Search results for How to Invest

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• The Augmented Scientist Part 1: Practical Application Machine Learning in Classification of SEM Images

  Our goal here is to see if we can build a classifier that can identify patterns in Scanning Electron Microscope (SEM) images, and compare the performance of our classifier to the current state-of-the-art.

  https://www.kdnuggets.com/2020/03/the-augmented-scientist-practical-application-machine-learning-classification-images.html

• Image Recognition and Object Detection in Retail

  “According to Gartner, by 2020, 85% of customer interactions in the retail industry will be managed by AI.”

  https://www.kdnuggets.com/2020/02/image-recognition-object-detection-retail.html

• Probability Distributions in Data Science">Probability Distributions in Data Science

  Some machine learning models are designed to work best under some distribution assumptions. Therefore, knowing with which distributions we are working with can help us to identify which models are best to use.

  https://www.kdnuggets.com/2020/02/probability-distributions-data-science.html

• Prepare for a Long Battle against Deepfakes

  While deepfakes threaten to destroy our perception of reality, the tech giants are throwing down the gauntlet and working to enhance the state of the art in combating doctored videos and images.

  https://www.kdnuggets.com/2020/02/long-battle-against-deepfakes.html

• The Death of Data Scientists – will AutoML replace them?">The Death of Data Scientists – will AutoML replace them?

  Soon after tech giants Google and Microsoft introduced their AutoML services to the world, the popularity and interest in these services skyrocketed. We first review AutoML, compare the platforms available, and then test them out against real data scientists to answer the question: will AutoML replace us?

  https://www.kdnuggets.com/2020/02/data-scientists-automl-replace.html

• The Forgotten Algorithm

  This article explores Monte Carlo Simulation with Streamlit.

  https://www.kdnuggets.com/2020/02/forgotten-algorithm-monte-carlo-simulation.html

• Hand labeling is the past. The future is #NoLabel AI

  Data labeling is so hot right now… but could this rapidly emerging market face disruption from a small team at Stanford and the Snorkel open source project, which enables highly efficient programmatic labeling that is 10 to 1,000x as efficient as hand labeling?

  https://www.kdnuggets.com/2020/02/hand-labeling-past-future-nolabel-ai.html

• Using AI to Identify Wildlife in Camera Trap Images from the Serengeti

  With recent developments in machine learning and computer vision, we acquired the tools to provide the biodiversity community with an ability to tap the potential of the knowledge generated automatically with systems triggered by a combination of heat and motion.

  https://www.kdnuggets.com/2020/02/using-ai-identify-wildlife-images-serengeti.html

• Introduction to Geographical Time Series Prediction with Crime Data in R, SQL, and Tableau

  When reviewing geographical data, it can be difficult to prepare the data for an analysis. This article helps by covering importing data into a SQL Server database; cleansing and grouping data into a map grid; adding time data points to the set of grid data and filling in the gaps where no crimes occurred; importing the data into R; running XGBoost model to determine where crimes will occur on a specific day

  https://www.kdnuggets.com/2020/02/introduction-geographical-time-series-crime-r-sql-tableau.html

• Fidelity on How to Find a Tailor-Fit Unicorn Data Scientist

  Predictive Analytics World for Financial Services in Las Vegas, May 31-Jun 4 is honored to host an exceptional keynote by Fidelity Investments’ AI and Data Science Center of Excellence Leader, Victor Lo: "How to Find a Tailor-Fit 'Unicorn' Data Scientist for Financial Services". Use the code KDNUGGETS for a 15% discount on your Predictive Analytics World ticket.

  https://www.kdnuggets.com/2020/02/paw-find-tailor-fit-unicorn-data-scientist.html

• Observability for Data Engineering

  Going beyond traditional monitoring techniques and goals, understanding if a system is working as intended requires a new concept in DevOps, called Observability. Learn more about this essential approach to bring more context to your system metrics.

  https://www.kdnuggets.com/2020/02/observability-data-engineering.html

• Why are Machine Learning Projects so Hard to Manage?

  What makes deploying a machine learning project so difficult? Is it the expectations? The people? The tech? There are common threads to these challenges, and best practices exist to deal with them.

  https://www.kdnuggets.com/2020/02/machine-learning-projects-manage.html

• Managing Machine Learning Cycles: Five Learnings from comparing Data Science Experimentation/ Collaboration Tools

  Machine learning projects require handling different versions of data, source code, hyperparameters, and environment configuration. Numerous tools are on the market for managing this variety, and this review features important lessons learned from an ongoing evaluation of the current landscape.

  https://www.kdnuggets.com/2020/01/managing-machine-learning-cycles.html

• Data Scientist Archetypes

  My goal here is to give you a map for navigating the sprawling terrain of data science. It’s to help you prioritize what you want to learn and what you want to do, so you don’t feel lost.

  https://www.kdnuggets.com/2020/01/data-scientist-archetypes.html

• The Decade of Data Science

  With the last decade being so strong for the emerging field of Data Science, this review considers current trends in the industry, popular frameworks, helpful tools, and new tools that can be leveraged more in the future.

  https://www.kdnuggets.com/2020/01/decade-data-science.html

• Uber Has Been Quietly Assembling One of the Most Impressive Open Source Deep Learning Stacks in the Market

