Search results for cost function

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• How to Get Data Science Interviews: Finding Jobs, Reaching Gatekeepers, and Getting Referrals">How to Get Data Science Interviews: Finding Jobs, Reaching Gatekeepers, and Getting Referrals

  In this post, the author shares what to do to get job interviews efficiently. Find answers to these questions: Where should I look for data science jobs? How do I reach out to the gatekeeper? How do I get referrals? What makes a good data science resume?

  https://www.kdnuggets.com/2021/02/data-science-interviews-finding-jobs-reaching-gatekeepers-getting-referrals.html

• Vision Transformers: Natural Language Processing (NLP) Increases Efficiency and Model Generality

  Why do we hear so little about transformer models applied to computer vision tasks? What about attention in computer vision networks?

  https://www.kdnuggets.com/2021/02/vision-transformers-nlp-efficiency-model-generality.html

• Baidu Research: 10 Technology Trends in 2021

  Understanding future technology trends may never have been as important as it is today. Check out the prediction of the 10 technology trends in 2021 from Baidu Research.

  https://www.kdnuggets.com/2021/01/top-10-technology-trends-2021.html

• What is Graph Theory, and Why Should You Care?

  Go from graph theory to path optimization.

  https://www.kdnuggets.com/2021/01/graph-theory-why-care.html

• Machine learning is going real-time

  Extracting immediate predictions from machine learning algorithms on the spot based on brand-new data can offer a next level of interaction and potential value to its consumers. The infrastructure and tech stack required to implement such real-time systems is also next level, and many organizations -- especially in the US -- seem to be resisting. But, what even is real-time ML, and how can it deliver a better experience?

  https://www.kdnuggets.com/2021/01/machine-learning-real-time.html

• Six Times Bigger than GPT-3: Inside Google’s TRILLION Parameter Switch Transformer Model

  Google’s Switch Transformer model could be the next breakthrough in this area of deep learning.

  https://www.kdnuggets.com/2021/01/google-trillion-parameter-switch-transformer-model.html

• Unsupervised Learning for Predictive Maintenance using Auto-Encoders

  This article outlines a machine learning approach to detect and diagnose anomalies in the context of machine maintenance, along with a number of introductory concepts, including: Introduction to machine maintenance; What is predictive maintenance?; ​​​​Approaches for machine diagnosis; Machine diagnosis using machine learning

  https://www.kdnuggets.com/2021/01/unsupervised-learning-predictive-maintenance-auto-encoders.html

• My Data Science Learning Journey So Far">My Data Science Learning Journey So Far

  These are some obstacles the author faced in their data science learning journey in the past year, including how much time it took to overcome each obstacle and what it has taught the author.

  https://www.kdnuggets.com/2021/01/data-science-learning-journey.html

• The Four Jobs of the Data Scientist">The Four Jobs of the Data Scientist

  So, what do you do for a living? Sometimes, the answer to that question can feel like, "everything!" Well, for the Data Scientist, an extreme sense of being a "jack of all trades" is common. In fact, four such trades can be defined that a top-quality Data Scientist will iterate through during any one project.

  https://www.kdnuggets.com/2021/01/four-jobs-data-scientist.html

• The Best Tool for Data Blending is KNIME

  These are the lessons and best practices I learned in many years of experience in data blending, and the software that became my most important tool in my day-to-day work.

  https://www.kdnuggets.com/2021/01/best-tool-data-blending-knime.html

• Cloud Data Warehouse is The Future of Data Storage

  Today, cloud data storage accounts for 45% of all enterprise data and by Q2 2021, that number could grow to 53%. Now is the time to embrace cloud than now.

  https://www.kdnuggets.com/2021/01/cloud-data-warehouse-future-data-storage.html

• Best Python IDEs and Code Editors You Should Know">Best Python IDEs and Code Editors You Should Know

  Developing machine learning algorithms requires implementing countless libraries and integrating many supporting tools and software packages. All this magic must be written by you in yet another tool -- the IDE -- that is fundamental to all your code work and can drive your productivity. These top Python IDEs and code editors are among the best tools available for you to consider, and are reviewed with their noteworthy features.

  https://www.kdnuggets.com/2021/01/best-python-ide-code-editors.html

• Top 10 Computer Vision Papers 2020">Top 10 Computer Vision Papers 2020

  The top 10 computer vision papers in 2020 with video demos, articles, code, and paper reference.

  https://www.kdnuggets.com/2021/01/top-10-computer-vision-papers-2020.html

• 11 Industrial AI Trends that will Dominate the World in 2021

  These trends broadly cover the three themes of: Where will businesses adopt AI in 2021? How will AI become more accessible? How will AI capabilities evolve?

  https://www.kdnuggets.com/2021/01/11-industrial-ai-trends-dominate-2021.html

• Where is Marketing Data Science Headed?

