challenges for getting models into production can be overwhelming. Issues range from how to deploy model code on production systems, how to monitor performance, and how to deploy updates to models over time.

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Data Scientist Python 16

Speaker: Scott Sehlhorst

We know we want to create products which our customers find to be valuable. Whether we label it as customer-centric or product-led depends on how long we've been doing product management. There are three challenges we face when doing this. The obvious challenge is figuring out what our users need; the non-obvious challenges are in creating a shared understanding of those needs and in sensing if what we're doing is meeting those needs.

Smart Data Collective

SEPTEMBER 18, 2019

I recently read a great post from The Verge on the impact of AI on the video gaming industry. Author Nick Statt made a great point about the evolution of AI in the industry. Pratt pointed out that AI has been a factor in the video game industry since the very beginning. Some of the AI tools that we see today resemble those in the 1980 game Rogue. Of course, AI has improved dramatically over the last 40 years.

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Artificial Intelligence Artificial Intelligence AI AI 77

KDnuggets

SEPTEMBER 20, 2019

With recent advances in AI being enabled through access to so much “Big Data” and cheap computing power, there is incredible momentum in the field. Can big data really deliver on all this hype, and what can go wrong?

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KDnuggets

SEPTEMBER 17, 2019

For some people anything below 60% is acceptable and for certain others, even a correlation of 30% to 40% is considered too high because it one variable may just end up exaggerating the performance of the model or completely messing up parameter estimates.

Analytics 266

Analytics Analytics 266

KDnuggets

SEPTEMBER 18, 2019

Algorithms are at the core of data science and sampling is a critical technical that can make or break a project. Learn more about the most common sampling techniques used, so you can select the best approach while working with your data.

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Speaker: Timothy Chan, PhD., Head of Data Science

Are you ready to move beyond the basics and take a deep dive into the cutting-edge techniques that are reshaping the landscape of experimentation? 🌐 From Sequential Testing to Multi-Armed Bandits, Switchback Experiments to Stratified Sampling, Timothy Chan, Data Science Lead, is here to unravel the mysteries of these powerful methodologies that are revolutionizing how we approach testing.

Data Science

KDnuggets

SEPTEMBER 20, 2019

This comprehensive tutorial aims to introduce the fundamentals of PyTorch building blocks for training neural networks.

Python 282

Python 282

KDnuggets

SEPTEMBER 19, 2019

While mature algorithms and extensive open-source libraries are widely available for machine learning practitioners, sufficient data to apply these techniques remains a core challenge. Discover how to leverage scikit-learn and other tools to generate synthetic data appropriate for optimizing and fine-tuning your models.

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KDnuggets

SEPTEMBER 18, 2019

Also: Cartoon: Unsupervised #MachineLearning?; Cartoon: Unsupervised Machine Learning ? How to Become More Marketable as a Data Scientist; Ensemble Methods for Machine Learning: AdaBoost.

Machine Learning 221

Machine Learning Machine Learning Data Scientist Python 221

KDnuggets

SEPTEMBER 20, 2019

When we create our machine learning models, a common task that falls on us is how to tune them. So that brings us to the quintessential question: Can we automate this process?

Machine Learning 215

Machine Learning Machine Learning 215

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Start-ups & SMBs launching products quickly must bundle dashboards, reports, & self-service analytics into apps. Customers expect rapid value from your product (time-to-value), data security, and access to advanced capabilities. Traditional Business Intelligence (BI) tools can provide valuable data analysis capabilities, but they have a barrier to entry that can stop small and midsize businesses from capitalizing on them.

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KDnuggets

SEPTEMBER 17, 2019

What other creative tools for data science beyond Python and R can you use to make an impression? It's not about the tool -- it's about its impact.

Data Science 215

Data Science Python Data Visualization 215

KDnuggets

SEPTEMBER 14, 2019

New KDnuggets Cartoon looks at one of the hottest directions in Machine Learning and asks can Machine Learning be too unsupervised?

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Machine Learning Machine Learning 227

KDnuggets

SEPTEMBER 19, 2019

“I want to learn machine learning and artificial intelligence, where do I start?” Here.

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KDnuggets

SEPTEMBER 18, 2019

This article covers the implementation of a data scraping and natural language processing project which had two parts: scrape as many posts from Reddit’s API as allowed &then use classification models to predict the origin of the posts.

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Speaker: Kevin Kai Wong, President of Emergent Energy Solutions

In today's industrial landscape, the pursuit of sustainable energy optimization and decarbonization has become paramount. Manufacturing corporations across the U.S. are facing the urgent need to align with decarbonization goals while enhancing efficiency and productivity. Unfortunately, the lack of comprehensive energy data poses a significant challenge for manufacturing managers striving to meet their targets.

Analytics

KDnuggets

SEPTEMBER 18, 2019

Support for Python 2 will expire on Jan. 1, 2020, after which the Python core language and many third-party packages will no longer be supported or maintained. Take this survey to help determine and share your level of preparation.

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Python 169

KDnuggets

SEPTEMBER 19, 2019

Check out this detailed tutorial on applying data science to the cybersecurity domain, written by an individual with backgrounds in both fields.

Data Science 189

Data Science Python Machine Learning Machine Learning 189

KDnuggets

SEPTEMBER 17, 2019

Join this technical webinar on Oct 3, where Domino Chief Data Scientist Josh Poduska will dive into popular open source and proprietary AutoML tools, and walk through hands-on examples of how to install and use these tools, so you can start using these technologies in your work right away.

Data Science 163

Data Science Data Scientist 163

KDnuggets

SEPTEMBER 16, 2019

How to turn a typical pytorch script into a scalable d6tflow DAG for faster research & development.

Deep Learning 218

Deep Learning Deep Learning Python 218

Speaker: Anne Steiner and David Laribee

As a concept, Developer Experience (DX) has gained significant attention in the tech industry. It emphasizes engineers’ efficiency and satisfaction during the product development process. As product managers, we need to understand how a good DX can contribute not only to the well-being of our development teams but also to the broader objectives of product success and customer satisfaction.