Forwarded from Artificial Intelligence
Google Faculty Research Awards 2018
http://ai.googleblog.com/2019/03/google-faculty-research-awards-2018.html
http://ai.googleblog.com/2019/03/google-faculty-research-awards-2018.html
Googleblog
Google Faculty Research Awards 2018
Robotic Control with Graph Networks
Exploiting relational inductive bias to improve generalization and control
https://towardsdatascience.com/robotic-control-with-graph-networks-f1b8d22b8c86
Exploiting relational inductive bias to improve generalization and control
https://towardsdatascience.com/robotic-control-with-graph-networks-f1b8d22b8c86
Medium
Robotic Control with Graph Networks
Exploiting relational inductive bias to improve generalization and control
DeepLearning.AI Convolutional Neural Networks Deep Learning Specialization Course (Review)
https://machinelearningmastery.com/deeplearning-ai-convolutional-neural-networks-deep-learning-specialization-review/
https://machinelearningmastery.com/deeplearning-ai-convolutional-neural-networks-deep-learning-specialization-review/
Machine Learning Mastery
DeepLearning.AI Convolutional Neural Networks Course (Review) - Machine Learning Mastery
Andrew Ng is famous for his Stanford machine learning course provided on Coursera.
In 2017, he released a five-part course on deep learning also on Coursera titled
In 2017, he released a five-part course on deep learning also on Coursera titled
Forwarded from Artificial Intelligence
DeepMind: This Card Game Is the Next Frontier for AI Research
https://www.youtube.com/watch?v=cD-eXjf854Q
https://www.youtube.com/watch?v=cD-eXjf854Q
YouTube
DeepMind: The Hanabi Card Game Is the Next Frontier for AI Research
📝 The paper "The Hanabi Challenge: A New Frontier for AI Research" and a blog post is available here:
https://arxiv.org/abs/1902.00506
http://www.marcgbellemare.info/blog/a-cooperative-benchmark-announcing-the-hanabi-learning-environment/
❤️ Pick up cool…
https://arxiv.org/abs/1902.00506
http://www.marcgbellemare.info/blog/a-cooperative-benchmark-announcing-the-hanabi-learning-environment/
❤️ Pick up cool…
A Complete Exploratory Data Analysis and Visualization for Text Data
https://towardsdatascience.com/a-complete-exploratory-data-analysis-and-visualization-for-text-data-29fb1b96fb6a
https://towardsdatascience.com/a-complete-exploratory-data-analysis-and-visualization-for-text-data-29fb1b96fb6a
Medium
A Complete Exploratory Data Analysis and Visualization for Text Data
How to combine visualization and NLP in order to generate insights in an intuitive way
Measuring the Limits of Data Parallel Training for Neural Networks
http://ai.googleblog.com/2019/03/measuring-limits-of-data-parallel.html
http://ai.googleblog.com/2019/03/measuring-limits-of-data-parallel.html
research.google
Measuring the Limits of Data Parallel Training for Neural Networks
Posted by Chris Shallue, Senior Software Engineer and George Dahl, Senior Research Scientist, Google AI Over the past decade, neural networks have ...
Stanford Convolutional Neural Networks for Visual Recognition Course (Review)
https://machinelearningmastery.com/stanford-convolutional-neural-networks-for-visual-recognition-course-review/
https://machinelearningmastery.com/stanford-convolutional-neural-networks-for-visual-recognition-course-review/
MachineLearningMastery.com
Stanford Convolutional Neural Networks for Visual Recognition Course (Review) - MachineLearningMastery.com
The Stanford course on deep learning for computer vision is perhaps the most widely known course on the topic. This is not surprising given that the course has been running for four years, is presented by top academics and researchers in the field, and the…
Adaptive - and Cyclical Learning Rates using PyTorch
The Learning Rate (LR) is one of the key parameters to tune. Using PyTorch, we’ll check how the common ones hold up against CLR!
https://medium.com/@thomas_dehaene/adaptive-and-cyclical-learning-rates-using-pytorch-2bf904d18dee
The Learning Rate (LR) is one of the key parameters to tune. Using PyTorch, we’ll check how the common ones hold up against CLR!
https://medium.com/@thomas_dehaene/adaptive-and-cyclical-learning-rates-using-pytorch-2bf904d18dee
Medium
Adaptive - and Cyclical Learning Rates using PyTorch
The Learning Rate (LR) is one of the key parameters to tune. Using PyTorch, we’ll check how the common ones hold up against CLR!