  Many of the technologies used by Uber teams have been open sourced and received accolades from the machine learning community. Let’s look at some of my favorites.

  https://www.kdnuggets.com/2020/01/uber-quietly-assembling-impressive-open-source-deep-learning.html

• Top 7 Location Intelligence Companies in 2020

  Here’s a complete list of top 7 location intelligence companies in the market - an overview, pricing, pros, and cons that’ll help you identify the right location intelligence tool for your business.

  https://www.kdnuggets.com/2020/01/top-7-location-intelligence-companies-2020.html

• How to Get Started With Algorithmic Finance

  Algorithmic finance has been around for decades as a money-making tool, and it's not magic. Learn about some practical strategies along with and introduction to code you can use to get started.

  https://www.kdnuggets.com/2020/01/get-started-algorithmic-finance.html

• What Do Data Scientists in Europe Do & How Much Are They Worth?

  Interested in knowing what a data scientist is worth in Europe, and what one does? Read this overview of a recent survey on the topic and gain some insight into the European data science professional job market.

  https://www.kdnuggets.com/2020/01/data-scientist-worth-europe.html

• NLP Year in Review — 2019

  In this blog post, I want to highlight some of the most important stories related to machine learning and NLP that I came across in 2019.

  https://www.kdnuggets.com/2020/01/nlp-year-review-2019.html

• Top 5 AI trends for 2020

  We are all witnessing a staggering growth of AI technology with so many new benefits for people while also changing the way we live and work. As AI continues to grow, which applications will have a significant impact in 2020?

  https://www.kdnuggets.com/2020/01/top-5-ai-trends-2020.html

• Top 10 Technology Trends for 2020">Top 10 Technology Trends for 2020

  With integrations of multiple emerging technologies just in the past year, AI development continues at a fast pace. Following the blueprint of science and technology advancements in 2019, we predict 10 trends we expect to see in 2020 and beyond.

  https://www.kdnuggets.com/2020/01/top-10-technology-trends-2020.html

• Geovisualization with Open Data

  In this post I want to show how to use public available (open) data to create geo visualizations in python. Maps are a great way to communicate and compare information when working with geolocation data. There are many frameworks to plot maps, here I focus on matplotlib and geopandas (and give a glimpse of mplleaflet).

  https://www.kdnuggets.com/2020/01/open-data-germany-maps-viz.html

• Graph Machine Learning Meets UX: An uncharted love affair

  When machine learning tools are developed by technology first, they risk failing to deliver on what users actually need. It can also be difficult for development teams to establish meaningful direction. This article explores the challenges of designing an interface that enables users to visualise and interact with insights from graph machine learning, and explores the very new, uncharted relationship between machine learning and UX.

  https://www.kdnuggets.com/2020/01/graph-machine-learning-ux.html

• Deepfakes Security Risks

  Deepfakes have instilled panic in experts since they first emerged in 2017. Microsoft and Facebook have recently announced a contest to identify deepfakes more efficiently.

  https://www.kdnuggets.com/2020/01/deepfakes-security-risks.html

• Stock Market Forecasting Using Time Series Analysis

  Time series analysis will be the best tool for forecasting the trend or even future. The trend chart will provide adequate guidance for the investor. So let us understand this concept in great detail and use a machine learning technique to forecast stocks.

  https://www.kdnuggets.com/2020/01/stock-market-forecasting-time-series-analysis.html

• 3 common data science career transitions, and how to make them happen

  Breaking into a career in Data Science can depend on where you start. See if you fit into one of these three categories of "newbies," and then find out how to make your professional transition into the field.

  https://www.kdnuggets.com/2020/01/3-common-data-science-career-transitions.html

• Automated Machine Learning: How do teams work together on an AutoML project?">Automated Machine Learning: How do teams work together on an AutoML project?

  In this use case, available to the public on GitHub, we’ll see how a data scientist, project manager, and business lead at a retail grocer can leverage automated machine learning and Azure Machine Learning service to reduce product overstock.

  https://www.kdnuggets.com/2020/01/teams-work-together-automl-project.html

• Predict Electricity Consumption Using Time Series Analysis">Predict Electricity Consumption Using Time Series Analysis

  Time series forecasting is a technique for the prediction of events through a sequence of time. In this post, we will be taking a small forecasting problem and try to solve it till the end learning time series forecasting alongside.

  https://www.kdnuggets.com/2020/01/predict-electricity-consumption-time-series-analysis.html

• 10 Best and Free Machine Learning Courses, Online

  Getting ready to leap into the world of Data Science? Consider these top machine learning courses curated by experts to help you learn and thrive in this exciting field.

  https://www.kdnuggets.com/2019/12/best-free-machine-learning-courses-online.html

• What is Data Catalog and Why You Should Care?