  Marketing data science - data science related to marketing - is now a significant part of marketing. Some of it directly competes with traditional marketing research and many marketing researchers may wonder what the future holds in store for it.

  https://www.kdnuggets.com/2021/01/marketing-data-science-headed.html

• How to Get a Job as a Data Engineer

  Data engineering skills are currently in high demand. If you are looking for career prospects in this fast-growing profession, then these 10 skills and key factors will help you prepare to land an entry-level position in this field.

  https://www.kdnuggets.com/2021/01/get-job-as-data-engineer.html

• Key Data Science Algorithms Explained: From k-means to k-medoids clustering">Key Data Science Algorithms Explained: From k-means to k-medoids clustering

  As a core method in the Data Scientist's toolbox, k-means clustering is valuable but can be limited based on the structure of the data. Can expanded methods like PAM (partitioning around medoids), CLARA, and CLARANS provide better solutions, and what is the future of these algorithms?

  https://www.kdnuggets.com/2020/12/algorithms-explained-k-means-k-medoids-clustering.html

• 2020: A Year Full of Amazing AI Papers — A Review

  So much happened in the world during 2020 that it may have been easy to miss the great progress in the world of AI. To catch you up quickly, check out this curated list of the latest breakthroughs in AI by release date, along with a video explanation, link to an in-depth article, and code.

  https://www.kdnuggets.com/2020/12/2020-amazing-ai-papers.html

• Data Catalogs Are Dead; Long Live Data Discovery

  Why data catalogs aren’t meeting the needs of the modern data stack, and how a new approach – data discovery – is needed to better facilitate metadata management and data reliability.

  https://www.kdnuggets.com/2020/12/data-catalogs-dead-long-live-data-discovery.html

• Can you trust AutoML?

  Automated Machine Learning, or AutoML, tries hundreds or even thousands of different ML pipelines to deliver models that often beat the experts and win competitions. But, is this the ultimate goal? Can a model developed with this approach be trusted without guarantees of predictive performance? The issue of overfitting must be closely considered because these methods can lead to overestimation -- and the Winner's Curse.

  https://www.kdnuggets.com/2020/12/trust-automl.html

• XGBoost: What it is, and when to use it

  XGBoost is a tree based ensemble machine learning algorithm which is a scalable machine learning system for tree boosting. Read more for an overview of the parameters that make it work, and when you would use the algorithm.

  https://www.kdnuggets.com/2020/12/xgboost-what-when.html

• Feature Store vs Data Warehouse

  A feature store is a data warehouse of features for machine learning. Differently from a data warehouse, it is dual-database: one serving features at low latency to online applications and another storing large volumes of features. Learn how Data Scientists leverage this capability in production-deployed models.

  https://www.kdnuggets.com/2020/12/feature-store-vs-data-warehouse.html

• MLOps Is Changing How Machine Learning Models Are Developed

  Delivering machine learning solutions is so much more than the model. Three key concepts covering version control, testing, and pipelines are the foundation for machine learning operations (MLOps) that help data science teams ship models quicker and with more confidence.

  https://www.kdnuggets.com/2020/12/mlops-changing-machine-learning-developed.html

• Industry 2021 Predictions for AI, Analytics, Data Science, Machine Learning

  We bring you industry predictions from 12 innovative companies - what key trends they expect in 2021 in AI, Analytics, Data Science, and Machine Learning?

  https://www.kdnuggets.com/2020/12/industry-2021-predictions-ai-data-science-machine-learning.html

• Covid or just a Cough? AI for detecting COVID-19 from Cough Sounds

  Increased capabilities in screening and early testing for a disease can significantly support quelling its spread and impact. Recent progress in developing deep learning AI models to classify cough sounds as a prescreening tool for COVID-19 has demonstrated promising early success. Cough-based diagnosis is non-invasive, cost-effective, scalable, and, if approved, could be a potential game-changer in our fight against COVID-19.

  https://www.kdnuggets.com/2020/12/covid-cough-ai-detecting-sounds.html

• State of Data Science and Machine Learning 2020: 3 Key Findings">State of Data Science and Machine Learning 2020: 3 Key Findings

  Kaggle recently released its State of Data Science and Machine Learning report for 2020, based on compiled results of its annual survey. Read about 3 key findings in the report here.

  https://www.kdnuggets.com/2020/12/kaggle-survey-2020-data-science-machine-learning.html

• 6 Things About Data Science that Employers Don’t Want You to Know

  As is the potential for any "trending hot" career, the reality of a position in the field may not be all that you initially expected. Data Science is no exception, and being still a young field, its evolving definition can offer some surprises that you should know about before accepting that dream offer.

  https://www.kdnuggets.com/2020/12/6-things-data-science-employers.html

• Data Compression via Dimensionality Reduction: 3 Main Methods

  Lift the curse of dimensionality by mastering the application of three important techniques that will help you reduce the dimensionality of your data, even if it is not linearly separable.

  https://www.kdnuggets.com/2020/12/data-compression-dimensionality-reduction.html

• A Journey from Software to Machine Learning Engineer

  In this blog post, the author explains his journey from Software Engineer to Machine Learning Engineer. The focus of the blog post is on the areas that the author wished he'd have focused on during his learning journey, and what should you look for outside of books and courses when pursuing your Machine Learning career.

  https://www.kdnuggets.com/2020/12/journey-from-software-machine-learning-engineer.html

• Main 2020 Developments and Key 2021 Trends in AI, Data Science, Machine Learning Technology">Main 2020 Developments and Key 2021 Trends in AI, Data Science, Machine Learning Technology

