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Algorithms for text analytics must model how language works to incorporate meaning in language—and so do the people deploying these algorithms. Bender & Lascarides 2019 is an accessible overview of what the field of linguistics can teach NLP about how meaning is encoded in human languages.
In this article I would like to focus on how companies can start their data-centric strategies and how to achieve success in their data transformation journeys. Have tried to share my thoughts why companies have to consider data at its epitome for their growth, for being competitive, for being smarter, innovative and be prepared for any unforeseen market surprises.
With more advancements in AI, it might be time to replace the age-old Turing Test with something better to determine if a machine is thinking. Specifically, a more modern approach might include standard questions designed to probe various facets of intelligence, and comparing the computer to a spectrum of human respondents of different ages, sexes, backgrounds, and abilities.
With ML models serving real people, misclassified cases (which are a natural consequence of using ML) are affecting peoples’ lives and sometimes treating them very unfairly. It makes the ability to explain your models’ predictions a requirement rather than just a nice to have.
Regularization techniques are crucial for preventing your models from overfitting and enables them perform better on your validation and test sets. This guide provides a thorough overview with code of four key approaches you can use for regularization in TensorFlow.
Here we look at some ways to interchangeably work with Python, PySpark and SQL using Azure Databricks, an Apache Spark-based big data analytics service designed for data science and data engineering offered by Microsoft.
As the number of data science positions continues to grow dramatically, so does the number of data scientists in the marketplace. Follow these expert tips and examples to help make your resume and job applications stand out in an increasingly competitive field.
Despite the benefits of federated learning, there are still ways of breaching a user’s privacy, even without sharing private data. In this article, we’ll review some research papers that discuss how federated learning includes this vulnerability.
For machine learning, more data is always better. What about more features of data? Not necessarily. This beginners' guide with code examples for selecting the most useful features from your data will jump start you toward developing the most effective and efficient learning models.
These data science tools illustrated guides are broken up into four distinct categories: data retrieval, data manipulation, data visualization, and engineering tips. Both online and PDF versions of these guides are available.
The agenda of Predictive Analytics World Berlin, 16-17 Nov, is taking shape. Curious? Take a peek at our keynote sessions 2020. Don't forget to use the code KDNUGGETS for a 15% discount on your Predictive Analytics World ticket.
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Even though transformers for NLP were introduced only a few years ago, they have delivered major impacts to a variety of fields from reinforcement learning to chemistry. Now is the time to better understand the inner workings of transformer architectures to give you the intuition you need to effectively work with these powerful tools.
NLP and deep learning continue to advance, nearly on a daily basis. Check out these recent must-read guides, feature articles, and other resources to keep you on top of the latest advancements and ahead of the curve.
The biggest hurdle in the use of data to create business value, is indeed the ability to operationalize analytics throughout the organization. Xpress Insight is geared to reduce the burden on IT and address their critical requirements while empowering business users to take ownership of decisions and change management.
In this post we present a step-by-step tutorial on how PyCaret can be used to build an Automated Machine Learning Solution within Power BI, thus allowing data scientists and analysts to add a layer of machine learning to their Dashboards without any additional license or software costs.
Learning data science means learning the hard skills of statistics, programming, and machine learning. To complete your training, a broader set of soft skills will round out your capabilities as an effective and successful professional Data Scientist.
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Samsung Semiconductor technology has played a particularly essential role in the fight against Covid-19. Samsung technology powers many of the most innovative programs and AI platforms that are helping scientists conduct research and achieve breakthroughs at a speed that would have been impossible just a few years ago.
In this post, the authors share their experience coming up with an automated system to tune one of the main parameters in their machine learning model that recommends content on LinkedIn’s Feed, which is just one piece of the community-focused architecture.
This post highlights the movement of people from the 10 most-affected European countries based on the way they stay at home, work, and visit places, using Google's anonymized location tracking dataset.
Using an interactive VR platform, KDD-2020 brings you the latest research in AI, Data Science, Deep Learning, and Machine Learning with tutorials to improve your skills, keynotes from top experts, workshops on state-of-the-art topics and over 200 research presentations.
As the collection of personal data democratized over the previous century, the question of data anonymization started to rise. The regulations coming into effect around the world sealed the importance of the matter.