  Learn why data catalogs could be just the thing you need to meet the challenges of data and metadata management and collaboration.

  https://www.kdnuggets.com/2019/12/data-catalog.html

• Google’s New Explainable AI Service">Google’s New Explainable AI Service

  Google has started offering a new service for “explainable AI” or XAI, as it is fashionably called. Presently offered tools are modest, but the intent is in the right direction.

  https://www.kdnuggets.com/2019/12/googles-new-explainable-ai-service.html

• The Most In Demand Tech Skills for Data Scientists

  By the end of this article you’ll know which technologies are becoming more popular with employers and which are becoming less popular.

  https://www.kdnuggets.com/2019/12/most-demand-tech-skills-data-scientists.html

• Interpretability part 3: opening the black box with LIME and SHAP

  The third part in a series on leveraging techniques to take a look inside the black box of AI, this guide considers methods that try to explain each prediction instead of establishing a global explanation.

  https://www.kdnuggets.com/2019/12/interpretability-part-3-lime-shap.html

• The 4 fastest ways NOT to get hired as a data scientist

  Ready to try to get hired as a data scientist for the first time? Avoiding these common mistakes won’t guarantee an offer, but not avoiding them is a sure fire way for your application to be tossed into the trash bin.

  https://www.kdnuggets.com/2019/12/4-ways-not-hired-data-scientist.html

• How To “Ultralearn” Data Science, Part 1

  What is "ultralearning" and how can you follow the strategy to become an expert of data science? Start with this first part in a series that will guide you through this self-motivated methodology to help you efficiently master difficult skills.

  https://www.kdnuggets.com/2019/12/ultralearn-data-science-part1.html

• Deploying a pretrained GPT-2 model on AWS

  This post attempts to summarize my recent detour into NLP, describing how I exposed a Huggingface pre-trained Language Model (LM) on an AWS-based web application.

  https://www.kdnuggets.com/2019/12/deploying-pretrained-gpt-2-model-aws.html

• AI, Analytics, Machine Learning, Data Science, Deep Learning Technology Main Developments in 2019 and Key Trends for 2020">AI, Analytics, Machine Learning, Data Science, Deep Learning Technology Main Developments in 2019 and Key Trends for 2020

  We asked leading experts - what are the most important developments of 2019 and 2020 key trends in AI, Analytics, Machine Learning, Data Science, and Deep Learning? This blog focuses mainly on technology and deployment.

  https://www.kdnuggets.com/2019/12/predictions-ai-machine-learning-data-science-technology.html

• Interpretability: Cracking open the black box, Part 2

  The second part in a series on leveraging techniques to take a look inside the black box of AI, this guide considers post-hoc interpretation that is useful when the model is not transparent.

  https://www.kdnuggets.com/2019/12/interpretability-black-box-part-2.html

• Moving Predictive Maintenance from Theory to Practice

  Here are four common hurdles that need to be overcome before tapping into the benefits of predictive maintenance.

  https://www.kdnuggets.com/2019/12/mathworks-predictive-maintenance-theory-practice.html

• The 4 Hottest Trends in Data Science for 2020">The 4 Hottest Trends in Data Science for 2020

  The field of Data Science is growing with new capabilities and reach into every industry. With digital transformations occurring in organizations around the world, 2019 included trends of more companies leveraging more data to make better decisions. Check out these next trends in Data Science expected to take off in 2020.

  https://www.kdnuggets.com/2019/12/4-hottest-trends-data-science-2020.html

• Why software engineering processes and tools don’t work for machine learning

  While AI may be the new electricity significant challenges remain to realize AI potential. Here we examine why data scientists and teams can’t rely on software engineering tools and processes for machine learning.

  https://www.kdnuggets.com/2019/12/comet-software-engineering-machine-learning.html

• Explainability: Cracking open the black box, Part 1

  What is Explainability in AI and how can we leverage different techniques to open the black box of AI and peek inside? This practical guide offers a review and critique of the various techniques of interpretability.

  https://www.kdnuggets.com/2019/12/explainability-black-box-part1.html

• A Non-Technical Reading List for Data Science">A Non-Technical Reading List for Data Science

  The world still cannot be reduced to numbers on a page because human beings are still the ones making all the decisions. So, the best data scientists understand the numbers and the people. Check out these great data science books that will make you a better data scientist without delving into the technical details.

  https://www.kdnuggets.com/2019/12/non-technical-reading-list-data-science.html

• Top 7 Data Science Use Cases in Trust and Security

  What are trust and safety? What is the role of trust and security in the modern world? Read this overview of 7 data science application use cases in the realm of trust and security.

  https://www.kdnuggets.com/2019/12/top-7-data-science-use-cases-trust-security.html

• Machine Learning 101: The What, Why, and How of Weighting

  Weighting is a technique for improving models. In this article, learn more about what weighting is, why you should (and shouldn’t) use it, and how to choose optimal weights to minimize business costs.

  https://www.kdnuggets.com/2019/11/machine-learning-what-why-how-weighting.html

• Top 8 Data Science Use Cases in Marketing

  In this article, we want to highlight some key data science use cases in marketing. Let us concentrate on several instances that present particular interest and managed to prove their efficiency in the course of time.

  https://www.kdnuggets.com/2019/11/top-8-data-science-use-cases-marketing.html

• Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead">Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead

  The two main takeaways from this paper: firstly, a sharpening of my understanding of the difference between explainability and interpretability, and why the former may be problematic; and secondly some great pointers to techniques for creating truly interpretable models.

