GAN Lab: Play with Generative Adversarial Networks (GANs) in your browser!
By created by Minsuk Kahng, Nikhil Thorat, Polo Chau, Fernanda Viégas, and Martin Wattenberg: https://poloclub.github.io/ganlab/
Research paper: https://minsuk.com/research/papers/kahng-ganlab-vast2018.pdf
#AI #ArtificialIntelligence #GenerativeAdversarialNetworks

Build your own robotic cat!
Blog by Alex Bate: https://www.raspberrypi.org/blog/robotic-cat-petoi-nybble/
#Arduino #ArtificialIntelligence #MachineLearning #RaspberryPi #Robotics

TensorSpace is a neural network 3D visualization framework
Built on TensorFlow.js, Three.js and Tween.js: https://tensorspace.org
#NeuralNetworks #ArtificialInteligence #DeepLearning #MachineLearning #Visualization

Machine learning top 10 articles for the past month (v.June 2019)

https://medium.com/@Mybridge/machine-learning-top-10-articles-for-the-past-month-v-june-2019-be8235cabaf0 https://t.iss.one/ArtificialIntelligenceArticles

Spinning Up in Deep RL
By Joshua Achiam: https://openai.com/blog/spinning-up-in-deep-rl/
#ArtificialIntelligence #DeepLearning #ReinforcementLearning

Complex-YOLO: Real-time 3D Object Detection on Point Clouds

Simon et al.: https://arxiv.org/abs/1803.06199

#ArtificialIntelligence #ComputerVision #DeepLearning #MachineLearning #PatternRecognition

The Best of AI: New Articles Published This Month (May 2019)
https://blog.sicara.com/05-2019-best-ai-new-articles-this-month-4a202e173e5a

Speech Model Pre-training for End-to-End Spoken Language Understanding

Lugosch et al.: https://arxiv.org/abs/1904.03670

#spokenlanguageunderstanding #machinelearning #technology

Python Machine Learning (2nd Ed.) Code Repository

By Sebastian Raschka et al.: https://github.com/rasbt/python-machine-learning-book-2nd-edition

#100DaysOfMLCode #Clustering #DataScience #DeepLearning #MachineLearning #NeuralNetworks #Programming #SupervisedLearning

"Neural Networks for Machine Learning by Geoffrey Hinton" (Coursera 2013)

Video Lectures: https://www.youtube.com/watch?v=2fRnHVVLf1Y&list=PLiPvV5TNogxKKwvKb1RKwkq2hm7ZvpHz0

#100DaysOfMLCode #ArtificialIntelligence #DeepLearning #NeuralNetworks #MachineLearning

Data Pre-processing for Deep Learning models (Deep Learning with Keras – Part 2)
Training deep learning models requires data… A lot of data! Unfortunately, in most cases data comes messy, and our models are very sensitive towards this. Therefore, we need to be careful while preparing our data to achieve the best results.

Part 1- https://www.marktechpost.com/2019/06/11/deep-learning-with-keras-tutorial-part-1/

Part 2- https://www.marktechpost.com/2019/06/14/deep-learning-wit...
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They say a picture is worth a thousand words. Sure, but the real trick is realizing a bot that draws pictures is using only a dozen. Throw in an ability to visualize an entire story and one day this bot could be working in the movies. #CVPR2019

https://www.microsoft.com/en-us/research/blog/a-picture-from-a-dozen-words-a-drawing-bot-for-realizing-everyday-scenes-and-even-stories/?OCID=msr_blog_drawbot_cvpr_tw

List of 200+ Data Science Blogs https://www.cybrhome.com/topic/data-science-blogs

new course :

3 days until you can take Course 3 of the TensorFlow Specialization! Andrew ng and Laurence discuss what it takes to process text, one of the foundational skills for building NLP applications.

Enroll in Course 1 and 2 if you haven't already:

https://www.coursera.org/collections/tensorflow-deeplearning-ai

https://t.iss.one/ArtificialIntelligenceArticles

deeplearning.ai

Reweighted Expectation Maximization
Adji B. Dieng and John Paisley: https://arxiv.org/abs/1906.05850
Code: https://github.com/adjidieng/REM
#ArtificialIntelligence #MachineLearning #VariationalAutoEncoders #VAEs

Turing's Neural Networks of 1948
By Jack Copeland and Diane Proudfoot

https://www.alanturing.net/turing_archive/pages/Reference%20Articles/connectionism/Turing's%20neural%20networks.html?fbclid=IwAR0B5k0lmUY2JNwV0aMgMvTkmb-lFlW7j4R8RID632rvkhGEdSYB3Vd_uv4

Contrastive Multiview Coding
Unsupervised representation learned to maximize MI measure between different views results in SoTA for downstream tasks such as object classification

code https://github.com/HobbitLong/CMC/

paper https://arxiv.org/pdf/1906.05849.pdf

Divide and Conquer the Embedding Space for Metric Learning, CVPR 2019

code https://github.com/CompVis/metric-learning-divide-and-conquer

Topology-Preserving Deep Image Segmentation. arxiv.org/abs/1906.05404

“CVPR 2019 | Waymo Introduces Open Dataset to Accelerate Autonomous Driving Research”
https://medium.com/syncedreview/cvpr-2019-waymo-introduces-open-dataset-to-accelerate-autonomous-driving-research-38d9dcde032c