  Our panel of leading experts reviews 2020 main developments and examines the key trends in AI, Data Science, Machine Learning, and Deep Learning Technology.

  https://www.kdnuggets.com/2020/12/developments-trends-ai-data-science-machine-learning-technology.html

• Why the Future of ETL Is Not ELT, But EL(T)">Why the Future of ETL Is Not ELT, But EL(T)

  The well-established technologies and tools around ETL (Extract, Transform, Load) are undergoing a potential paradigm shift with new approaches to data storage and expanding cloud-based compute. Decoupling the EL from T could reconcile analytics and operational data management use cases, in a new landscape where data warehouses and data lakes are merging.

  https://www.kdnuggets.com/2020/12/future-etl-is-elt.html

• 10 Python Skills for Beginners

  Python is the fastest growing, most-beloved programming language. Get started with these Data Science tips.

  https://www.kdnuggets.com/2020/12/10-python-skills-beginners.html

• Data Science History and Overview

  In this era of big data that is only getting bigger, a huge amount of information from different fields is gathered and stored. Its analysis and extraction of value have become one of the most attractive tasks for companies and society in general, which is harnessed by the new professional role of the Data Scientist.

  https://www.kdnuggets.com/2020/11/data-science-history-overview.html

• A Friendly Introduction to Graph Neural Networks

  Despite being what can be a confusing topic, graph neural networks can be distilled into just a handful of simple concepts. Read on to find out more.

  https://www.kdnuggets.com/2020/11/friendly-introduction-graph-neural-networks.html

• Learn Deep Learning with this Free Course from Yann LeCun">Learn Deep Learning with this Free Course from Yann LeCun

  Here is a freely-available NYU course on deep learning to check out from Yann LeCun and Alfredo Canziani, including videos, slides, and other helpful resources.

  https://www.kdnuggets.com/2020/11/learn-deep-learning-free-course-yann-lecun.html

• How to Know if a Neural Network is Right for Your Machine Learning Initiative

  It is important to remember that there must be a business reason for even considering neural nets and it should not be because the C-Suite is feeling a bad case of FOMO.

  https://www.kdnuggets.com/2020/11/neural-network-right-machine-learning-initiative.html

• 15 Exciting AI Project Ideas for Beginners">15 Exciting AI Project Ideas for Beginners

  There are many branches to AI to learn, but a project-based approach can keep things interesting. Here is a list of 15 such projects you can get started on implementing today.

  https://www.kdnuggets.com/2020/11/greatlearning-ai-project-ideas-beginners.html

• How Machine Learning Works for Social Good

  We often discuss applying data science and machine learning techniques in term so of how they help your organization or business goals. But, these algorithms aren't limited to only increasing the bottom line. Developing new applications that leverage the predictive power of AI to benefit society and those communities in need is an equally valuable endeavor for Data Scientists that will further expand the positive impact of machine learning to the world.

  https://www.kdnuggets.com/2020/11/machine-learning-social-good.html

• Compute Goes Brrr: Revisiting Sutton’s Bitter Lesson for AI

  "It's just about having more compute." Wait, is that really all there is to AI? As Richard Sutton's 'bitter lesson' sinks in for more AI researchers, a debate has stirred that considers a potentially more subtle relationship between advancements in AI based on ever-more-clever algorithms and massively scaled computational power.

  https://www.kdnuggets.com/2020/11/revisiting-sutton-bitter-lesson-ai.html

• Kubernetes vs. Amazon ECS for Data Scientists

  In this article, we’ll look at two container management solutions — Kubernetes and Amazon Elastic Container Service (ECS) — from a perspective that makes sense for aspiring and current data scientists.

  https://www.kdnuggets.com/2020/11/kubernetes-amazon-ecs-data-scientists.html

• Hypothesis Vetting: The Most Important Skill Every Successful Data Scientist Needs

  A well-thought hypothesis sets the direction and plan for a Data Science project. Accordingly, a hypothesis is the most important item for evaluating whether a Data Science project will be successful.

  https://www.kdnuggets.com/2020/11/hypothesis-vetting-most-important-skill-every-successful-data-scientist-needs.html

• Algorithms for Advanced Hyper-Parameter Optimization/Tuning

  In informed search, each iteration learns from the last, whereas in Grid and Random, modelling is all done at once and then the best is picked. In case for small datasets, GridSearch or RandomSearch would be fast and sufficient. AutoML approaches provide a neat solution to properly select the required hyperparameters that improve the model’s performance.

  https://www.kdnuggets.com/2020/11/algorithms-for-advanced-hyper-parameter-optimization-tuning.html

• How to Acquire the Most Wanted Data Science Skills">How to Acquire the Most Wanted Data Science Skills

  We recently surveyed KDnuggets readers to determine the "most wanted" data science skills. Since they seem to be those most in demand from practitioners, here is a collection of resources for getting started with this learning.

  https://www.kdnuggets.com/2020/11/acquire-most-wanted-data-science-skills.html

• Moving from Data Science to Machine Learning Engineering

  The world of machine learning — and software — is changing. Read this article to find out how, and what you can do to stay ahead of it.