  https://www.kdnuggets.com/2019/11/stop-explaining-black-box-models.html

• Transfer Learning Made Easy: Coding a Powerful Technique

  While the revolution of deep learning now impacts our daily lives, these networks are expensive. Approaches in transfer learning promise to ease this burden by enabling the re-use of trained models -- and this hands-on tutorial will walk you through a transfer learning technique you can run on your laptop.

  https://www.kdnuggets.com/2019/11/transfer-learning-coding.html

• Understanding NLP and Topic Modeling Part 1

  In this post, we seek to understand why topic modeling is important and how it helps us as data scientists.

  https://www.kdnuggets.com/2019/11/understanding-nlp-topic-modeling-part-1.html

• How Data Analytics Can Assist in Fraud Detection

  A primary advantage of data analytics tools is that they can handle massive quantities of information at once. These solutions typically learn what's normal within a collection of information and how to spot anomalies.

  https://www.kdnuggets.com/2019/11/data-analytics-assist-fraud-detection.html

• Orchestrating Dynamic Reports in Python and R with Rmd Files

  Do you want to extract csv files with Python and visualize them in R? How does preparing everything in R and make conclusions with Python sound? Both are possible if you know the right libraries and techniques. Here, we’ll walk through a use-case using both languages in one analysis

  https://www.kdnuggets.com/2019/11/orchestrating-dynamic-reports-python-r-rmd-files.html

• 10 Free Must-read Books on AI">10 Free Must-read Books on AI

  Artificial Intelligence continues to fill the media headlines while scientists and engineers rapidly expand its capabilities and applications. With such explosive growth in the field, there is a great deal to learn. Dive into these 10 free books that are must-reads to support your AI study and work.

  https://www.kdnuggets.com/2019/11/10-free-must-read-books-ai.html

• Data Sources 101

  Data collection is one of the first steps of the data lifecycle — you need to get all the data you require in the first place. To collect the right data, you need to know where to find it and determine the effort involved in collecting it. This article answers the most basic question: where does all the data you need (or might need) come from?

  https://www.kdnuggets.com/2019/10/data-sources-101.html

• Data Anonymization – History and Key Ideas

  While effective anonymization technology remains elusive, understanding the history of this challenge can guide data science practitioners to address these important concerns through ethical and responsible use of sensitive information.

  https://www.kdnuggets.com/2019/10/data-anonymization-history-key-ideas.html

• Beyond Word Embedding: Key Ideas in Document Embedding

  This literature review on document embedding techniques thoroughly covers the many ways practitioners develop rich vector representations of text -- from single sentences to entire books.

  https://www.kdnuggets.com/2019/10/beyond-word-embedding-document-embedding.html

• 8 Paths to Getting a Machine Learning Job Interview

  While you may be focused on your performance during your next job interview, landing that interview can be just as hard. Check out these tips for finding and securing an interview for a machine learning job.

  https://www.kdnuggets.com/2019/10/8-paths-machine-learning-job-interview.html

• Four questions to help accurately scope analytics engineering project

  Being really good at scoping analytics projects is crucial for team productivity and profitability. You can consistently deliver on time if you work out the issue first, and these four questions can help you prepare.

  https://www.kdnuggets.com/2019/10/four-questions-scope-analytics-engineering-project.html

• The 4 Quadrants of Data Science Skills and 7 Principles for Creating a Viral Data Visualization">The 4 Quadrants of Data Science Skills and 7 Principles for Creating a Viral Data Visualization

  As a data scientist, your most important skill is creating meaningful visualizations to disseminate knowledge and impact your organization or client. These seven principals will guide you toward developing charts with clarity, as exemplified with data from a recent KDnuggets poll.

  https://www.kdnuggets.com/2019/10/4-quadrants-data-science-skills-data-visualization.html

• 5 Fundamental AI Principles

  While AI may appear magical at times, these five principles will help guide you to avoid pitfalls when leveraging this tech.

  https://www.kdnuggets.com/2019/10/5-fundamental-ai-principles.html

• How AI will transform healthcare (and can it fix the US healthcare system?)">How AI will transform healthcare (and can it fix the US healthcare system?)

  This thorough review focuses on the impact of AI, 5G, and edge computing on the healthcare sector in the 2020s as well as a look at quantum computing's potential impact on AI, healthcare, and financial services.

  https://www.kdnuggets.com/2019/09/ai-transform-healthcare.html

• 6 bits of advice for Data Scientists">6 bits of advice for Data Scientists

  As a data scientist, you can get lost in your daily dives into the data. Consider this advice to be certain to follow in your work for being diligent and more impactful for your organization.

  https://www.kdnuggets.com/2019/09/advice-data-scientists.html

• Automatic Version Control for Data Scientists

  How can you keep your machine learning models and data organized so you can collaborate effectively? Discover this new tool set available for better version control designed for the data scientist workflow.

  https://www.kdnuggets.com/2019/09/automatic-version-control-data-scientists.html

• 12 Deep Learning Researchers and Leaders">12 Deep Learning Researchers and Leaders

  Our list of deep learning researchers and industry leaders are the people you should follow to stay current with this wildly expanding field in AI. From early practitioners and established academics to entrepreneurs and today’s top corporate influencers, this diverse group of individuals is leading the way into tomorrow’s deep learning landscape.

