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implementation using Python and Machine Learning along with Convolutional Neural Network 🛑.\n\n![Screenshot](Freebirds_Crew.png)\n\nLink - https://www.youtube.com/watch?v=2H0tSHGK_CU\n\nExplained all about Quick Draw -\n\n• Architecture\n\n• Data Pre-Processing and Data Manipulation\n\n• Activation Functions ( RELU and Sigmoid )\n\n• Convolutional Neural Network\n\n• Max Pooling\n\n• Fully Connected Layers\n\n• Flaterning\n\nNote - If Computational power is not enough to train the model on 10-12 Doodled images or .npy files.\nDownload .npy Files - https://bit.ly/2P7TDut\n\nQuickdraw API for accessing the Quick Draw data that downloads the data files as and when needed, caches them locally and interprets them so they can be used.\nAPI - https://bit.ly/2DkRCbz\n\nWatch the Full Vidoe and Like Share and Subscribe our YouTube Channel.\n\nYouTube Channel - https://www.youtube.com/channel/UC4RZP6hNT5gMlWCm0NDzUWg?view_as=subscriber?sub_confirmation=1\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Ffreebirdscrew%2Fgoogle_quickdraw_implementation","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Ffreebirdscrew%2Fgoogle_quickdraw_implementation","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Ffreebirdscrew%2Fgoogle_quickdraw_implementation/lists"}