  https://www.kdnuggets.com/2020/11/moving-data-science-machine-learning-engineering.html

• My Data Science Online Learning Journey on Coursera

  Check out the author's informative list of courses and specializations on Coursera taken to get started on their data science and machine learning journey.

  https://www.kdnuggets.com/2020/11/data-science-online-learning-journey-coursera.html

• Six Ethical Quandaries of Predictive Policing

  When predictive machine learning models are applied to real-life scenarios, especially those that directly impact humans, such as cancer detection and other medical-related applications, the risks involved with incorrect predictions carry very high stakes. These risks are also prominent in how machine learning is applied in law enforcement, and serious ethical questions must be considered.

  https://www.kdnuggets.com/2020/11/six-ethical-quandaries-predictive-policing.html

• Essential data science skills that no one talks about">Essential data science skills that no one talks about

  Old fashioned engineering skills are what you need to boost your data science career.

  https://www.kdnuggets.com/2020/11/essential-data-science-skills-no-one-talks-about.html

• When good data analyses fail to deliver the results you expect

  To all those Data Scientists out there who thrive on discovering actionable insights from your data (all of you, right?), take heed from this cautionary tale of a data analysis, a dashboard, and a huge waste of resources.

  https://www.kdnuggets.com/2020/11/good-data-analyses-fail.html

• Microsoft and Google Open Sourced These Frameworks Based on Their Work Scaling Deep Learning Training

  Google and Microsoft have recently released new frameworks for distributed deep learning training.

  https://www.kdnuggets.com/2020/11/microsoft-google-open-sourced-frameworks-scaling-deep-learning-training.html

• Building Neural Networks with PyTorch in Google Colab">Building Neural Networks with PyTorch in Google Colab

  Combining PyTorch and Google's cloud-based Colab notebook environment can be a good solution for building neural networks with free access to GPUs. This article demonstrates how to do just that.

  https://www.kdnuggets.com/2020/10/building-neural-networks-pytorch-google-colab.html

• Dealing with Imbalanced Data in Machine Learning

  This article presents tools & techniques for handling data when it's imbalanced.

  https://www.kdnuggets.com/2020/10/imbalanced-data-machine-learning.html

• An Introduction to AI, updated">An Introduction to AI, updated

  We provide an introduction to key concepts and methods in AI, covering Machine Learning and Deep Learning, with an updated extensive list that includes Narrow AI, Super Intelligence, and Classic Artificial Intelligence, as well as recent ideas of NeuroSymbolic AI, Neuroevolution, and Federated Learning.

  https://www.kdnuggets.com/2020/10/introduction-ai-updated.html

• Getting A Data Science Job is Harder Than Ever – How to turn that to your advantage

  Although many aspiring Data Scientists are finding it is becoming more difficult to land a job than it was in previous years, understanding what has changed in the hiring landscape can be used to to your advantage in matching with the best organization for your goals and interests.

  https://www.kdnuggets.com/2020/10/getting-data-science-job-harder.html

• How to become a Data Scientist: a step-by-step guide">How to become a Data Scientist: a step-by-step guide

  Data science is everywhere. But what are the best ways to learn the field well enough to enter the profession? Read on for some tips and steps on doing so, and some great courses to help you get there.

  https://www.kdnuggets.com/2020/10/greatlearning-become-data-scientist-guide.html

• Deploying Secure and Scalable Streamlit Apps on AWS with Docker Swarm, Traefik and Keycloak

  If you are a data scientist who just wants to get the work done but doesn’t necessarily want to go down the DevOps rabbit hole, this tutorial offers a relatively straightforward deployment solution leveraging Docker Swarm and Traefik, with an option of adding user authentication with Keycloak.

  https://www.kdnuggets.com/2020/10/deploying-secure-scalable-streamlit-apps-aws-docker-swarm-traefik-keycloak.html

• Software 2.0 takes shape

  Software developers remain in very high demand as many organizations continue to experience workloads that far exceed available talent. AI-enhanced approaches that automate more areas of the software development lifecycle are in development with interesting potentials for how machine learning and natural language processing can significantly impact how software is designed, developed, tested, and deployed in the future.

  https://www.kdnuggets.com/2020/10/software-20-takes-shape.html

• Which flavor of BERT should you use for your QA task?

  Check out this guide to choosing and benchmarking BERT models for question answering.

  https://www.kdnuggets.com/2020/10/flavor-bert-use-qa-task.html

• Deploying Streamlit Apps Using Streamlit Sharing

  Read this sneak peek into Streamlit’s new deployment platform.

  https://www.kdnuggets.com/2020/10/deploying-streamlit-apps-streamlit-sharing.html

• 5 Must-Read Data Science Papers (and How to Use Them)

  Keeping ahead of the latest developments in a field is key to advancing your skills and your career. Five foundational ideas from recent data science papers are highlighted here with tips on how to leverage these advancements in your work, and keep you on top of the machine learning game.

  https://www.kdnuggets.com/2020/10/5-must-read-data-science-papers.html

• Goodhart’s Law for Data Science and what happens when a measure becomes a target?">Goodhart’s Law for Data Science and what happens when a measure becomes a target?