  https://www.kdnuggets.com/2019/09/12-deep-learning-research-leaders.html

• Introducing IceCAPS: Microsoft’s Framework for Advanced Conversation Modeling

  The new open source framework that brings multi-task learning to conversational agents.

  https://www.kdnuggets.com/2019/09/introducing-icecaps-microsofts-framework-advanced-conversation-modeling.html

• Scikit-Learn & More for Synthetic Dataset Generation for Machine Learning

  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.

  https://www.kdnuggets.com/2019/09/scikit-learn-synthetic-dataset.html

• 5 Step Guide to Scalable Deep Learning Pipelines with d6tflow

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

  https://www.kdnuggets.com/2019/09/5-step-guide-scalable-deep-learning-pipelines-d6tflow.html

• What is Machine Behavior?

  The new emerging field that wants to study AI agents the way social scientists study humans.

  https://www.kdnuggets.com/2019/09/machine-behavior.html

• What’s the difference between analytics and statistics?

  From asking the best questions about data to answering those questions with certainty, understanding the value of these two seemingly different professions is clarified when you see how they should work together.

  https://www.kdnuggets.com/2019/09/difference-analytics-statistics.html

• 6 Tips for Building a Training Data Strategy for Machine Learning

  Without a well-defined approach for collecting and structuring training data, launching an AI initiative becomes an uphill battle. These six recommendations will help you craft a successful strategy.

  https://www.kdnuggets.com/2019/09/6-tips-training-data-strategy-machine-learning.html

• Top 10 Data Science Use Cases in Energy and Utilities

  In this article, we will consider the most vivid data science use cases in the industry of energy and utilities.

  https://www.kdnuggets.com/2019/09/top-10-data-science-use-cases-energy-utilities.html

• TensorFlow 2.0: Dynamic, Readable, and Highly Extended

  With substantial changes coming with TensorFlow 2.0, and the release candidate version now available, learn more in this guide about the major updates and how to get started on the machine learning platform.

  https://www.kdnuggets.com/2019/08/tensorflow-20.html

• Why Data Visualization Is The Most Important Skill in a Data Analyst Arsenal">Why Data Visualization Is The Most Important Skill in a Data Analyst Arsenal

  Visually-displayed data is much more accessible, and it’s critical to promptly identify the weaknesses of an organization, accurately forecast trading volumes and sale prices, or make the right business choices.

  https://www.kdnuggets.com/2019/08/simpliv-data-visualization-data-analyst.html

• Artificial Intelligence vs. Machine Learning vs. Deep Learning: What is the Difference?

  Over the past few years, artificial intelligence continues to be one of the hottest topics. And in order to work effectively with it, you need to understand its constituent parts.

  https://www.kdnuggets.com/2019/08/artificial-intelligence-vs-machine-learning-vs-deep-learning-difference.html

• How to Sell Your Boss on the Need for Data Analytics

  Here are some ways you can make the case to your boss that analytics investments are smart for your company to pursue.

  https://www.kdnuggets.com/2019/08/sell-boss-need-data-analytics.html

• Proptech and the proper use of technology for house sales prediction

  Using the ATTOM dataset, we extracted data on sales transactions in the USA, loans, and estimated values of property. We developed an optimal prediction model from correlations in the time and status of ownership as well as the time of the year of sales fluctuations.

  https://www.kdnuggets.com/2019/08/proptech-technology-house-sales-prediction.html

• Gender Diversity in AI Research

  Through an analysis of 1.5M papers from arXiv, this study reviews the evolution of gender diversity across disciplines, countries, and institutions as well as the semantic differences between AI papers with and without female co-authors.

  https://www.kdnuggets.com/2019/08/gender-diversity-ai-research.html

• Detecting stationarity in time series data

  Explore how to determine if your time series data is generated by a stationary process and how to handle the necessary assumptions and potential interpretations of your result.

  https://www.kdnuggets.com/2019/08/stationarity-time-series-data.html

• Crafting an Elevator Pitch for your Data Science Startup

  If you are launching a data science startup, these tips will give you a head start as you seek capital for seed funding or your next level of growth.

  https://www.kdnuggets.com/2019/08/elevator-pitch-data-science-startup.html

• How to Become More Marketable as a Data Scientist">How to Become More Marketable as a Data Scientist

  As a data scientist, you are in high demand. So, how can you increase your marketability even more? Check out these current trends in skills most desired by employers in 2019.

  https://www.kdnuggets.com/2019/08/marketable-data-scientist.html

• Understanding Cancer using Machine Learning">Understanding Cancer using Machine Learning

  Use of Machine Learning (ML) in Medicine is becoming more and more important. One application example can be Cancer Detection and Analysis.

  https://www.kdnuggets.com/2019/08/understanding-cancer-machine-learning.html

• 6 Key Concepts in Andrew Ng’s “Machine Learning Yearning”">6 Key Concepts in Andrew Ng’s “Machine Learning Yearning”

  If you are diving into AI and machine learning, Andrew Ng's book is a great place to start. Learn about six important concepts covered to better understand how to use these tools from one of the field's best practitioners and teachers.

