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| − | [http://www.youtube.com/results?search_query=Kaggle+Competition+artificial+intelligence+deep+learning Youtube search...] | + | {{#seo: |
| | + | |title=PRIMO.ai |
| | + | |titlemode=append |
| | + | |keywords=artificial, intelligence, machine, learning, models, algorithms, data, singularity, moonshot, Tensorflow, Google, Nvidia, Microsoft, Azure, Amazon, AWS |
| | + | |description=Helpful resources for your journey with artificial intelligence; videos, articles, techniques, courses, profiles, and tools |
| | + | }} |
| | + | [https://www.youtube.com/results?search_query=Kaggle+Competition+artificial+intelligence+deep+learning Youtube search...] |
| | + | [https://www.google.com/search?q=Kaggle+Competition+deep+machine+learning+ML ...Google search] |
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| − | * [[Kaggle Overview]] | + | * [[Kaggle]] |
| − | * [[Kaggle Kernels]] | + | ** [https://www.kaggle.com/competitions Competitions | Kaggle] |
| − | * [http://www.kaggle.com/competitions Competitions | Kaggle] | + | *** [[COVID-19]] |
| − | **[[Passenger Screening]] | + | *** [[Screening; Passenger, Luggage, & Cargo]] |
| − | * [[Jupyter Notebooks]] | + | *** [[Seismology]]: Earthquake Prediction |
| | + | *** [https://www.kaggle.com/mlg-ulb/creditcardfraud Credit Card Fraud Detection] |
| | + | **** [[Watch me Build a Cybersecurity Startup]] | [[Creatives#Siraj Raval|Siraj Raval]] |
| | + | **** [https://www.kaggle.com/sarathchandra/credit-card-fraud-detection-99-accuracy/comments#144915 2016 Credit Card Fraud Detection | Sarath Chandra] |
| | + | **** 2019 Fraud Competition: [https://www.kaggle.com/c/ieee-fraud-detection/overview/description IEEE-CIS Fraud Detection] |
| | + | *** [[The Abstraction and Reasoning Corpus]] |
| | + | * [[Competitions]] | General |
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| | + | <youtube>ez_eVGUjIP8</youtube> |
| | <youtube>jmHbS8z57yI</youtube> | | <youtube>jmHbS8z57yI</youtube> |
| | <youtube>7665INW4I5g</youtube> | | <youtube>7665INW4I5g</youtube> |
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| | <youtube>LgLcfZjNF44</youtube> | | <youtube>LgLcfZjNF44</youtube> |
| | <youtube>ClAZQI_B4t8</youtube> | | <youtube>ClAZQI_B4t8</youtube> |
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| − | == Passenger Screening Algorithm Challenge ==
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| − | [http://www.youtube.com/results?search_query=Passenger+Screening+Challenge+kaggle Youtube search...]
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| − | * [http://www.kaggle.com/c/passenger-screening-algorithm-challenge Passenger Screening Algorithm Challenge]
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| − | ** [http://www.kaggle.com/c/passenger-screening-algorithm-challenge/discussion/45805 1st Place | idle_speculation]
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| − | ** [http://suchir.io/posts/passenger-screening-algorithm-challenge-writeup.html 7th Place | Suchir Balaji]
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| − | ** [http://github.com/ShayanPersonal/Kaggle-Passenger-Screening-Challenge-Solution 10th Place | MoeJoe (Shayan)]
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| − | ** [http://www.kaggle.com/tensorflight/discussion 32nd Place | TensorFlight]
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| − | ** [http://github.com/mmuneebs/screening ? | Muneeb Saleem]
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| − | ** [http://github.com/trueb2/passenger-screening ? | trueb2]
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| − | About the Challenge: The Department of Homeland Security (DHS) gives notice of the availability of the “Department of Homeland Security’s Person Screening Algorithm Challenge Prize Competition and rules.” The DHS Science and Technology Directorate ( S&T) Homeland Security Advanced Research Projects Agency (HSARPA) Explosives Division (EXD) and the Transportation Security Administration (TSA) (Competition Sponsor) are seeking new automated detection algorithms from individuals and entities that improve the speed, accuracy, and detection of small threat objects and other prohibited items during the airport passenger screening process. Algorithms developed from this Competition, or through further research and development under a limited intellectual property use agreement, have the potential to improve the speed, the detection of prohibited items, and the accuracy of Advanced Imaging Technology (AIT) scanners. A comprehensive set of new automated detection algorithms have the potential to be integrated into the latest screening equipment.
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| − | ==== Models idle_speculation used to win ====
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| − | * [http://github.com/suhangpro/mvcnn Multi-view CNN (MVCNN) for shape recognition]
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| − | * [http://github.com/Microsoft/LightGBM LightGBM - gradient boosting (GBDT, GBRT, GBM or MART) framework based on decision tree algorithms, used for ranking, classification, etc.]
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| − | * [http://www.pyimagesearch.com/2017/03/20/imagenet-vggnet-resnet-inception-xception-keras/ ImageNet: VGGNet, ResNet, Inception, and Xception with Keras]
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| − | ** [http://en.wikipedia.org/wiki/ImageNet ImageNet | Wikipedia]
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| − | ** [http://www.tensorflow.org/api_docs/python/tf/keras/applications/ResNet50 ResNet50 | TensorFlow Keras]
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| − | ** [http://www.quora.com/What-is-the-VGG-neural-network What is the (Visual Geometry Group) VGG neural network?]
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| − | ** [http://www.cs.toronto.edu/~frossard/post/vgg16/ VGG is a convolutional neural network in TensorFlow]
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| − | ** [http://www.kaggle.com/ardiya/tensorflow-vgg-pretrained TensorFlow VGG Pretrained | Aditya Ardiya on Kaggle]
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| − | ** [http://www.kaggle.com/keras/vgg16 VGG-16 Pre-trained Model for Keras | Karen Simonyan, Andrew Zisserman]
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| − | ** [http://www.robots.ox.ac.uk/~vgg/ Visual Geometry Group]
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| − | <youtube>QQbOy6J2PI0</youtube>
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| − | <youtube>uf4-l6h7iGM</youtube>
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| − | <youtube>swoGdqGSn-c</youtube>
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| − | <youtube>5CWwwtEM2TA</youtube>
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| − | <youtube>jYvBmJo7qjc</youtube>
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| − | <youtube>b6LgMlTZtMM</youtube>
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| − | <youtube>m5RjXjvAAhQ</youtube>
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| − | <youtube>9w13O3gwUOQ</youtube>
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| − | <youtube>oDHpqu52soI</youtube>
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| − | <youtube>L7qjQu2ry2Q</youtube>
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| − | <youtube>Dz6vX2qYC4s</youtube>
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| − | <youtube>bXNbe_jsoMg</youtube>
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| − | <youtube>9zJlI5VzAvQ</youtube>
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