Search results for private cloud

    Found 216 documents, 14021 searched:

  • 11 Best Practices of Cloud and Data Migration to AWS Cloud

    list of Best Practices compiled from our learnings during our migration journey to the AWS cloud.

  • Beginner’s Guide to Cloud Computing

    Learn how cloud computing works, different types of models, top cloud platforms, and applications.

  • Top 5 Free Cloud Notebooks in 2022

    Create and collaborate on data science projects or train machine learning models using free cloud Jupyter notebook platforms. You get a hassle-free IDE experience and free compute resources.

  • Deploy a Dockerized FastAPI App to Google Cloud Platform

    A short guide to deploying a Dockerized Python app to Google Cloud Platform using Cloud Run and a SQL instance.

  • Alternative Cloud Hosted Data Science Environments

    Over the years new alternative providers have risen to provided a solitary data science environment hosted on the cloud for data scientist to analyze, host and share their work.

  • Platinum BlogThe Death of Big Data and the Emergence of the Multi-Cloud Era">Gold BlogPlatinum BlogThe 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.

  • Top Stories, Jun 24-30: Understanding Cloud Data Services; 7 Steps to Mastering Data Preparation for Machine Learning with Python — 2019 Edition

    Also: How To Get Funding For AI Startups; Optimization with Python: How to make the most amount of money with the least amount of risk?; 5 Useful Statistics Data Scientists Need to Know; Data Science Jobs Report 2019: Python Way Up, TensorFlow Growing Rapidly, R Use Double SAS

  • 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.

  • Comparing Machine Learning as a Service: Amazon, Microsoft Azure, Google Cloud AI">Gold BlogComparing Machine Learning as a Service: Amazon, Microsoft Azure, Google Cloud AI

    A complete and unbiased comparison of the three most common Cloud Technologies for Machine Learning as a Service.

  • The Internet of Things in the Cloud

    Cloud computing is the next evolutionary step in Internet-based computing, which provides the means for delivering ICT resources as a service. Internet-of-Things can benefit from the scalability, performance and pay-as-you-go nature of cloud computing infrastructures.

  • Why the Data Scientist and Data Engineer Need to Understand Virtualization in the Cloud

    This article covers the value of understanding the virtualization constructs for the data scientist and data engineer as they deploy their analysis onto all kinds of cloud platforms. Virtualization is a key enabling layer of software for these data workers to be aware of and to achieve optimal results from.

  • RCloud – DevOps for Data Science

    After almost two decades of software development, term – DevOps was coined and officially given importance to collaboration between development and deployment of software systems. In this early stage of Data Science field, use of standardized and empirical practises like DevOps will definitely speed up its evolution.

  • Cloud Computing Key Terms, Explained

    A concise overview of 20 core cloud computing ecosystem concepts. The focus here is on the terminology, not The Big Picture.

  • KDnuggets Interview: Amr Awadallah, CTO & Co-founder, Cloudera on the Need for Self-Service Analytics

    We discuss the importance of enabling self-service analytics, partnership with Cask, Big Data vendor selection and competitive landscape.

  • Hadoop as a Service: 18 Cloud Options

    Hadoop as a service in the cloud makes big data applications and projects easier to approach and these 18 platforms each provide their own unique solutions.

  • iMath Cloud Data Science Platform beta

    iMathResearch presents a Data Science platform, offering development in Python, R or Octave, cloud-based collaboration, private computational instances and visualization from the browser.

  • KDnuggets Exclusive: Marten Mickos, SVP, HP on the Role of Open Source in Cloud industry

    In an exclusive interview with KDnuggets, Marten talks about HP’s Open Source strategy, evolution of Open Source production model, learning from the success of Open Source in Web, trends and more.

  • KDnuggets Exclusive: Marten Mickos, SVP, HP on Why the Future Belongs to “Hybrid Clouds”

    In an exclusive interview with KDnuggets, Marten talks about the future of Eucalyptus (recently acquired by HP), defines Hybrid Clouds and their importance, and gives some tips for vendor selection.

