Cutting Edge Deep Learning
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πŸ“• Deep learning
πŸ“— Reinforcement learning
πŸ“˜ Machine learning
πŸ“™ Papers - tools - tutorials

πŸ”— Other Social Media Handles:
https://linktr.ee/cedeeplearning
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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 β€” 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
πŸ”» Open Images V6 β€” 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.

πŸ“Œ Via: @cedeeplearning

link: https://ai.googleblog.com/search?updated-max=2020-03-11T09:00:00-07:00&max-results=10

#image_detection
#machinelearning
#deeplearning
πŸ”ΉHow 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.

πŸ”»If you already have a basic understanding of linear algebra, #calculus, #probability and #programming: I recommend starting with Stanford’s CS231n.

πŸ”»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.

πŸ“ŒVia: @cedeeplearning

link: https://www.kdnuggets.com/2016/07/start-learning-deep-learning.html
πŸ”»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: (πŸ”»click on the link to the rest)

link: https://www.kdnuggets.com/2016/12/ibm-predictions-deep-learning-2017.html

πŸ“ŒVia: @cedeeplearning