  https://www.kdnuggets.com/2019/08/key-concepts-andrew-ng-machine-learning-yearning.html

• 12 NLP Researchers, Practitioners & Innovators You Should Be Following">12 NLP Researchers, Practitioners & Innovators You Should Be Following

  Check out this list of NLP researchers, practitioners and innovators you should be following, including academics, practitioners, developers, entrepreneurs, and more.

  https://www.kdnuggets.com/2019/08/nlp-researchers-practitioners-innovators-should-follow.html

• Inside Pluribus: Facebook’s New AI That Just Mastered the World’s Most Difficult Poker Game

  The reasons why Pluribus represents a major breakthrough in AI systems might result confusing to many readers. After all, in recent years AI researchers have made tremendous progress across different complex games. However, six-player, no-limit Texas Hold’em still remains one of the most elusive challenges for AI systems.

  https://www.kdnuggets.com/2019/08/inside-pluribus-facebooks-new-ai-poker.html

• How a simple mix of object-oriented programming can sharpen your deep learning prototype

  By mixing simple concepts of object-oriented programming, like functionalization and class inheritance, you can add immense value to a deep learning prototyping code.

  https://www.kdnuggets.com/2019/08/simple-mix-object-oriented-programming-sharpen-deep-learning-prototype.html

• Can we trust AutoML to go on full autopilot?

  We put an AutoML tool to the test on a real-world problem, and the results are surprising. Even with automatic machine learning, you still need expert data scientists.

  https://www.kdnuggets.com/2019/07/automl-full-autopilot.html

• Understanding Tensor Processing Units

  The Tensor Processing Unit (TPU) is Google's custom tool to accelerate machine learning workloads using the TensorFlow framework. Learn more about what TPUs do and how they can work for you.

  https://www.kdnuggets.com/2019/07/understanding-tensor-processing-units.html

• Fantastic Four of Data Science Project Preparation">Fantastic Four of Data Science Project Preparation

  This article takes a closer look at the four fantastic things we should keep in mind when approaching every new data science project.

  https://www.kdnuggets.com/2019/07/fantastic-four-data-science-project-preparation.html

• Top Certificates and Certifications in Analytics, Data Science, Machine Learning and AI">Top Certificates and Certifications in Analytics, Data Science, Machine Learning and AI

  Here are the top certificates and certifications in Analytics, AI, Data Science, Machine Learning and related areas.

  https://www.kdnuggets.com/2019/07/top-certificates-analytics-data-science-machine-learning-ai.html

• What’s the Best Data Strategy for Enterprises: Build, buy, partner or acquire?

  Every large organization is investing heavily in building data solutions and tools. They are building data solutions from scratch when they could be taking advantage of readily available tools and solutions. Many organizations are re-inventing the wheel and wasting resources.

  https://www.kdnuggets.com/2019/07/best-data-strategy-enterprises-build-buy-partner-acquire.html

• Big Data for Insurance

  The insurance industry has always been quite conservative; however, the adoption of new technologies is not just a modern trend but a necessity to maintain the competitive pace. In the modern digital era, Big Data technologies help to process vast amounts of information, increase workflow efficiency, and reduce operational costs. Learn more about the benefits of Big Data for insurance from our material.

  https://www.kdnuggets.com/2019/07/big-data-insurance.html

• Adapters: A Compact and Extensible Transfer Learning Method for NLP

  Adapters obtain comparable results to BERT on several NLP tasks while achieving parameter efficiency.

  https://www.kdnuggets.com/2019/07/adapters-compact-extensible-transfer-learning-method-nlp.html

• Secrets to a Successful Data Science Interview

  Are you puzzled as to what to prepare for data science interviews? That you are reading this document is a reflection of your seriousness in being a successful data scientist.

  https://www.kdnuggets.com/2019/07/secrets-data-science-interview.html

• The Hackathon Guide for Aspiring Data Scientists">The Hackathon Guide for Aspiring Data Scientists

  This article is an overview of how to prepare for a hackathon as an aspiring data scientist, highlighting the 4 reasons why you should take part in one, along with a series of tips for participation.

  https://www.kdnuggets.com/2019/07/hackathon-guide-aspiring-data-scientists.html

• Introducing Gen: MIT’s New Language That Wants to be the TensorFlow of Programmable Inference">Introducing Gen: MIT’s New Language That Wants to be the TensorFlow of Programmable Inference

  Researchers from MIT recently unveiled a new probabilistic programming language named Gen, a language which allow researchers to write models and algorithms from multiple fields where AI techniques are applied without having to deal with equations or manually write high-performance code.

  https://www.kdnuggets.com/2019/07/introducing-gen-language-progammable-inference.html

• The Death of Big Data and the Emergence of the Multi-Cloud Era">The Death of Big Data and the Emergence of the Multi-Cloud Era

  The Era of Big Data is coming to an end as the focus shifts from how we collect data to processing that data in real-time. Big Data is now a business asset supporting the next eras of multi-cloud support, machine learning, and real-time analytics.

  https://www.kdnuggets.com/2019/07/death-big-data-multi-cloud-era.html

• 10 Simple Hacks to Speed up Your Data Analysis in Python

  This article lists some curated tips for working with Python and Jupyter Notebooks, covering topics such as easily profiling data, formatting code and output, debugging, and more. Hopefully you can find something useful within.