  • iMathCloud, Python Data Science Platform

    iMathResearch presents its first tool for big data analysis, offering easy access to computational tools, a simple Python-based interface, cloud-based collaboration, and private computational instances.

  • Saxon Global, fast growing BI, Big Data, Cloud Service Provider

    Why India is emerging as a powerhouse of Analytics, Big Data Applications, Privacy, What will replace Big Data, and more.

  • KDnuggets Interview: Michael Brodie on Data Curation, Cloud Computing, Startup Quality, Verizon (part 2)

    The second part of our exclusive interview focuses on Data Curation, Cloud Computing, Data Tamer and Jisto startups, and his experience as a chief Scientist of Verizon - and how that relates to teenager never tidying a room for 60 years.

  • Cloudera Data Science Challenge

    Your task is to analyze a large amount of data from Medicare and try to detect abnormal data -- providers, areas, or patients with unusual procedures and/or claims. Challenge starts March 31, 2014.

  • Explore Analytics offers Data Visualization in the cloud

    Explore Analytics is a self-service BI tool that lets you access, explore, analyze, and instantly create interactive visualizations and dashboards to share with your team.

  • SiSense first In-Chip Analytics Solution in the Cloud

    SiSense makes Terabyte-range analytics workloads in the Cloud easy and affordable, with costs as low as $1/TB per hour. SiSense will work with Rackspace to reduce cost and complexity from Big Data Cloud deployments.

  • SiSense first In-Chip Analytics Solution in the Cloud

    SiSense makes Terabyte-range analytics workloads in the Cloud easy and affordable, with costs as low as $1/TB per hour. SiSense will work with Rackspace to reduce cost and complexity from Big Data Cloud deployments.

  • 5 Key Components of a Data Sharing Platform

    Read this article for an overview of what the components of a data-sharing platform are.

  • Create Synthetic Time-series with Anomaly Signatures in Python

    A simple and intuitive way to create synthetic (artificial) time-series data with customized anomalies — particularly suited to industrial applications.

  • 5 strategies for enterprise machine learning for 2021

    While it is important for enterprises to continue solving the past challenges in a machine learning pipeline (manage, monitor, track experiments and models) in 2021 enterprises should focus on strategies to achieve scalability, elasticity and operationalization of machine learning.

  • Setting Up Your Data Science & Machine Learning Capability in Python">Silver BlogSetting Up Your Data Science & Machine Learning Capability in Python

    With the rich and dynamic ecosystem of Python continuing to be a leading programming language for data science and machine learning, establishing and maintaining a cost-effective development environment is crucial to your business impact. So, do you rent or buy? This overview considers the hidden and obvious factors involved in selecting and implementing your Python platform.

  • Seven Myths About the True Costs of AI Systems

    While there is much excitement today around implementing AI at the enterprise level, the financial costs of this process are often unexpected and underappreciated. These seven myths are crucial lessons learned that executives should know before heading down the road to AI.

  • Platinum BlogEverything a Data Scientist Should Know About Data Management">Silver BlogPlatinum BlogEverything a Data Scientist Should Know About Data Management

    For full-stack data science mastery, you must understand data management along with all the bells and whistles of machine learning. This high-level overview is a road map for the history and current state of the expansive options for data storage and infrastructure solutions.

  • Deep Learning for the Masses (… and The Semantic Layer)

    Deep learning is everywhere right now, in your watch, in your television, your phone, and in someway the platform you are using to read this article. Here I’ll talk about how can you start changing your business using Deep Learning in a very simple way. But first, you need to know about the Semantic Layer.

  • UnitedHealth Group: Big Data Engineering Lead (Eden Prairie, MN)

    Seeking strong leaders who are collaborative, self -starters, take ownership / accountability and drive results. The Lead Big Data Engineer will involve managing one or more Scrum teams, provide technical leadership and work with Product Owners/Functional experts and Senior Management.

