1. Articles from oreilly.com

  2. 1-19 of 19
    1. How Ray makes continuous learning accessible and easy to scale

      How Ray makes continuous learning accessible and easy to scale

      Data Show Podcast How Ray makes continuous learning accessible and easy to scale The O’Reilly Data Show Podcast: Robert Nishihara and Philipp Moritz on a new framework for reinforcement learning and AI applications. Subscribe to the O'Reilly Data Show Podcast to explore the opportunities and techniques driving big data, data science, and AI. Find us on Stitcher , TuneIn , iTunes , SoundCloud , RSS .

      Read Full Article
      Mentions: UC Berkeley MPI
    2. The wisdom hierarchy: From signals to artificial intelligence and beyond

      The wisdom hierarchy: From signals to artificial intelligence and beyond

      The wisdom hierarchy: From signals to artificial intelligence and beyond A framework for moving from data to wisdom. August 1, 2017 Pyramid pattern. (source: Pixabay ) In " Learning to Love Data Science ," Mike Barlow looks at how organizations are using data science to turn information into wisdom and business value. Download a collection of free chapters from "Learning to Love Data Science ." We are swimming in data. Or possibly drowning in it.

      Read Full Article
      Mentions: Hadoop Intel IBM
    3. R’s tidytext turns messy text into valuable insight

      R’s tidytext turns messy text into valuable insight

      Data science R’s tidytext turns messy text into valuable insight Authors Julia Silge and David Robinson discuss the power of tidy data principles, sentiment lexicons, and what they're up to at Stack Overflow. Woodtype (source: Pixabay ) Check out " Text Mining with R: A tidy approach " to learn about how tidy data principles and the tidytext package can help you perform text mining in R. “Many of us who work in analytical fields are not trained in even simple interpretation of natural language,” write Julia Silge, Ph.D., and David Robinson, Ph.D., in their newly released book Text ...

      Read Full Article
    4. How AI is used to infer human emotion

      How AI is used to infer human emotion

      How AI is used to infer human emotion Rana el Kaliouby discusses the techniques, possibilities, and challenges around emotion AI today. For more on the untapped opportunities of applied AI, check out O'Reilly Artificial Intelligence Conference in New York, June 26-29, 2017 . Rana el Kaliouby is the co-founder and CEO of Affectiva , an emotion measurement technology company that grew out of MIT's Media Lab.

      Read Full Article
    5. Caption this, with TensorFlow

      Caption this, with TensorFlow

      By Raul Puri Daniel Ricciardelli March 28, 2017 The image caption generation model. (source: Shannon Shih from Machine Learning at Berkeley. Horse Image sourced from MS COCO.) . Attention readers: We invite you to access the corresponding Python code and iPython notebooks for this article on GitHub . Image caption generation models combine recent advances in computer vision and machine translation to produce realistic image captions using neural networks.

      Read Full Article
      Mentions: Berkeley Google
    6. Genevieve Bell on moving from human-computer interactions to human-computer relationships

      Genevieve Bell on moving from human-computer interactions to human-computer relationships

      Radar Podcast Genevieve Bell on moving from human-computer interactions to human-computer relationships The O’Reilly Radar Podcast: AI on the hype curve, imagining nurturing technology, and gaps in the AI conversation. By Jenn Webb January 26, 2017 Subscribe to the O'Reilly Radar Podcast to track the technologies and people that will shape our world in the years to come. Find us on Stitcher , TuneIn , iTunes , SoundCloud , RSS .

      Read Full Article
      Mentions: Amazon Intel Google
    7. Richard Socher on the future of deep learning

      Richard Socher on the future of deep learning

      Richard Socher on the future of deep learning The O’Reilly Bots Podcast: Making neural networks more accessible. A two-layer feedforward artificial neural network. (source: Akritasa on Wikimedia Commons ). Bots Podcast to learn about advances in conversational user interfaces, artificial intelligence, and messaging that are revolutionizing the way we interact with software.

      Read Full Article
    8. The hard thing about deep learning

      The hard thing about deep learning

      The hard thing about deep learning Deeper neural nets often yield harder optimization problems. Rastrigin Function. (source: Diegotorquemada on Wikimedia Commons ). At the heart of deep learning lies a hard optimization problem. So hard that for several decades after the introduction of neural networks, the difficulty of optimization on deep neural networks was a barrier to their mainstream usage and contributed to their decline in the 1990s and 2000s . Since then, we have overcome this issue.