8 Excellent Pretrained Models to get you Started with Natural Language Processing (NLP)
https://www.analyticsvidhya.com/blog/2019/03/pretrained-models-get-started-nlp/
https://www.analyticsvidhya.com/blog/2019/03/pretrained-models-get-started-nlp/
Analytics Vidhya
8 Excellent Pretrained Models to get you Started with Natural Language Processing (NLP)
This article contains some pretrained models to get started with natural language processing. This NLP pretrained model helps you to learn deep learning.
How to Load and Manipulate Images for Deep Learning in Python With PIL/Pillow
https://machinelearningmastery.com/how-to-load-and-manipulate-images-for-deep-learning-in-python-with-pil-pillow/
https://machinelearningmastery.com/how-to-load-and-manipulate-images-for-deep-learning-in-python-with-pil-pillow/
MachineLearningMastery.com
How to Load and Manipulate Images for Deep Learning in Python With PIL/Pillow - MachineLearningMastery.com
Before you can develop predictive models for image data, you must learn how to load and manipulate images and photographs. The most popular and de facto standard library in Python for loading and working with image data is Pillow. Pillow is an updated version…
MIT 6.S191: Visualization for Machine Learning (Google Brain)
https://www.youtube.com/watch?v=ulLx2iPTIcs
https://www.youtube.com/watch?v=ulLx2iPTIcs
YouTube
MIT 6.S191 (2019): Visualization for Machine Learning (Google Brain)
MIT Introduction to Deep Learning 6.S191: Lecture 7
Data Visualization for Machine Learning
Lecturer: Fernanda Viegas
Google Brain Guest Lecture
January 2019
For all lectures, slides and lab materials: http://introtodeeplearning.com
Data Visualization for Machine Learning
Lecturer: Fernanda Viegas
Google Brain Guest Lecture
January 2019
For all lectures, slides and lab materials: http://introtodeeplearning.com
Forwarded from Artificial Intelligence
Stanford CS230: Deep Learning | Autumn 2018 | Lecture 1 - Class Introduction and Logistics
https://www.youtube.com/watch?v=PySo_6S4ZAg
https://www.youtube.com/watch?v=PySo_6S4ZAg
YouTube
Stanford CS230: Deep Learning | Autumn 2018 | Lecture 1 - Class Introduction & Logistics, Andrew Ng
For more information about Stanford's Artificial Intelligence professional and graduate programs visit: https://stanford.io/3eJW8yT
Andrew Ng is an Adjunct Professor, Computer Science at Stanford University.
Kian Katanforoosh is a Lecturer, Computer Science…
Andrew Ng is an Adjunct Professor, Computer Science at Stanford University.
Kian Katanforoosh is a Lecturer, Computer Science…
6.883 Science of Deep Learning: Bridging Theory and Practice -- Spring 2018
https://people.csail.mit.edu/madry/6.883/
https://people.csail.mit.edu/madry/6.883/
Deep Learning Examples
https://developer.nvidia.com/deep-learning-examples
https://developer.nvidia.com/deep-learning-examples
NVIDIA Developer
Deep Learning Examples
Deep Learning Explainability: Hints from Physics
https://towardsdatascience.com/deep-learning-explainability-hints-from-physics-2f316dc07727
https://towardsdatascience.com/deep-learning-explainability-hints-from-physics-2f316dc07727
Medium
Deep Learning Explainability: Hints from Physics
Deep Neural Networks from a Physics Viewpoint
Forwarded from Artificial Intelligence
Simulated Policy Learning in Video Models
http://ai.googleblog.com/2019/03/simulated-policy-learning-in-video.html
http://ai.googleblog.com/2019/03/simulated-policy-learning-in-video.html
Googleblog
Simulated Policy Learning in Video Models
Variational inference for Bayesian neural networks
https://krasserm.github.io/2019/03/14/bayesian-neural-networks/
https://krasserm.github.io/2019/03/14/bayesian-neural-networks/
How to Evaluate Pixel Scaling Methods for Image Classification With Convolutional Neural Networks
https://machinelearningmastery.com/how-to-evaluate-pixel-scaling-methods-for-image-classification/
https://machinelearningmastery.com/how-to-evaluate-pixel-scaling-methods-for-image-classification/
MachineLearningMastery.com
How to Evaluate Pixel Scaling Methods for Image Classification With CNNs - MachineLearningMastery.com
Image data must be prepared before it can be used as the basis for modeling in image classification tasks. One aspect of preparing image data is scaling pixel values, such as normalizing the values to the range 0-1, centering, standardization, and more. How…