  • Deep Learning Made Easy with Deep Cognition

    So normally we do Deep Learning programming, and learning new APIs, some harder than others, some are really easy an expressive like Keras, but how about a visual API to create and deploy Deep Learning solutions with the click of a button? This is the promise of Deep Cognition.

  • Streamlining Analytic Deployment: Inside the FICO Decision Management Suite 2.0

    This post explains what’s new in the 2.0 version of the FICO Decision Management Suite, and how it can be used by data scientists and others to create stronger customer relationships and provide strategic competitive advantage.

  • API for Prediction and Machine Learning: poll results and analysis

    APIs are set procedures which provide easy to use, automated, robust solution to the recurring programming challenges. Here, we analyzed major players in the big data domain are providing machine learning APIs.

  • Interview: Reiner Kappenberger, HP Security Voltage on Data-Centric Security for Big Data

    We discuss HP Security Voltage growth story, HP acquisition, assessing the state of current security standards, and the need for “data-centric” security.

  • Computing Platforms for Analytics, Data Mining, Data Science

    The poll results suggest a split between a majority of data miners and data scientists who work with growing but still "PC-size", small GB-sized data, and a smaller group of Big Data analysts who work with cloud-sized data. Cloud computing, Unix, and especially Mac gained in popularity.

  • PredictionIO (Open Source Version) vs Microsoft Azure Machine Learning

    Azure Machine Learning and PredictionIO are tools that both have similar visions and similar features, but when digging deeper you’ll notice key differences and key advantages to each.

  • Data Science’s Most Used, Confused, and Abused Jargon

    As data science has spread through the mainstream, so too has a dense vocabulary of ill-defined jargon. In a split-personality post, we offer several perspectives on many of data science's most confused terms.

  • Simplilearn Big Data and Analytics courses, 30% off

    Keep pace with the competition - upgrade your Big Data and Analytics skills with Simplilearn online courses in Cloud computing, Hadoop, SAS, R and more, now 30% off until Dec 31, 2014.

  • CRN 50 Emerging Big Data Vendors

    We examine CRN top 50 Emerging Big Data Vendors, with 65% located in Silicon Valley. The prototypical company is located in San Francisco and develops software for Hadoop analytics platform. Competition will be tough!

  • CRN 25 Big Data Management Companies

    We examine top 25 Big Data Management companies, part of CRN Big Data 100, including Actian, Couchbase, and MemSQL. A large fraction of these companies develop NoSQL solutions.

  • Big Data for Executives 2014: Day 1 Highlights

    Highlights from the presentations by Big Data experts from Sears Holdings, PWC, Oracle, Altamira, Tesora on Day 1 of Big Data for Executives 2014.

  • Big Data generates Big Returns, says VC Roger Ehrenberg

    An interview with Roger Ehrenberg, founder of venture capital firm IA Ventures is a leading investor in startups solely around a big data theme. Ehrenberg talks about Big data, what he looks for in firms he invests, Wall Street, and whether consumer privacy is dead.

  • Wikibon Real World of Big Data Infographic

    This visually appealing infographic focuses on Big Data in the enterprise, covering the revenue breakdown, main growth drivers, who are the big spenders, Big Data Investment sectors, and Big Data in Motion.

  • Who Will Make Money from the Generative AI Gold Rush?

    Buckle up for the Generative AI gold rush! Will BigTech rule with its picks and shovels? Which startups will strike it rich? Will “copilot for X” be the business strategy to hit pay dirt? How can startups dig moats to keep out other prospectors? And will the US once again have the richest gold seams?

  • Where Does AI Happen?

    Which sector should aspiring researchers flock toward? Academia or industry?

  • Introducing MPT-7B: A New Open-Source LLM

    An LLM Trained on 1T Tokens of Text and Code by MosaicML Foundation Series.

  • 5 Ways to Deal with the Lack of Data in Machine Learning

    Effective solutions exist when you don't have enough data for your models. While there is no perfect approach, five proven ways will get your model to production.

