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Deep Learning/Fundamentals2

🌸Model Analysis - Convolution / number of Pooling params + shape size ① case 1# Define the modelmodel = tf.keras.models.Sequential([ # Add convolutions and max pooling tf.keras.layers.Conv2D(32, (3,3), activation='relu', input_shape=(28, 28, 1)), tf.keras.layers.MaxPooling2D(2, 2), tf.keras.layers.Conv2D(32, (3,3), activation='relu'), tf.keras.layers.MaxPooling2D(2,2), # Add the same layers as before .. Deep Learning/Fundamentals 2024. 5. 12.
C1W2 - Logistic Regression as a Neural Network 1. Binary Classification ★ Logistic Regression is an algorithm for binary classification ex) an input of an image(represented in 3 RGB matrices: 3 x 64 x 64; if 64 x 64 pixels) → output 1 (cat) vs 0 (non cat) ★ in binary classification, our goal is to learn a classifier(x → y), to predict whether this is a cat image or non-cat image(whether y is 1 or 0) ★ Notation: ① A single training example is.. Deep Learning/Fundamentals 2023. 9. 6.

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