  https://www.kdnuggets.com/2019/07/10-simple-hacks-speed-data-analysis-python.html

• Collaborative Evolutionary Reinforcement Learning

  Intel Researchers created a new approach to RL via Collaborative Evolutionary Reinforcement Learning (CERL) that combines policy gradient and evolution methods to optimize, exploit, and explore challenges.

  https://www.kdnuggets.com/2019/07/collaborative-evolutionary-reinforcement-learning.html

• State of AI Report 2019">State of AI Report 2019

  This year's "State of AI Report" has been released. Read it to find out about the latest in AI research, talent, industry, and politics form the past 12 months.

  https://www.kdnuggets.com/2019/07/state-ai-report-2019.html

• NLP vs. NLU: from Understanding a Language to Its Processing">NLP vs. NLU: from Understanding a Language to Its Processing

  As AI progresses and the technology becomes more sophisticated, we expect existing techniques to evolve. With these changes, will the well-founded natural language processing give way to natural language understanding? Or, are the two concepts subtly distinct to hold their own niche in AI?

  https://www.kdnuggets.com/2019/07/nlp-vs-nlu-understanding-language-processing.html

• 4 Most Popular Alternative Data Sources Explained

  Alternative data is the new game changer. To start with alternative data, people might even wonder from where you can get hold of alternative data that can give such a competitive advantage. This post details 4 alternative data sources that you can exploit to the fullest.

  https://www.kdnuggets.com/2019/07/4-most-popular-alternative-data-sources-explained.html

• Seven Key Dimensions to Help You Understand Artificial Intelligence Environments

  Understanding an AI environment is an incredibly complex task but there are several key dimensions that provide clarity on that reasoning.

  https://www.kdnuggets.com/2019/07/seven-key-dimensions-understand-artificial-intelligence-environments.html

• A Data Scientist’s Path to Understanding Market Simulation

  Made possible by recent advances in computing power and machine learning, market simulation employs agent-based modeling, behavioral science and network science to recreate the complex dynamics and rules of how a population of people in a given market behave, influence each other and make decisions.

  https://www.kdnuggets.com/2019/07/data-scientist-understanding-market-simulation.html

• How To Get Funding For AI Startups

  What are the biggest challenges AI startups have when pitching to investors? Learn how to grab their attention with these recommendations on how to start building your AI company.

  https://www.kdnuggets.com/2019/06/funding-ai-startups.html

• PySyft and the Emergence of Private Deep Learning

  PySyft is an open-source framework that enables secured, private computations in deep learning, by combining federated learning and differential privacy in a single programming model integrated into different deep learning frameworks such as PyTorch, Keras or TensorFlow.

  https://www.kdnuggets.com/2019/06/pysyft-emergence-deep-learning.html

• Optimization with Python: How to make the most amount of money with the least amount of risk?

  Learn how to apply Python data science libraries to develop a simple optimization problem based on a Nobel-prize winning economic theory for maximizing investment profits while minimizing risk.

  https://www.kdnuggets.com/2019/06/optimization-python-money-risk.html

• How to Make a Success Story of your Data Science Team

  Today, data science is a crucial component for an organization's growth. Given how important data science has grown, it’s important to think about what data scientists add to an organization, how they fit in, and how to hire and build effective data science teams.

  https://www.kdnuggets.com/2019/06/success-story-data-science-team.html

• Understanding Cloud Data Services">Understanding Cloud Data Services

  Ready to move your systems to a cloud vendor or just learning more about big data services? This overview will help you understand big data system architectures, components, and offerings with an end-to-end taxonomy of what is available from the big three cloud providers.

  https://www.kdnuggets.com/2019/06/understanding-cloud-data-services.html

• How Google uses Reinforcement Learning to Train AI Agents in the Most Popular Sport in the World

  Researchers from the Google Brain team open sourced Google Research Football, a new environment that leverages reinforcement learning to teach AI agents how to master the most popular sport in the world.

  https://www.kdnuggets.com/2019/06/google-reinforcement-learning-ai-agents-sport.html

• Data Literacy: Using the Socratic Method

  How can organizations and individuals promote Data Literacy? Data literacy is all about critical thinking, so the time-tested method of Socratic questioning can stimulate high-level engagement with data.

  https://www.kdnuggets.com/2019/06/data-literacy-socratic-method.html

• The Emergence of Cooperative and Competitive AI Agents

  Without specific training in collaboration or competition, a recent AI model from DeepMind uses reinforcement learning to evolve these behaviors in game-playing agents. Learn how this emergent collective intelligence outperforms their human counterparts in 3D multiplayer games.

  https://www.kdnuggets.com/2019/06/emergence-cooperative-competitive-ai-agents.html

• If you’re a developer transitioning into data science, here are your best resources"> If you’re a developer transitioning into data science, here are your best resources

  This article will provide a background on the data scientist role and why your background might be a good fit for data science, plus tangible stepwise actions that you, as a developer, can take to ramp up on data science.

  https://www.kdnuggets.com/2019/06/developer-transitioning-data-science-best-resources.html

