Tinker With a Neural Network Right Here in Your Browser
This was created by Daniel Smilkov and Shan Carter. This is a continuation of many people’s previous work — most notably Andrej Karpathy’s #convnet.js demo and Chris Olah’s articles about neural networks.
Via: @cedeeplearning
https://playground.tensorflow.org/
#visualization #neural_networks
This was created by Daniel Smilkov and Shan Carter. This is a continuation of many people’s previous work — most notably Andrej Karpathy’s #convnet.js demo and Chris Olah’s articles about neural networks.
Via: @cedeeplearning
https://playground.tensorflow.org/
#visualization #neural_networks
playground.tensorflow.org
Tensorflow — Neural Network Playground
Tinker with a real neural network right here in your browser.
🔻 Open Images V6 — Now Featuring Localized Narratives
Open Images is the largest annotated image dataset in many regards, for use in training the latest deep #convolutional #neural_networks for #computer_vision tasks. With the introduction of version 5 last May, the Open Images dataset includes 9M images annotated with 36M image-level labels, 15.8M bounding boxes, 2.8M instance #segmentations, and 391k visual relationships. Along with the dataset itself, the associated Open Images Challenges have spurred the latest advances in #object_detection, instance segmentation, and visual relationship detection.
📌 Via: @cedeeplearning
link: https://ai.googleblog.com/search?updated-max=2020-03-11T09:00:00-07:00&max-results=10
#image_detection
#machinelearning
#deeplearning
Open Images is the largest annotated image dataset in many regards, for use in training the latest deep #convolutional #neural_networks for #computer_vision tasks. With the introduction of version 5 last May, the Open Images dataset includes 9M images annotated with 36M image-level labels, 15.8M bounding boxes, 2.8M instance #segmentations, and 391k visual relationships. Along with the dataset itself, the associated Open Images Challenges have spurred the latest advances in #object_detection, instance segmentation, and visual relationship detection.
📌 Via: @cedeeplearning
link: https://ai.googleblog.com/search?updated-max=2020-03-11T09:00:00-07:00&max-results=10
#image_detection
#machinelearning
#deeplearning
🔹How to Start Learning Deep Learning
Want to get started #learning_deep learning? Sure you do! Check out this great overview, advice, and list of resources.
Due to the recent achievements of artificial #neural_networks across many different tasks (such as face #recognition, object detection and Go), deep learning has become extremely popular. This post aims to be a starting point for those interested in learning more about it.
🔻If you already have a basic understanding of linear algebra, #calculus, #probability and #programming: I recommend starting with Stanford’s CS231n.
🔻If you don’t have the relevant math background: There is an incredible amount of free material online that can be used to learn the required math knowledge. Gilbert Strang’s course on #linear_algebra is a great introduction to the field. For the other subjects, edX has courses from MIT on both calculus and probability.
📌Via: @cedeeplearning
link: https://www.kdnuggets.com/2016/07/start-learning-deep-learning.html
Want to get started #learning_deep learning? Sure you do! Check out this great overview, advice, and list of resources.
Due to the recent achievements of artificial #neural_networks across many different tasks (such as face #recognition, object detection and Go), deep learning has become extremely popular. This post aims to be a starting point for those interested in learning more about it.
🔻If you already have a basic understanding of linear algebra, #calculus, #probability and #programming: I recommend starting with Stanford’s CS231n.
🔻If you don’t have the relevant math background: There is an incredible amount of free material online that can be used to learn the required math knowledge. Gilbert Strang’s course on #linear_algebra is a great introduction to the field. For the other subjects, edX has courses from MIT on both calculus and probability.
📌Via: @cedeeplearning
link: https://www.kdnuggets.com/2016/07/start-learning-deep-learning.html
🔻Predictions for Deep Learning in 2017
The first hugely successful consumer application of deep learning will come to market, a dominant #open-source deep-learning tool and library will take the developer community by storm, and more Deep Learning predictions.
Deep learning is all the rage as we move into 2017. Grounded in #multilayer #neural_networks, this technology is the foundation of artificial intelligence, #cognitive computing, and #real-time streaming #analytics in many of the most disruptive new #applications.
For data scientists, #deep_learning will be a top professional focus going forward. Here are my #predictions for the chief #trends in deep learning in the coming year: (🔻click on the link to the rest)
link: https://www.kdnuggets.com/2016/12/ibm-predictions-deep-learning-2017.html
📌Via: @cedeeplearning
The first hugely successful consumer application of deep learning will come to market, a dominant #open-source deep-learning tool and library will take the developer community by storm, and more Deep Learning predictions.
Deep learning is all the rage as we move into 2017. Grounded in #multilayer #neural_networks, this technology is the foundation of artificial intelligence, #cognitive computing, and #real-time streaming #analytics in many of the most disruptive new #applications.
For data scientists, #deep_learning will be a top professional focus going forward. Here are my #predictions for the chief #trends in deep learning in the coming year: (🔻click on the link to the rest)
link: https://www.kdnuggets.com/2016/12/ibm-predictions-deep-learning-2017.html
📌Via: @cedeeplearning