  When developing analytics and algorithms to better understand a business target, unintended biases can sneak in that ensure desired outcomes are obtained. Guiding your work with multiple metrics in mind can help avoid such consequences of Goodhart's Law.

  https://www.kdnuggets.com/2020/10/goodharts-law-data-science-measure-target.html

• How to be a 10x data scientist

  If you are a Data Scientist looking to make it to the next level, then there are many opportunities to up your game and your efficiency to stand out from the others. Some of these recommendations that you can follow are straightforward, and others are rarely followed, but they will all pay back in dividends of time and effectiveness for your career.

  https://www.kdnuggets.com/2020/10/10x-data-scientist.html

• Strategies of Docker Images Optimization

  Large Docker images lengthen the time it takes to build and share images between clusters and cloud providers. When creating applications, it’s therefore worth optimizing Docker Images and Dockerfiles to help teams share smaller images, improve performance, and debug problems.

  https://www.kdnuggets.com/2020/10/strategies-docker-images-optimization.html

• 6 Lessons Learned in 6 Months as a Data Scientist

  When transitioning into a Data Science career, a new mindset toward collaboration, data, and reporting is required. Learn from these recommendations on approaches you should consider to successfully develop into your dream job.

  https://www.kdnuggets.com/2020/10/6-lessons-6-months-data-scientist.html

• 5 Challenges to Scaling Machine Learning Models

  ML models are hard to be translated into active business gains. In order to understand the common pitfalls in productionizing ML models, let’s dive into the top 5 challenges that organizations face.

  https://www.kdnuggets.com/2020/10/5-challenges-scaling-machine-learning-models.html

• Data Protection Techniques Needed to Guarantee Privacy

  This article takes a look at the concepts of data privacy and personal data. It presents several privacy protection techniques and explains how they contribute to preserving the privacy of individuals.

  https://www.kdnuggets.com/2020/10/data-protection-techniques-guarantee-privacy.html

• Comparing the Top Business Intelligence Tools: Power BI vs Tableau vs Qlik vs Domo

  How smart are your organizations’ decisions? Do you have the right information to make those decisions in the first place?

  https://www.kdnuggets.com/2020/10/comparing-top-business-intelligence-tools.html

• AI in Healthcare: A review of innovative startups

  The AI innovation in healthcare has been overwhelming with the Global Healthcare AI Market accounting for $0.95 billion in 2017, and is expected to reach $19.25 billion by 2026. What drives this vibrant growth?

  https://www.kdnuggets.com/2020/09/ai-healthcare-review-innovative-startups.html

• Artificial Intelligence for Precision Medicine and Better Healthcare

  In this article, we will focus on various machine learning, deep learning models, and applications of AI which can pave the way for a new data-centric era of discovery in healthcare.

  https://www.kdnuggets.com/2020/09/artificial-intelligence-precision-medicine-better-healthcare.html

• How to Effectively Obtain Consumer Insights in a Data Overload Era

  Everybody knows how important is understanding your customer, but how to do that in an era of Information Overload?

  https://www.kdnuggets.com/2020/09/effectively-obtain-consumer-insights-data-overload-era.html

• Unpopular Opinion – Data Scientists Should Be More End-to-End

  Can a do-it-all Data Scientist really be more effective at delivering new value from data? While it might sound exhausting, important efficiencies can exist that might bring better value to the business even faster.

  https://www.kdnuggets.com/2020/09/data-scientists-should-be-more-end-to-end.html

• Online Certificates/Courses in AI, Data Science, Machine Learning from Top Universities">Online Certificates/Courses in AI, Data Science, Machine Learning from Top Universities

  We present the online courses and certificates in AI, Data Science, Machine Learning, and related topics from the top 20 universities in the world.

  https://www.kdnuggets.com/2020/09/online-certificates-ai-data-science-machine-learning-top.html

• Let’s Be Honest: We’re Drowning in Data

  The fields of Big Data, Data Analytics/Science, and Data Integration need to face a new truth: We are drowning in data, more and more so every second of every day.

  https://www.kdnuggets.com/2020/09/honest-drowning-data.html

• 9 Developing Data Science & Analytics Job Trends

  With so much disruption in 2020 already, a recent report by Burtch Works looks ahead to next year and beyond, and shares insights about how today's hiring market trends may impact our work lives for years to come.

  https://www.kdnuggets.com/2020/09/data-science-analytics-job-trends.html

• Design of Experiments in Data Science

  Read this overview of the process of designing experiments for collecting data.

  https://www.kdnuggets.com/2020/09/design-experiments-data-science.html

• How to Evaluate the Performance of Your Machine Learning Model">How to Evaluate the Performance of Your Machine Learning Model

  You can train your supervised machine learning models all day long, but unless you evaluate its performance, you can never know if your model is useful. This detailed discussion reviews the various performance metrics you must consider, and offers intuitive explanations for what they mean and how they work.

  https://www.kdnuggets.com/2020/09/performance-machine-learning-model.html

• Top Online Masters in Analytics, Business Analytics, Data Science – Updated">Top Online Masters in Analytics, Business Analytics, Data Science – Updated

  We provide an updated list of best online Masters in AI, Analytics, and Data Science, including rankings, tuition, and duration of the education program.

  https://www.kdnuggets.com/2020/09/best-online-masters-data-science-analytics-online.html

• Showcasing the Benefits of Software Optimizations for AI Workloads on Intel® Xeon® Scalable Platforms

  The focus of this blog is to bring to light that continued software optimizations can boost performance not only for the latest platforms, but also for the current install base from prior generations. This means customers can continue to extract value from their current platform investments.

  https://www.kdnuggets.com/2020/09/showcasing-benefits-software-optimizations-ai-workloads-intel.html

• Data is everywhere and it powers everything we do!