  • Creating a Web Application to Extract Topics from Audio with Python

    A step-by-step tutorial to build and deploy a web application for topic modeling of a Spotify podcast.

  • Learn Data Science From These GitHub Repositories

    KDnuggets Top Blog Kickstart your data science career with these curated GitHub repositories.

  • Sentiment Analysis on Encrypted Data with Homomorphic Encryption

    This blog post uses the Concrete-ML library, allowing data scientists to use machine learning models in fully homomorphic encryption (FHE) settings without any prior knowledge of cryptography. We provide a practical tutorial on how to use the library to build a sentiment analysis model on encrypted data.

  • Is OLAP Dead?

    OLAP enables citizen analysts to quickly, efficiently, and cost-effectively uncover new business insights at a reduced time-to-value.

  • The 5 Best Places To Host Your Data Science Portfolio

    How can you showcase your data scientist skills and abilities? The answer to this question is online platforms where you can publish your portfolio and seize opportunities.

  • Data Preparation with SQL Cheatsheet

    KDnuggets Top Blog If your raw data is in a SQL-based data lake, why spend the time and money to export the data into a new platform for data prep?

  • 5 Free Hosting Platform For Machine Learning Applications

    Learn about the free and easy-to-deploy hosting platform for your machine learning projects.

  • An Overview of Mercury: Creating Data Science Portfolio and Notebook Based WebApps

    Turn your dull Jupyter notebooks into interactive web apps by adding a YAML header and sharing it with your friends and colleagues. You can also use Mercury to create your data science portfolio, which consists of a resume and projects.

  • A New Way of Managing Deep Learning Datasets

    Create, version-control, query, and visualize image, audio, and video datasets using Hub 2.0 by Activeloop.

  • No Brainer AutoML with AutoXGB

    Learn how to train, optimize, and build API with a few lines of code using AutoXGB.

  • 11 Best Companies to Work for as a Data Scientist

    This list of best data science companies aims to go beyond the usual and expected. Some great and perhaps underrated options to get a job as a data scientist.

  • Data Science & Analytics Industry Main Developments in 2021 and Key Trends for 2022

    We have solicited insights from experts at industry-leading companies, asking: "What were the main AI, Data Science, Machine Learning Developments in 2021 and what key trends do you expect in 2022?" Read their opinions here.

  • AI, Analytics, Machine Learning, Data Science, Deep Learning Research Main Developments in 2021 and Key Trends for 2022

    2021 has almost come and gone. We saw some standout advancements in AI, Analytics, Machine Learning, Data Science, Deep Learning Research this past year, and the future, starting with 2022, looks bright. As per KDnuggets tradition, our collection of experts have contributed their insights on the matter. Read on to find out more.

  • How to Build Data Frameworks with Open Source Tools to Enhance Agility and Security

    Let’s take a look at how to harness open source tools to build your data frameworks.

  • Adventures in MLOps with Github Actions,, Label Studio and NBDEV

    This article documents the authors' experience building their custom MLOps approach.

  • MLOps And Machine Learning Roadmap

    A 16–20 week roadmap to review machine learning and learn MLOps.

  • Data Monetization 101

    The evolving marketplace of data now includes many firms that support a variety of needs from organizations looking to grow with data. This listing of the key players categorized by target market provides an interesting picture of this exciting industry sector.

  • MLOps Best Practices

    Many technical challenges must be overcome to achieve successful delivery of machine learning solutions at scale. This article shares best practices we encountered while architecting and applying a model deployment platform within a large organization, including required functionality, the recommendation for a scalable deployment pattern, and techniques for testing and performance tuning models to maximize platform throughput.

  • Overview of AutoNLP from Hugging Face with Example Project

    AutoNLP is a beta project from Hugging Face that builds on the company’s work with its Transformer project. With AutoNLP you can get a working model with just a few simple terminal commands.

  • Will There Be a Shortage of Data Science Jobs in the Next 5 Years?">Gold BlogWill There Be a Shortage of Data Science Jobs in the Next 5 Years?

    The data science workflow is getting automated day by day.