      Read Full Article
    9. Compressing and regularizing deep neural networks

      Compressing and regularizing deep neural networks

      Compressing and regularizing deep neural networks Improving prediction accuracy using deep compression and DSD training. "Directed Differentiation of Multipotential Human Neural Progenitor Cells." (source: National Institutes of Health on Wikimedia Commons ). Deep neural networks have evolved to be the state-of-the-art technique for machine learning tasks ranging from computer vision and speech recognition to natural language processing.

      Read Full Article
      Mentions: Sram CNN Baidu
    10. Complex neural networks made easy by Chainer

      Complex neural networks made easy by Chainer

      Complex neural networks made easy by Chainer A define-by-run approach allows for flexibility and simplicity when building deep learning networks. Neurons. (source: Pixabay ). Chainer is an open source framework designed for efficient research into and development of deep learning algorithms. In this post, we briefly introduce Chainer with a few examples and compare with other frameworks such as Caffe, Theano, Torch, and Tensorflow.

      Read Full Article
    11. Four short links: 28 September 2016

      Four short links: 28 September 2016

      Four short links: 28 September 2016 Offline First, Machine Translation, Kernel Security, and Javascript Maps September 28, 2016 Four short links Offline First -- as Google designs for the Rest of The World, they're learning to build entirely different priorities and assumptions into their software. Meet YouTube Go: a new YouTube app built from scratch to bring YouTube to the next generation of viewers. YouTube Go is designed with four concepts in mind.

      Read Full Article
      Mentions: Google The World
    12. Alyona Medelyan on applications of NLU

      Alyona Medelyan on applications of NLU

      Emerging Tech Alyona Medelyan on applications of NLU The O'Reilly Radar Podcast: Natural language understanding and natural language processing applications, our future with chatbots, and open source indexing. Subscribe to the O'Reilly Radar Podcast to track the technologies and people that will shape our world in the years to come. Find us on Stitcher , TuneIn , iTunes , SoundCloud , RSS This week, I talk with Alyona Medelyan , co-founder and CEO at Thematic and founder and CEO at Entopix .

      Read Full Article
      Mentions: Apple Google Portugal
    13. To supervise or not to supervise in Ai? - O'Reilly Media

      To supervise or not to supervise in Ai? - O'Reilly Media

      To supervise or not to supervise in AI? If you look carefully at how humans learn, you see surprisingly little unsupervised learning. "First Steps, after Millet," Vincent van Gogh, 1890. (source: Metropolitan Museum of Art on Wikimedia Commons ). To learn more about opportunities in applied AI, join us at the O'Reilly Artificial Intelligence Conference , September 26-27, 2016 in New York.

      Read Full Article
    14. Three tips for getting started with Nlu - O'Reilly Media

      Three tips for getting started with Nlu - O'Reilly Media

      Three tips for getting started with NLU Natural language understanding: What it is and where to begin. By Alyona Medelyan May 26, 2016 Languages of the Roman Empire: Bound lead tablets of magic inscriptions (300–500 AD). (source: Kircherian Museum on Wikimedia Commons ). What makes a cartoon caption funny?

      Read Full Article
    15. Data’s international impact on manufacturing - O'Reilly Media

      Data’s international impact on manufacturing - O'Reilly Media

      Data’s international impact on manufacturing A new O’Reilly report explores global trends in data analytics for the Industrial IoT. Editor’s note: This is an excerpt from the upcoming report Data Science for Modern Manufacturing: Global Trends: Big Data Analytics for the Industrial Internet of Things , by Li Ping Chu. Sign up to be notified when the report becomes available .

      Read Full Article
      Mentions: Google IBM
    16. Data’s international impact on manufacturing

      Data’s international impact on manufacturing

      Data’s international impact on manufacturing A new O’Reilly report explores global trends in data analytics for the Industrial IoT. Editor’s note: This is an excerpt from the upcoming report Data Science for Modern Manufacturing: Global Trends: Big Data Analytics for the Industrial Internet of Things , by Li Ping Chu. Sign up to be notified when the report becomes available .

      Read Full Article
      Mentions: Google IBM
    1-19 of 19
  1. Categories

    1. Default:

      Discourse, Entailment, Machine Translation, NER, Parsing, Segmentation, Semantic, Sentiment, Summarization, WSD