  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.

  https://www.kdnuggets.com/2020/08/data-everywhere-powers-everything.html

• Explainable and Reproducible Machine Learning Model Development with DALEX and Neptune

  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.

  https://www.kdnuggets.com/2020/08/explainable-reproducible-machine-learning-model-development-dalex-neptune.html

• Working with Spark, Python or SQL on Azure Databricks

  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.

  https://www.kdnuggets.com/2020/08/spark-python-sql-azure-databricks.html

• A Deep Dive Into the Transformer Architecture – The Development of Transformer Models

  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.

  https://www.kdnuggets.com/2020/08/transformer-architecture-development-transformer-models.html

• The NLP Model Forge: Generate Model Code On Demand">The NLP Model Forge: Generate Model Code On Demand

  You've seen their Big Bad NLP Database and The Super Duper NLP Repo. Now Quantum Stat is back with its most ambitious NLP product yet: The NLP Model Forge.

  https://www.kdnuggets.com/2020/08/nlp-model-forge.html

• Performance Testing on Big Data Applications

  You can use performance testing in any application you’re working on but it’s especially useful for big data applications. Let’s see why.

  https://www.kdnuggets.com/2020/08/performance-testing-big-data-applications.html

• Build Your Own AutoML Using PyCaret 2.0

  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.

  https://www.kdnuggets.com/2020/08/build-automl-pycaret.html

• These Data Science Skills will be your Superpower">These Data Science Skills will be your Superpower

  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.

  https://www.kdnuggets.com/2020/08/data-science-skills-superpower.html

• Introduction to Federated Learning">Introduction to Federated Learning

  Federated learning means enabling on-device training, model personalization, and more. Read more about it in this article.

  https://www.kdnuggets.com/2020/08/introduction-federated-learning.html

• Autotuning for Multi-Objective Optimization on LinkedIn’s Feed Ranking

  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.

  https://www.kdnuggets.com/2020/08/autotuning-multi-objective-optimization-linkedin-feed-ranking.html

• Top Google AI, Machine Learning Tools for Everyone">Top Google AI, Machine Learning Tools for Everyone

  Google is much more than a search company. Learn about all the tools they are developing to help turn your ideas into reality through Google AI.

  https://www.kdnuggets.com/2020/08/top-google-ai-machine-learning-tools.html

• 3D Human Pose Estimation Experiments and Analysis

  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.

  https://www.kdnuggets.com/2020/08/3d-human-pose-estimation-experiments-analysis.html

• 10 Use Cases for Privacy-Preserving Synthetic Data

  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.

  https://www.kdnuggets.com/2020/08/10-use-cases-privacy-preserving-synthetic-data.html

• Containerization of PySpark Using Kubernetes

  This article demonstrates the approach of how to use Spark on Kubernetes. It also includes a brief comparison between various cluster managers available for Spark.

  https://www.kdnuggets.com/2020/08/containerization-pyspark-kubernetes.html

• Fuzzy Joins in Python with d6tjoin

  Combining different data sources is a time suck! d6tjoin is a python library that lets you join pandas dataframes quickly and efficiently.

  https://www.kdnuggets.com/2020/07/fuzzy-joins-python-d6tjoin.html

• Scaling Computer Vision Models with Dataflow

  Scaling Machine Learning models is hard and expensive. We will shortly introduce the Google Cloud service Dataflow, and how it can be used to run predictions on millions of images in a serverless way.

  https://www.kdnuggets.com/2020/07/scaling-computer-vision-models-dataflow.html

• Deep Learning for Signal Processing: What You Need to Know

  Signal Processing is a branch of electrical engineering that models and analyzes data representations of physical events. It is at the core of the digital world. And now, signal processing is starting to make some waves in deep learning.

  https://www.kdnuggets.com/2020/07/deep-learning-signal-processing.html

• Is depth useful for self-attention?