  • The Best Machine Learning Frameworks & Extensions for TensorFlow

    Check out this curated list of useful frameworks and extensions for TensorFlow.

  • Overview of MLOps

    Building a machine learning model is great, but to provide real business value, it must be made useful and maintained to remain useful over time. Machine Learning Operations (MLOps), overviewed here, is a rapidly growing space that encompasses everything required to deploy a machine learning model into production, and is a crucial aspect to delivering this sought after value.

  • AI in Dating: Can Algorithms Help You Find Love?

    Can AI algorithms help us find love? Can they go a step further and replace a human being as a partner in a relationship? Here, we analyze how far technology has come in helping us meet "our" people, find love, and feel less lonely.

  • Getting Started with Distributed Machine Learning with PyTorch and Ray

    Ray is a popular framework for distributed Python that can be paired with PyTorch to rapidly scale machine learning applications.

  • GPT-2 vs GPT-3: The OpenAI Showdown

    Thanks to the diversity of the dataset used in the training process, we can obtain adequate text generation for text from a variety of domains. GPT-2 is 10x the parameters and 10x the data of its predecessor GPT.

  • Going Beyond the Repo: GitHub for Career Growth in AI & Machine Learning

    Many online tools and platforms exist to help you establish a clear and persuasive online profile for potential employers to review. Have you considered how your go-to online code repository could also help you land your next job?

  • Navigate the road to Responsible AI

    Deploying AI ethically and responsibly will involve cross-functional team collaboration, new tools and processes, and proper support from key stakeholders.

  • 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.

  • 2 Coding-free Ways to Extract Content From Websites to Boost Web Traffic

    There are 2 main coding-free solutions for extracting content from websites to build your content base: use web scraping tools and use content aggregation tools. We review top choices.

  • 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.

  • A Tour of End-to-End Machine Learning Platforms

    An end-to-end machine learning platform needs a holistic approach. If you’re interested in learning more about a few well-known ML platforms, you’ve come to the right place!

  • 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.

  • 5 Essential Papers on AI Training Data

    Data pre-processing is not only the largest time sink for most Data Scientists, but it is also the most crucial aspect of the work. Learn more about training data and data processing tasks from 5 leading academic papers.

  • 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.

  • 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.

  • 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.

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

  • Top Machine Learning Software Tools for Developers">Gold BlogTop Machine Learning Software Tools for Developers

    As a developer who is excited about leveraging machine learning for faster and more effective development, these software tools are worth trying out.

  • Choosing a Machine Learning Model

    Selecting the perfect machine learning model is part art and part science. Learn how to review multiple models and pick the best in both competitive and real-world applications.

  • 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.

  • OpenAI Tried to Train AI Agents to Play Hide-And-Seek but Instead They Were Shocked by What They Learned

    OpenAI trained agents in a simple game of hide-and-seek and learned many other different skills in the process.

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

  • Top 10 Best Podcasts on AI, Analytics, Data Science, Machine Learning">Gold BlogTop 10 Best Podcasts on AI, Analytics, Data Science, Machine Learning

    Check out our latest Top 10 Most Popular Data Science and Machine Learning podcasts available on iTunes. Stay up to date in the field with these recent episodes and join in with the current data conversations.

  • Easy, One-Click Jupyter Notebooks

    All of the setup for software, networking, security, and libraries is automatically taken care of by the Saturn Cloud system. Data Scientists can then focus on the actual Data Science and not the tedious infrastructure work that falls around it

  • KDnuggets™ News 19:n25, Jul 10: 5 Probability Distributions for Data Scientists; What the Machine Learning Engineer Job is Really Like

    This edition of the KDnuggets newsletter is double-sized after taking the holiday week off. Learn about probability distributions every data scientist should know, what the machine learning engineering job is like, making the most money with the least amount of risk, the difference between NLP and NLU, get a take on Nvidia's new data science workstation, and much, much more.

