Exploring Univariate Data
Using Super Hero data to get started with univariate EDA in Python
https://towardsdatascience.com/exploring-univariate-data-e7e2dc8fde80
Using Super Hero data to get started with univariate EDA in Python
https://towardsdatascience.com/exploring-univariate-data-e7e2dc8fde80
Forwarded from Artificial Intelligence
9 Applications of Deep Learning for Computer Vision
https://machinelearningmastery.com/applications-of-deep-learning-for-computer-vision/
https://machinelearningmastery.com/applications-of-deep-learning-for-computer-vision/
MachineLearningMastery.com
9 Applications of Deep Learning for Computer Vision - MachineLearningMastery.com
The field of computer vision is shifting from statistical methods to deep learning neural network methods. There are still many challenging problems to solve in computer vision. Nevertheless, deep learning methods are achieving state-of-the-art results on…
The Latest Machine Learning Trends From MIT Professors and Researchers
https://hackernoon.com/the-latest-machine-learning-trends-from-mit-professors-and-researchers-cdda345dd207
https://hackernoon.com/the-latest-machine-learning-trends-from-mit-professors-and-researchers-cdda345dd207
Hackernoon
The Latest Machine Learning Trends From MIT Professors and Researchers | HackerNoon
The Massachusetts Institute of Technology (<a href="http://web.mit.edu/" target="_blank">MIT</a>) has always been known for its pioneering research, making a lasting difference to today’s technologically focused environment. And with machine learning development…
Harnessing Organizational Knowledge for Machine Learning
http://ai.googleblog.com/2019/03/harnessing-organizational-knowledge-for.html
http://ai.googleblog.com/2019/03/harnessing-organizational-knowledge-for.html
Googleblog
Harnessing Organizational Knowledge for Machine Learning
Computer_Vision:_Models,_Learning
26.3 MB
Book Computer Vision: Models, Learning, and Inference
Checklist for debugging neural networks
https://towardsdatascience.com/checklist-for-debugging-neural-networks-d8b2a9434f21
https://towardsdatascience.com/checklist-for-debugging-neural-networks-d8b2a9434f21
Medium
Checklist for debugging neural networks
Tangible steps you can take to identify and fix issues with training, generalization, and optimization for machine learning models
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 noscriptd
In 2017, he released a five-part course on deep learning also on Coursera noscriptd
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/
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/