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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]] |
| − | * [[Jupyter Notebooks]] | + | *** [[Screening; Passenger, Luggage, & Cargo]] |
| | + | *** [[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> |
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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/discussion/45805 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://github.com/mmuneebs/screening 10th Place | MoeJoe (Shayan) ]
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| − | ** [http://github.com/mmuneebs/screening Muneeb Saleem]
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| − | While long lines and frantically shuffling luggage into plastic bins isn’t a fun experience, airport security is a critical and necessary requirement for safe travel.
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| − | No one understands the need for both thorough security screenings and short wait times more than U.S. Transportation Security Administration (TSA). They’re responsible for all U.S. airport security, screening more than two million passengers daily. As part of their Apex Screening at Speed Program, DHS has identified high false alarm rates as creating significant bottlenecks at the airport checkpoints. Whenever TSA’s sensors and algorithms predict a potential threat, TSA staff needs to engage in a secondary, manual screening process that slows everything down. And as the number of travelers increase every year and new threats develop, their prediction algorithms need to continually improve to meet the increased demand. Currently, TSA purchases updated algorithms exclusively from the manufacturers of the scanning equipment used. These algorithms are proprietary, expensive, and often released in long cycles. In this competition, TSA is stepping outside their established procurement process and is challenging the broader data science community to help improve the accuracy of their threat prediction algorithms. Using a dataset of images collected on the latest generation of scanners, participants are challenged to identify the presence of simulated threats under a variety of object types, clothing types, and body types. Even a modest decrease in false alarms will help TSA significantly improve the passenger experience while maintaining high levels of security.
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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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