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  • Artificial Intelligence
  • Researchers Have Created an AI That Is Naturally Curious

    Researchers have successfully given AI a curiosity implant, which motivated it to explore a virtual environment. This could be the bridge between AI and real world application.

    Source: Futurism
    10020
  • What Is Artificial Intelligence, Really?

    In popular media, "Artificial Intelligence" is by turns godlike, monstrous, uncannily human and a hoax; it inspires both awe and deep suspicion - it's unnatural.

    Source: Huffingtonpost
    9564
  • New Algorithm Lets AI Learn From Mistakes, Become a Little More Human

    OpenAI continues to make strides in reinforcement learning algorithms for training artificial intelligence agents. Their latest platform, released late February, allows AIs to learn from their mistakes by looking at them as goals instead of failures.

    Source: Futurism
    9876
  • The Best Machine Learning Resources! How to learn machine learning?

    A compendium of resources for crafting a curriculum on artificial intelligence, machine learning, and deep learning.

    Source: Medium
    24267
  • Machine Learning Explained: Understanding Supervised, Unsupervised, and Reinforcement Learning

    Once we start delving into the concepts behind Artificial Intelligence (AI) and Machine Learning (ML), we come across copious amounts of jargon related to this field of study. Understanding this jargon and how it can have an impact on the study related to ML goes a long way in comprehending the study that has been conducted by researchers and data scientists to get AI to the state it now is.

    Source: DSC
    21063
  • 5 Things You Need to Know about Reinforcement Learning

    With the popularity of Reinforcement Learning continuing to grow, we take a look at five things you need to know about RL.

    Source: Kdnugget
    21444
  • AI-Based Operation: Learnings for Business and Technical Managers

    Using Reinforcement Learning to Tackle CitiBike Rebalancing Problems and Beyond

    Source: HOB
    10482
  • Machine Learning: Applications, Types and How Does it Work?

    Machine learning is changing the way we do things, and it has started becoming main-stream very quickly. While many factors have contributed to this increase in machine learning, one reason is that it is becoming easier for developers to apply it. And, that is through open source frameworks.

    Source: HOB
    15453
  • 5 EBooks to Read Before Getting into A Machine Learning Career

    A carefully-curated list of 5 free ebooks to help you better understand the various aspects of what machine learning, and skills necessary for a career in the field.

    Source: HOB
    16296
  • 5 Free Online EBooks To Read Before Getting Into Machine Learning Career

    A carefully-curated list of 5 free ebooks to help you better understand the various aspects of what machine learning, and skills necessary for a career in the field.

    Source: HOB
    15480
  • How Organizations Are Using Machine Learning Platforms?

    Machine Learning algorithms enable software applications to predict more accurately outcomes. Most of the organizations use top machine learning platforms to build the models that can receive data from the various sources.

    Source: HOB
    14418
  • How Reinforcement Learning Will Be 2019's Biggest Demand In Data Science

    What will be the next thing to revolutionize data science in 2019? originally appeared on Quora: the place to gain and share knowledge, empowering people to learn from others and better understand the world.

    Source: HOB
    7830
  • Data Science The Backbone Of Artificial Intelligence

    Artifical Intelligence is a collection of data science technologies that at this point in development are not even particularly well integrated or even easy to use. In each of these areas however, we've made a lot of progress and that's caught the attention of the popular press.

    Source: HOB
    6141
  • Data Science The Backbone Of Artificial Intelligence

    Artifical Intelligence is a collection of data science technologies that at this point in development are not even particularly well integrated or even easy to use. In each of these areas however, we've made a lot of progress and that's caught the attention of the popular press.

    Source: HOB
    12276
  • Future Through The Eyes Of Data Science

    Once the Business Intelligence reports and dashboards have been prepared and insights which are extracted from them, this information becomes the basis for predicting future values. And the accuracy of these predictions lies in the methods used.

    Source: HOB
    23361
  • Top 3 Features Make Programming Languages Popular In World- According to Github

    GitHub has identified three features that make a programming language popular in 2018.

    Source: HOB
    8223
  • Know How Deep Learning Gives Base To Robots?

    The majority of artificial intelligence (AI) research to date has been focused on the vision. Thanks to machine learning, and in particular deep learning, we now have robots and devices that have a pretty good visual understanding of their environment.

    Source: HOB
    11103
  • Reinforcement Learning The New Drift For Data Science

    Reinforcement Learning (RL) could be a machine learning methodology that empowers a specialist to be told in Associate in Nursing intuitive setting by activity trial and error utilizing observations from its terribly own activities and encounters.

    Source: HOB
    12549
  • Five Must-Read Books of Machine Learning and Data Science

    We are here to provide you a list of 5 books which will play a key factor in honing your skills in Data Science and Machine Learning and sets you apart from your competitors.

    Source: HOB
    9270
  • Most Popular 10 Free Courses For Machine Learning & Data Science

    Here are 10 machine learning courses to help with your spring learning season. Courses range from introductory machine learning to deep learning to natural language processing and beyond.

    Source: HOB
    17409
  • Are Deep Learning and Reinforcement Learning Different? What sets them apart?

    The main difference behind Deep Learning from Reinforcement Learning is that while Deep learning uses example sets to achieve the desired results, Reinforcement Learning learns after every reward given to it.

    Source: HOB
    10137
  • Top 5 EBooks All Engineer Should Read Before Getting Into A Machine Learning Career

    A carefully curated list of 5 free ebooks to help you better understand the various aspects of what machine learning, and skills necessary for a career in the field.

    Source: HOB
    7326
  • What are some of the Most Important Machine Learning Interview Questions

    Cracking a Machine Learning interview is the hardest thing for any aspirant. For the same purpose, I have come up with this article to share with you a YouTube video of the most important questions that are asked in a Machine Learning Interview.

    Source: HOB
    7734
  • Which are the Best Books on Reinforcement Learning

    Books are always the best sources to explore while learning a new thing. Reinforcement Learning has finds its huge applications in recent times with categories like Autonomous Driving, Computer Vision, Robotics, Education and many others. Here are some best books on Reinforcement Learning that you can easily find on Amazon

    Source: HOB
    25338
  • How a Business Analyst Differ from a Data Scientist

    A Business Analyst is often confused with a Data Scientist. So I have written this article to clear out the differences between the two with the help of clear bullet points.

    Source: HOB
    6561
  • Develop Concepts in Deep Learning today with this amazing YouTube Video

    Deep Learning is a part or subfield of Machine Learning which uses artificial neural networks to enable machines to learn and perform complex tasks without any human intervention.

    Source: HOB
    7494
  • Taking an Artificial Intelligence certification revamps your career options

    Learn the fundamentals of Artificial Intelligence (AI), and apply them. Design intelligent agents to solve real-world problems including, search, games, machine learning, logic, and constraint satisfaction problems.

    Source: HOB
    5340
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