Difference between revisions of "Image Classification"
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− | |keywords=artificial, intelligence, machine, learning, models, algorithms, data, singularity, moonshot, TensorFlow, Google, Nvidia, Microsoft, Azure, Amazon, AWS, Facebook | + | |keywords=artificial, intelligence, machine, learning, models, algorithms, data, singularity, moonshot, TensorFlow, Google, Nvidia, Microsoft, Azure, Amazon, AWS, Meta, Facebook |
|description=Helpful resources for your journey with artificial intelligence; videos, articles, techniques, courses, profiles, and tools | |description=Helpful resources for your journey with artificial intelligence; videos, articles, techniques, courses, profiles, and tools | ||
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<youtube>eAVWos2-qp0</youtube> | <youtube>eAVWos2-qp0</youtube> | ||
<b>Image Classification using [[PyTorch]] in 2020 | <b>Image Classification using [[PyTorch]] in 2020 | ||
− | </b><br>A virtual hands-on interactive [[PyTorch]] workshop, organized by People In Data together with [[Facebook]] DevC Stockholm. You will be guided through the code and implement your own [[(Deep) Convolutional Neural Network (DCNN/CNN)|CNN model]]! The only thing you need to bring is your [[Google]] account (as we will be using [[Google]] [[Colaboratory|Colab]]) and your curiosity. This webinar will be led by Pranjal Chaubey who is an AI mentor at Udacity and has done workshops for Developer Circles Hyderabad. He is a keen learner of anything data and always eager to share his knowledge. | + | </b><br>A virtual hands-on interactive [[PyTorch]] workshop, organized by People In Data together with [[Meta|Facebook]] DevC Stockholm. You will be guided through the code and implement your own [[(Deep) Convolutional Neural Network (DCNN/CNN)|CNN model]]! The only thing you need to bring is your [[Google]] account (as we will be using [[Google]] [[Colaboratory|Colab]]) and your curiosity. This webinar will be led by Pranjal Chaubey who is an AI mentor at Udacity and has done workshops for Developer Circles Hyderabad. He is a keen learner of anything data and always eager to share his knowledge. |
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Revision as of 23:40, 8 February 2023
Youtube search... ...Google search
- ...predict categories
- Capabilities
- Case Studies
- Image Retrieval / Object Detection
- (Deep) Convolutional Neural Network (DCNN/CNN)
- Image/Video Transfer Learning
- Image-to-Image Translation
- ResNet-50
- The MNIST Database | Y. LeCun, C. Cortes, and C. Burges
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Microsoft Lobe
- Lobe aims to make it easy for anyone to train machine learning models. Free, private desktop application that has everything you need to take your machine learning ideas from prototype to production. This version of Lobe learns to look at images using image classification - categorizing an image into a single label overall. We are working to expand to more types of problems and data in future versions.
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