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Want to learn more about our recommendations for strengthening privacy while preserving utility? Read Immuta's new whitepaper, "Reducing Re-Identification Risk in Health Data: A Guide to Three Privacy Enhancing Technologies", to get the inside scoop on the best privacy enhancing technologies.
In this article, we explore how 3D human pose estimation works based on our research and experiments, which were part of the analysis of applying human pose estimation in AI fitness coach applications.
With neural networks being so popular today in AI and machine learning development, they can still look like a black box in terms of how they learn to make predictions. To understand what is going on deep in these networks, we must consider how neural networks perform optimization.
A recent paper has explored the possibility of influencing the predictions of a freshly trained Natural Language Processing (NLP) model by tweaking the weights re-used in its training. his result is especially interesting if it proves to transfer also to the context of Computer Vision (CV) since there, the usage of pre-trained weights is widespread.
Bring your Pandas dataframes to life with D-Tale. D-Tale is an open-source solution for which you can visualize, analyze and learn how to code Pandas data structures. In this tutorial you'll learn how to open the grid, build columns, create charts and view code exports.
Join this exclusive Reuters Events webinar: Technology enabled customer-centric innovation with Travelers, Wells Fargo and Henkel, on Aug 20 @ 11:00 ET, and learn how to marry both concepts to delight your consumers with data driven customer-centric innovation!
Statistics is foundational for Data Science and a crucial skill to master for any practitioner. This advanced introduction reviews with examples the fundamental concepts of inferential statistics by illustrating the differences between Point Estimators and Confidence Intervals Estimates.
As it becomes more prevalent, NLP will enable humans to interact with computers in ways not possible before. This new type of collaboration will allow improvements in a wide variety of human endeavors, including business, philanthropy, health, and communication.
Python Machine Learning, Third Edition covers the essential concepts of reinforcement learning, starting from its foundations, and how RL can support decision making in complex environments. Read more on the topic from the book's author Sebastian Raschka.
While you cannot test model output, at least you should test that inputs are correct. Compared to the time you invest in writing unit tests, good pieces of simple tests will save you much more time later, especially when working on large projects or big data.
Data science is an attractive field because not only is it lucrative, but you can have opportunities to work on interesting projects, and you’re always learning new things. If you're trying to get started from the ground up, then review this guide to prepare for the interview essentials.
This article presents 10 use-cases for synthetic data, showing how enterprises today can use this artificially generated information to train machine learning models or share data externally without violating individuals' privacy.
The HOSTKEY GPU Grant Program is open to specialists and professionals in the Data Science sector performing research or other projects centered on innovative uses of GPU processing and which will glean practical results in the field of Data Science, with the objective of supporting basic scientific research and prospective startups.
GPT-3 is the largest natural language processing (NLP) transformer released to date, eclipsing the previous record, Microsoft Research’s Turing-NLG at 17B parameters, by about 10 times. This has resulted in an explosion of demos: some good, some bad, all interesting.
With an analysis of over a thousand Data Scientist job descriptions in the USA, check out the trends for 2020 and current expectations on new positions in the field, including credentials, experience, and programming languages.
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A single source of truth provides stakeholders with a clear picture of the enterprise assets and the potential complications that can disrupt the data strategy. Find out how you can implement this single source of truth in your enterprise ecosystem.
Classification, as a predictive model, involves aligning each class label to examples. Algorithms designed for binary classification cannot be applied to multi-class classification problems. For such situations, heuristic methods come in handy.
It's important to understand which metric should be used to evaluate trained object detectors and which one is more important. Is mAP alone enough to evaluate the objector models? Can the same metric be used to evaluate object detectors on validation set and test set?
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Take part in the latest KDnuggets poll, and share your insights with the community. Which Data Science skills do you currently possess, and which are you looking forward to add or improve upon? Vote now!
With word embeddings being such a crucial component of NLP, the reported social biases resulting from the training corpora could limit their application. The framework introduced here intends to measure the fairness in word embeddings to better understand these potential biases.
With the rich and dynamic ecosystem of Python continuing to be a leading programming language for data science and machine learning, establishing and maintaining a cost-effective development environment is crucial to your business impact. So, do you rent or buy? This overview considers the hidden and obvious factors involved in selecting and implementing your Python platform.
This straightforward guide offers a structured overview of all machine learning prerequisites needed to start working on your project, including the complete data pipeline from importing and cleaning data to modelling and production.
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