  Learn about recent research that is the first to explain a surprising phenomenon where in BERT/Transformer-like architectures, deepening the network does not seem to be better than widening (or, increasing the representation dimension). This empirical observation is in contrast to a fundamental premise in deep learning.

  https://www.kdnuggets.com/2020/07/depth-useful-self-attention.html

• Labelling Data Using Snorkel

  In this tutorial, we walk through the process of using Snorkel to generate labels for an unlabelled dataset. We will provide you examples of basic Snorkel components by guiding you through a real clinical application of Snorkel.

  https://www.kdnuggets.com/2020/07/labelling-data-using-snorkel.html

• Recommender Systems in a Nutshell

  Marketing scientist Kevin Gray asks Dr. Anna Farzindar of the University of Southern California about recommender systems and the ways they are used.

  https://www.kdnuggets.com/2020/07/recommender-systems-nutshell.html

• Monitoring Apache Spark – We’re building a better Spark UI

  Data Mechanics is developing a free monitoring UI tool for Apache Spark to replace the Spark UI with a better UX, new metrics, and automated performance recommendations. Preview these high-level feedback features, and consider trying it out to support its first release.

  https://www.kdnuggets.com/2020/07/monitoring-apache-spark-better-ui.html

• Powerful CSV processing with kdb+

  This article provides a glimpse into the available tools to work with CSV files and describes how kdb+ and its query language q raise CSV processing to a new level of performance and simplicity.

  https://www.kdnuggets.com/2020/07/powerful-csv-processing-kdb.html

• Why would you put Scikit-learn in the browser?

  Honestly? I don’t know. But I do think WebAssembly is a good target for ML/AI deployment (in the browser and beyond).

  https://www.kdnuggets.com/2020/07/why-put-scikit-learn-browser.html

• 10 Steps for Tackling Data Privacy and Security Laws in 2020

  Data privacy laws, such as the CCPA, GDPR, and HIPAA, are here to stay and significantly impact everyone in the digital era. These steps will guide organizations to prepare for compliance and ensure they support the fundamental privacy rights of their customers and users.

  https://www.kdnuggets.com/2020/07/10-steps-data-privacy-security-laws.html

• What I learned from looking at 200 machine learning tools

  While hundreds of machine learning tools are available today, the ML software landscape may still be underdeveloped with more room to mature. This review considers the state of ML tools, existing challenges, and which frameworks are addressing the future of machine learning software.

  https://www.kdnuggets.com/2020/07/200-machine-learning-tools.html

• Apache Spark on Dataproc vs. Google BigQuery

  This post looks at research undertaken to provide interactive business intelligence reports and visualizations for thousands of end users, in the hopes of addressing some of the challenges to architects and engineers looking at moving to Google Cloud Platform in selecting the best technology stack based on their requirements and to process large volumes of data in a cost effective yet reliable manner.

  https://www.kdnuggets.com/2020/07/apache-spark-dataproc-vs-google-bigquery.html

• The Bitter Lesson of Machine Learning">The Bitter Lesson of Machine Learning

  Since that renowned conference at Dartmouth College in 1956, AI research has experienced many crests and troughs of progress through the years. From the many lessons learned during this time, some have needed to be re-learned -- repeatedly -- and the most important of which has also been the most difficult to accept by many researchers.

  https://www.kdnuggets.com/2020/07/bitter-lesson-machine-learning.html

• 5 Things You Don’t Know About PyCaret

  In comparison with the other open source machine learning libraries, PyCaret is an alternate low-code library that can be used to replace hundreds of lines of code with a few words only.

  https://www.kdnuggets.com/2020/07/5-things-pycaret.html

• Stop training more models, start deploying them

  We are hardly living up to the promises of AI in healthcare. It’s not because of our training, it’s because of our deployment.

  https://www.kdnuggets.com/2020/06/stop-training-models-start-deploying.html

• The Unreasonable Progress of Deep Neural Networks in Natural Language Processing (NLP)

  Natural language processing has made incredible advances through advanced techniques in deep learning. Learn about these powerful models, and find how close (or far away) these approaches are to human-level understanding.

  https://www.kdnuggets.com/2020/06/unreasonable-progress-deep-neural-networks-nlp.html

• Free Economics & Finance Courses for Data Scientists

  Here is a selection of courses for those interested in diversifying their domain knowledge into the related realms of economics and finance, with the goal of being able to apply your data science skills to these domains.

  https://www.kdnuggets.com/2020/06/free-economics-finance-courses-data-scientists.html

• What is emotion AI and why should you care?

  What is emotion AI, why is it relevant, and what do you need to know about it?

  https://www.kdnuggets.com/2020/06/emotion-ai.html

• LightGBM: A Highly-Efficient Gradient Boosting Decision Tree

  LightGBM is a histogram-based algorithm which places continuous values into discrete bins, which leads to faster training and more efficient memory usage. In this piece, we’ll explore LightGBM in depth.

  https://www.kdnuggets.com/2020/06/lightgbm-gradient-boosting-decision-tree.html

• Build Dog Breeds Classifier Step By Step with AWS Sagemaker

  This post takes you through the basic steps for creating a cloud-based deep learning dog classifier, with everything accomplished from the AWS Management Console.

  https://www.kdnuggets.com/2020/06/build-dog-breeds-classifier-aws-sagemaker.html

• Crop Disease Detection Using Machine Learning and Computer Vision

  Computer vision has tremendous promise for improving crop monitoring at scale. We present our learnings from building such models for detecting stem and wheat rust in crops.

  https://www.kdnuggets.com/2020/06/crop-disease-detection-computer-vision.html

• Five Cognitive Biases In Data Science (And how to avoid them)">Five Cognitive Biases In Data Science (And how to avoid them)

  Everyone is prey to cognitive biases that skew thinking, but data scientists must prevent them from spoiling their work. Learn more about five biases that can all too easily make your seemingly objective work become surprisingly subjective.

  https://www.kdnuggets.com/2020/06/five-cognitive-biases-data-science.html

• Upgrading the Brand Mobile App with Machine Learning

  The tech progress in mobile app development, as well as digital enhancements, have created new chances for brands to allure and retain customers. In bridging the individualization gap, Machine Learning comes to the rescue.

  https://www.kdnuggets.com/2020/06/upgrading-brand-mobile-app-machine-learning.html

• Why Do AI Systems Need Human Intervention to Work Well?