  • Best US/Canada Masters in Analytics, Business Analytics, Data Science

    In the final part of this series, we provide an updated list of our comprehensive, unbiased survey of graduate programs in Data Science and Analytics from across the US and Canada.

  • 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.

  • Fors Marsh Group: Lead Data Scientist [Arlington, VA]

    Seeking an intelligent and motivated Lead Data Scientist (Subject Matter Expert) to grow our data science portfolio. As a part of our Advanced Analytics division, the Lead Data Scientist will have the opportunity to provide expert support on a variety of behavioral research and data science projects.

  • AI: Arms Race 2.0

    An analysis of the current state of the competition between US, Europe, and China in AI, examining research, patent publications, global datasphere, devices and IoT, people, and more.

  • The 7 Myths of Data Anonymisation

    Anonymisation has always been rather seen as a necessary evil instead of a helpful tool. That’s why plenty of myths have arisen around that technology over the years.

  • Dr. Data Show Video: What the Hell Does “Data Science” Really Mean?

    The latest episode of the Dr. Data Show answers the question, "What the hell is data science?"

  • Are you buying an apartment? How to hack competition in the real estate market

    Many real estate developers use online systems for sales. Things become interesting when all available data is monitored on a weekly basis, and sales progress is analysed.

  • Graphs Are The Next Frontier In Data Science">Gold BlogGraphs Are The Next Frontier In Data Science

    GraphConnect 2018, Neo4j’s bi-annual conference, was held in New York City in mid-September. Read about what happened, and why graphs are the next big thing in data science.

  • Raspberry Pi IoT Projects for Fun and Profit

    In this post, I will explain how to run an IoT project from the command line, without graphical interface, using Ubuntu Core in a Raspberry Pi 3.

  • The Economics and Benefits of Artificial Intelligence

    In this article, focus on current AI, which is mostly based on the algorithms that can do predictions, and discuss how the economics of AI works and how it may affect business.

  • What is it like to be a machine learning engineer in 2018?">Silver BlogWhat is it like to be a machine learning engineer in 2018?

    A personal account as to why 2018 is going to be a fun year for machine learning engineers.

  • IoT on AWS: Machine Learning Models and Dashboards from Sensor Data

    I developed my first IoT project using my notebook as an IoT device and AWS IoT as infrastructure, with this "simple" idea: collect CPU Temperature from my Notebook running on Ubuntu, send to Amazon AWS IoT, save data, make it available for Machine Learning models and dashboards.

  • Deep Learning With Apache Spark: Part 2

    In this article I’ll continue the discussion on Deep Learning with Apache Spark. I will focus entirely on the DL pipelines library and how to use it from scratch.

  • DSTI: Applied MSc in Data Engineering, Advanced MSc in AI – Learn in France

    DSTI launches 2 new programmes for October 2018 entry: Applied MSc in Data Engineering and Advanced MSc in AI - Paris, Nice, and online.

  • Data Engineer vs Data Scientist: the evolution of aggressive species

    This article looks at how the two "species" - data scientists and data engineers - harmonise and coexist.

  • How I Used CNNs and Tensorflow and Lost a Silver Medal in Kaggle Challenge

    I joined the competition a month before it ended, eager to explore how to use Deep Natural Language Processing (NLP) techniques for this problem. Then came the deception. And I will tell you how I lost my silver medal in that competition.

  • 8 Useful Advices for Aspiring Data Scientists">Gold Blog8 Useful Advices for Aspiring Data Scientists

    I recently read Sebastian Gutierrez’s “Data Scientists at Work”, in which he interviewed 16 data scientists. I want to share the best answers that these data scientists gave for the question: "What advice would you give to someone starting out in data science?"

  • To Kaggle Or Not

    Kaggle is the most well known competition platform for predictive modeling and analytics. This article looks into the different aspects of Kaggle and the benefits it can bring to data scientists.

  • Jupyter Notebook for Beginners: A Tutorial

    The Jupyter Notebook is an incredibly powerful tool for interactively developing and presenting data science projects. Although it is possible to use many different programming languages within Jupyter Notebooks, this article will focus on Python as it is the most common use case.

