{"version":"1.0","type":"rich","title":"Gist 50acac748fdb4324a0dc2759fb6fce55","description":"This notebook builds and trains a convolutional neural network for binary image classification using Keras with TensorFlow. It defines a Sequential model with Conv2D, LeakyReLU, MaxPooling2D, Dropout, Flatten, and Dense layers, then compiles it with categorical crossentropy and the Adam optimizer. The model trains on pre-loaded NumPy arrays from seed-0-train.npz, with validation on seed-0-val.npz, and uses ModelCheckpoint to save the best weights based on validation accuracy.","provider_name":"VizHub AI Studio","provider_url":"https://next.vizhub.com","html":"<iframe src=\"https://next.vizhub.com/viz/gist-50acac748fdb4324a0dc2759fb6fce55?mode=embed\" width=\"960\" height=\"500\" scrolling=\"no\" frameborder=\"1\"></iframe>","width":960,"height":500,"cache_age":3600}