  All is not well with artificial intelligence-based systems during the coronavirus pandemic. No, the virus does not impact AI – however, it does impact humans, without whom AI and ML systems cannot function properly. Surprised?

  https://www.kdnuggets.com/2020/06/ai-systems-need-human-intervention.html

• If you had to start statistics all over again, where would you start?">If you had to start statistics all over again, where would you start?

  If you are just diving into learning statistics, then where do you begin? Find insight from those who have tread in these waters before, and see what they might have done differently along their personal journeys in statistics.

  https://www.kdnuggets.com/2020/06/start-statistics-all-over-again.html

• Machine Learning Experiment Tracking

  Why is experiment tracking so important for doing real world machine learning?

  https://www.kdnuggets.com/2020/06/machine-learning-experiment-tracking.html

• Forecasting Stories 4: Time-series too, Causal too

  This article is about the story of taking effective business decisions basis a combined model. Let us together study how these components work hand in hand.

  https://www.kdnuggets.com/2020/06/forecasting-stories-4-time-series-causal.html

• Model Evaluation Metrics in Machine Learning">Model Evaluation Metrics in Machine Learning

  A detailed explanation of model evaluation metrics to evaluate a classification machine learning model.

  https://www.kdnuggets.com/2020/05/model-evaluation-metrics-machine-learning.html

• 5 Machine Learning Papers on Face Recognition

  This article will highlight some of that research and introduce five machine learning papers on face recognition.

  https://www.kdnuggets.com/2020/05/5-machine-learning-papers-face-recognition.html

• Are Tera Operations Per Second (TOPS) Just hype? Or Dark AI Silicon in Disguise?

  This article explains why TOPS isn’t as accurate a gauge as many people think, and discusses other criteria that should be considered when evaluating a solution to a real application.

  https://www.kdnuggets.com/2020/05/tops-just-hype-dark-ai-silicon-disguise.html

• Deepmind’s Gaming Streak: The Rise of AI Dominance

  There is still a long way to go before machine agents match overall human gaming prowess, but Deepmind’s gaming research focus has shown a clear progression of substantial progress.

  https://www.kdnuggets.com/2020/05/deepmind-gaming-ai-dominance.html

• Faster machine learning on larger graphs with NumPy and Pandas

  One of the most exciting features of StellarGraph 1.0 is a new graph data structure — built using NumPy and Pandas — that results in significantly lower memory usage and faster construction times.

  https://www.kdnuggets.com/2020/05/faster-machine-learning-larger-graphs-numpy-pandas.html

• A Holistic Framework for Managing Data Analytics Projects

  Agile project management for Data Science development continues to be an effective framework that enables flexibility and productivity in a field that can experience continuous changes in data and evolving stakeholder expectations. Learn more about the leading approaches for developing Data Science models, and apply them to your next project.

  https://www.kdnuggets.com/2020/05/framework-managing-data-analytics-projects.html

• AI and Machine Learning for Healthcare">AI and Machine Learning for Healthcare

  Traditional business and technology sectors are not the only fields being impacted by AI. Healthcare is a field that is thought to be highly suitable for the applications of AI tools and techniques.

  https://www.kdnuggets.com/2020/05/ai-machine-learning-healthcare.html

• Machine Learning in Power BI using PyCaret

  Check out this step-by-step tutorial for implementing machine learning in Power BI within minutes.

  https://www.kdnuggets.com/2020/05/machine-learning-power-bi-pycaret.html

• What You Need to Know About Deep Reinforcement Learning

  How does deep learning solve the challenges of scale and complexity in reinforcement learning? Learn how combining these approaches will make more progress toward the notion of Artificial General Intelligence.

  https://www.kdnuggets.com/2020/05/deep-reinforcement-learning.html

• The Architecture Used at LinkedIn to Improve Feature Management in Machine Learning Models

  The new typed feature schema streamlined the reusability of features across thousands of machine learning models.

  https://www.kdnuggets.com/2020/05/architecture-linkedin-feature-management-machine-learning-models.html

• Chatbots in a Nutshell

  Marketing scientist Kevin Gray asks Dr. Anna Farzindar of the University of Southern California about chatbots and the ways they are used.

  https://www.kdnuggets.com/2020/05/chatbots-nutshell.html

• 4 Realistic Career Options for Data Scientists

  It’s almost 10 years since "Data Science" became mainstream. We ask less about how to get into Data Science, but wonder "what’s next?" This article includes insights on four non-trivial, but practical, options and their pitfalls.

  https://www.kdnuggets.com/2020/04/4-career-options-data-scientists.html