  • A “Weird” Introduction to Deep Learning">Silver BlogA “Weird” Introduction to Deep Learning

    There are amazing introductions, courses and blog posts on Deep Learning. But this is a different kind of introduction.

  • Blockchains and APIs

    Major technological advances are providing opportunities for new business models, based on blockchain, which will see an increase in the number of connected devices in our day-to-day lives.

  • Pew Research Center: Director, Data Labs

    Seeking a Director to lead the Data Labs team in our Washington, DC, office. Data Labs uses computational methods to complement and expand on the Center's existing research agenda.

  • Four Broken Systems & Four Tech Trends for 2018

    We may be well into 2018, but here are a set of tech trends for looking forward, along with a set of 4 systems that manifested how inappropriate, inaccurate or outright broken they are in 2017.

  • The Current Hype Cycle in Artificial Intelligence

    Over the past decade, the field of artificial intelligence (AI) has seen striking developments. As surveyed in, there now exist over twenty domains in which AI programs are performing at least as well as (if not better than) humans.

  • Age of AI Conference 2018 – Day 2 Highlights

    Here are some of the highlights from the second day of the Age of AI Conference, February 1, at the Regency Ballroom in San Francisco.

  • A Comparative Analysis of Top 6 BI and Data Visualization Tools in 2018">Silver BlogA Comparative Analysis of Top 6 BI and Data Visualization Tools in 2018

    In this article, we will compare the most commonly used platforms and analyze their main features to help you choose one or several platforms that will provide indispensable aid for your work communication.

  • KDnuggets™ News 18:n04, Jan 24: TensorFlow vs XGBoost; Machine Learning Pipelines in Python; Semi-Supervised Machine Learning

    Gradient Boosting in TensorFlow vs XGBoost; Managing Machine Learning Workflows with Scikit-learn Pipelines Part 2; Using Genetic Algorithm for Optimizing Recurrent Neural Networks; The Value of Semi-Supervised Machine Learning; Comparing Machine Learning as a Service: Amazon, Microsoft Azure, Google Cloud AI

  • How Docker Can Help You Become A More Effective Data Scientist">Silver BlogHow Docker Can Help You Become A More Effective Data Scientist

    I wrote this quick primer so you don’t have to parse all the information out there and instead can learn the things you need to know to quickly get started.

  • Supercharging Visualization with Apache Arrow

    Interactive visualization of large datasets on the web has traditionally been impractical. Apache Arrow provides a new way to exchange and visualize data at unprecedented speed and scale.

  • 70 Amazing Free Data Sources You Should Know">Silver Blog70 Amazing Free Data Sources You Should Know

    70 free data sources for 2017 on government, crime, health, financial and economic data, marketing and social media, journalism and media, real estate, company directory and review, and more to start working on your data projects.

  • SlashData: Technology Analyst & Author

    Seeking a Technology Analyst & Author to support our developer research efforts. The Technology Analyst & Author will be responsible for analysing our Developer Economics survey data to deliver cutting-edge insights and reports on the future of software.

  • Best Masters in Data Science and Analytics in US/Canada

    Second comprehensive list of master's degrees in the US and Canada with tuition information and duration.

  • Key Takeaways from Open Data Science Conference (ODSC) West 2017

    This year, the ODSC West was held at the Hyatt Regency San Francisco Airport, from November 2 to 4. I am, attempting here, to give you a snapshot tour of what I experienced.

  • Choosing an Open Source Machine Learning Library: TensorFlow, Theano, Torch, scikit-learn, Caffe

    Open Source is the heart of innovation and rapid evolution of technologies, these days. Here we discuss how to choose open source machine learning tools for different use cases.

  • Introducing R-Brain: A New Data Science Platform

    R-Brain is a next generation platform for data science built on top of Jupyterlab with Docker, which supports not only R, but also Python, SQL, has integrated intellisense, debugging, packaging, and publishing capabilities.

Refine your search here: