Deep Learning based object detector #YOLOv3 with OpenCV
With code in both #Python and #C++
https://www.learnopencv.com/deep-learning-based-object-detection-using-yolov3-with-opencv-python-c/
@ArtificialIntelligenceArticles
With code in both #Python and #C++
https://www.learnopencv.com/deep-learning-based-object-detection-using-yolov3-with-opencv-python-c/
@ArtificialIntelligenceArticles
LearnOpenCV – Learn OpenCV, PyTorch, Keras, Tensorflow with code, & tutorials
YOLOv3 – Deep Learning Based Object Detection – YOLOv3 with OpenCV ( Python / C++ )
In this post, we will understand what is Yolov3 and learn how to use YOLOv3 — a state-of-the-art object detector — with OpenCV. YOLOv3 is the latest variant of a popular object detection algorithm YOLO – You Only Look Once. The published model recognizes…
A very good article for Reinforcement Learning https://goo.gl/twLP7Y @ArtificialIntelligenceArticles
The Matrix Calculus You Need For Deep Learning
https://explained.ai/matrix-calculus/index.html
@ArtificialIntelligenceArticles
https://explained.ai/matrix-calculus/index.html
@ArtificialIntelligenceArticles
What You Need to Know Before Considering a PhD
If you're considering a PhD, go read this excellent post by Rachel Thomas /
https://goo.gl/eqRRDy @ArtificialIntelligenceArticles
If you're considering a PhD, go read this excellent post by Rachel Thomas /
https://goo.gl/eqRRDy @ArtificialIntelligenceArticles
Evaluating Theory of Mind in Question Answering
https://arxiv.org/abs/1808.09352 @ArtificialIntelligenceArticles
https://arxiv.org/abs/1808.09352 @ArtificialIntelligenceArticles
In this Viewpoint, Geoffrey Hinton of Google’s Brain Team discusses the basics of neural networks. Learn more https://goo.gl/mmAiX8 @ArtificialIntelligenceArticles
Deep learning for predicting aftershocks of large earthquakes.
Besides offering better predictions, interpretations of the model suggest promising directions for new physical theories
https://www.nature.com/articles/s41586-018-0438-y
The success of artificial intelligence in this domain is thanks to one of the technology’s core strengths: its ability to uncover previously overlooked patterns in complex datasets. This is especially relevant in seismology, where it can be incredibly difficult to see connections in the data. Seismic events involve too many variables, from the makeup of the ground in different areas to the types of interactions between seismic plates to the ways energy propagates in waves through the Earth. Making sense of it all is incredibly hard.
The researchers say their deep learning model was able to make its predictions by considering a factor known as the “von Mises yield criterion,” a complex calculation used to predict when materials will begin to break under stress.
https://t.iss.one/ArtificialIntelligenceArticles
Besides offering better predictions, interpretations of the model suggest promising directions for new physical theories
https://www.nature.com/articles/s41586-018-0438-y
The success of artificial intelligence in this domain is thanks to one of the technology’s core strengths: its ability to uncover previously overlooked patterns in complex datasets. This is especially relevant in seismology, where it can be incredibly difficult to see connections in the data. Seismic events involve too many variables, from the makeup of the ground in different areas to the types of interactions between seismic plates to the ways energy propagates in waves through the Earth. Making sense of it all is incredibly hard.
The researchers say their deep learning model was able to make its predictions by considering a factor known as the “von Mises yield criterion,” a complex calculation used to predict when materials will begin to break under stress.
https://t.iss.one/ArtificialIntelligenceArticles
Nature
Deep learning of aftershock patterns following large earthquakes
Nature - Neural networks trained on data from about 130,000 aftershocks from around 100 large earthquakes improve predictions of the spatial distribution of aftershocks and suggest physical...
The p5.js Web Editor is a friendly online platform for learning to code in a visual way. Designed for all ages and abilities, anyone can get started quickly creating, editing, https://goo.gl/3TLZrU
Why Technology Favors Tyranny
"Artificial intelligence could erase many practical advantages of democracy, and erode the ideals of liberty and equality. It will further concentrate power among a small elite if we don’t take steps to stop it."
By Yuval Noah Harari :
https://www.theatlantic.com/magazine/archive/2018/10/yuval-noah-harari-technology-tyranny/568330/
https://t.iss.one/ArtificialIntelligenceArticles
"Artificial intelligence could erase many practical advantages of democracy, and erode the ideals of liberty and equality. It will further concentrate power among a small elite if we don’t take steps to stop it."
By Yuval Noah Harari :
https://www.theatlantic.com/magazine/archive/2018/10/yuval-noah-harari-technology-tyranny/568330/
https://t.iss.one/ArtificialIntelligenceArticles
The Atlantic
Why Technology Favors Tyranny
Artificial intelligence could erase many practical advantages of democracy, and erode the ideals of liberty and equality. It will further concentrate the power among a small elite if we don’t take steps to stop it.
Kaggle winner explains how to combine categorical, numerical, image and text features into a single NN that gets you into top 10 without stacking.
Online ad demand prediction kaggle competition 1st place summary:
https://www.kaggle.com/c/avito-demand-prediction/discussion/59880
@ArtificialIntelligenceArticles
Online ad demand prediction kaggle competition 1st place summary:
https://www.kaggle.com/c/avito-demand-prediction/discussion/59880
@ArtificialIntelligenceArticles
250 awesome short lectures on robotics
The Queensland University of Technology robot academy : https://robotacademy.net.au/ @ArtificialIntelligenceArticles
The Queensland University of Technology robot academy : https://robotacademy.net.au/ @ArtificialIntelligenceArticles
The First World-Class Overview of AI for the General Public
Curated Open-Source Codes, Implementations and Science : https://goo.gl/AZ3DJy @ArtificialIntelligenceArticles
Curated Open-Source Codes, Implementations and Science : https://goo.gl/AZ3DJy @ArtificialIntelligenceArticles
What makes TPUs fine-tuned for deep learning?
https://cloud.google.com/blog/products/ai-machine-learning/what-makes-tpus-fine-tuned-for-deep-learning @ArtificialIntelligenceArticles
https://cloud.google.com/blog/products/ai-machine-learning/what-makes-tpus-fine-tuned-for-deep-learning @ArtificialIntelligenceArticles
Princeton Team using Deep Learning to develop Fusion Energy
https://goo.gl/KGefMB @ArtificialIntelligenceArticles
https://goo.gl/KGefMB @ArtificialIntelligenceArticles
Page Proportions as Musical Intervals
New Codepen by Tero Parviainen : https://codepen.io/teropa/full/xaqzLj/
New Codepen by Tero Parviainen : https://codepen.io/teropa/full/xaqzLj/
Graph Attention Networks
"We present graph attention networks (GATs), novel neural network architectures that operate on graph-structured data, leveraging masked self-attentional layers to address the shortcomings of prior methods based on graph convolutions or their approximations. By stacking layers in which nodes are able to attend over their neighborhoods' features, we enable (implicitly) specifying different weights to different nodes in a neighborhood, without requiring any kind of costly matrix operation (such as inversion) or depending on knowing the graph structure upfront. (...)"
Paper by Petar Veličković, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Liò, Yoshua Bengio : https://arxiv.org/abs/1710.10903
Source Code : https://github.com/PetarV-/GAT
Website : https://mila.quebec/en/publication/graph-attention-networks/
"We present graph attention networks (GATs), novel neural network architectures that operate on graph-structured data, leveraging masked self-attentional layers to address the shortcomings of prior methods based on graph convolutions or their approximations. By stacking layers in which nodes are able to attend over their neighborhoods' features, we enable (implicitly) specifying different weights to different nodes in a neighborhood, without requiring any kind of costly matrix operation (such as inversion) or depending on knowing the graph structure upfront. (...)"
Paper by Petar Veličković, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Liò, Yoshua Bengio : https://arxiv.org/abs/1710.10903
Source Code : https://github.com/PetarV-/GAT
Website : https://mila.quebec/en/publication/graph-attention-networks/
GitHub
GitHub - PetarV-/GAT: Graph Attention Networks (https://arxiv.org/abs/1710.10903)
Graph Attention Networks (https://arxiv.org/abs/1710.10903) - PetarV-/GAT
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Sketch2Code : Turn your whiteboard sketches to working code in seconds
https://azure.microsoft.com/en-us/blog/turn-your-whiteboard-sketches-to-working-code-in-seconds-with-sk
https://azure.microsoft.com/en-us/blog/turn-your-whiteboard-sketches-to-working-code-in-seconds-with-sk
The 50 Best Free Datasets for Machine Learning
https://gengo.ai/articles/the-50-best-free-datasets-for-machine-learning/ @ArtificialIntelligenceArticles
https://gengo.ai/articles/the-50-best-free-datasets-for-machine-learning/ @ArtificialIntelligenceArticles
Five books every data scientist should read that are not about data science
https://towardsdatascience.com/five-books-every-data-scientist-should-read-that-are-not-about-data-science-f7335fb1f84f
https://towardsdatascience.com/five-books-every-data-scientist-should-read-that-are-not-about-data-science-f7335fb1f84f
List of free resources to learn Natural Language Processing
https://blog.paralleldots.com/data-science/nlp/free-natural-language-processing-resources/
https://blog.paralleldots.com/data-science/nlp/free-natural-language-processing-resources/