• The Infinity Stones of Data Science">The Infinity Stones of Data Science

  Do you love data science 3000? Don't want to be embarrassed in front of the other analytics wizards? Aspire to be one of Earth's mightiest heroes, like Kevin Bacon? Help make data science a snap with these simple insights.

  https://www.kdnuggets.com/2019/06/infinity-stones-data-science.html

• Top 10 Statistics Mistakes Made by Data Scientists">Top 10 Statistics Mistakes Made by Data Scientists

  The following are some of the most common statistics mistakes made by data scientists. Check this list often to make sure you are not making any of these while applying statistics to data science.

  https://www.kdnuggets.com/2019/06/statistics-mistakes-data-scientists.html

• Why physical storage of your database tables might matter

  Follow this investigation into why physical storage of your database tables might matter, from problem identification to possible issue resolutions.

  https://www.kdnuggets.com/2019/05/physical-storage-database-tables-might-matter.html

• How the Lottery Ticket Hypothesis is Challenging Everything we Knew About Training Neural Networks

  The training of machine learning models is often compared to winning the lottery by buying every possible ticket. But if we know how winning the lottery looks like, couldn’t we be smarter about selecting the tickets?

  https://www.kdnuggets.com/2019/05/lottery-ticket-hypothesis-neural-networks.html

• Becoming a Level 3.0 Data Scientist

  Want to be a Junior, Senior, or Principal Data Scientists? Find out what you need to do to navigate the Data Science Career Game.

  https://www.kdnuggets.com/2019/05/becoming-a-level-3-data-scientist.html

• Boost Your Image Classification Model

  Check out this collection of tricks to improve the accuracy of your classifier.

  https://www.kdnuggets.com/2019/05/boost-your-image-classification-model.html

• 6 Industries Warming up to Predictive Analytics and Forecasting

  Here are six sectors that are realizing how beneficial predictive analytics could be, embracing the possibilities of valuable insights extracted from such technology.

  https://www.kdnuggets.com/2019/05/6-industries-warming-up-predictive-analytics-forecasting.html

• 60+ useful graph visualization libraries">60+ useful graph visualization libraries

  We outline 60+ graph visualization libraries that allow users to build applications to display and interact with network representations of data.

  https://www.kdnuggets.com/2019/05/60-useful-graph-visualization-libraries.html

• PyCharm for Data Scientists

  This article is a discussion of some of PyCharm's features, and a comparison with Spyder, an another popular IDE for Python. Read on to find the benefits and drawbacks of PyCharm, and an outline of when to prefer it to Spyder and vice versa.

  https://www.kdnuggets.com/2019/05/pycharm-data-scientists.html

• 5 Things to Review Before Accepting That Data Scientist Job Offer

  Before you get too excited and sign the papers for that new data scientist job, and solidify your role as a new hire, make sure you look over these 5 things first.

  https://www.kdnuggets.com/2019/05/5-things-review-before-accepting-data-scientist-job-offer.html

• “Please, explain.” Interpretability of machine learning models

  Unveiling secrets of black box models is no longer a novelty but a new business requirement and we explain why using several different use cases.

  https://www.kdnuggets.com/2019/05/interpretability-machine-learning-models.html

• [White Paper] Unlocking the Power of Data Science & Machine Learning with Python

  This guide from ActiveState provides an executive overview of how you can implement Python for your team’s data science and machine learning initiatives.

  https://www.kdnuggets.com/2019/05/activestate-whitepaper-data-science-machine-learning-python.html

• Linear Programming and Discrete Optimization with Python using PuLP

  Knowledge of such optimization techniques is extremely useful for data scientists and machine learning (ML) practitioners as discrete and continuous optimization lie at the heart of modern ML and AI systems as well as data-driven business analytics processes.

  https://www.kdnuggets.com/2019/05/linear-programming-discrete-optimization-python-pulp.html

• How to Automate Tasks on GitHub With Machine Learning for Fun and Profit

  Check this tutorial on how to build a GitHub App that predicts and applies issue labels using Tensorflow and public datasets.

  https://www.kdnuggets.com/2019/05/automate-tasks-github-machine-learning-fun-profit.html

• XGBoost Algorithm: Long May She Reign

  In recent years, XGBoost algorithm has gained enormous popularity in academic as well as business world. We outline some of the reasons behind this incredible success.

  https://www.kdnuggets.com/2019/05/xgboost-algorithm.html

• Interview Questions for Data Science – Three Case Interview Examples

  Part two in this series of useful posts for aspiring data scientists focuses on case interviews and how you can best go about answering them.

  https://www.kdnuggets.com/2019/04/interview-questions-data-science.html

• Generative Adversarial Networks – Key Milestones and State of the Art

  We provide an overview of Generative Adversarial Networks (GANs), discuss challenges in GANs learning, and examine two promising GANs: the RadialGAN, designed for numbers, and the StyleGAN, which does style transfer for images.

  https://www.kdnuggets.com/2019/04/future-generative-adversarial-networks.html

• Machine Learning and Deep Link Graph Analytics: A Powerful Combination

  We investigate how graphs can help machine learning and how they are related to deep link graph analytics for Big Data.

  https://www.kdnuggets.com/2019/04/machine-learning-graph-analytics.html