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chapter02_mathematical-building-blocks.ipynb

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"matches = predicted_labels == test_labels\n",
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"f\"accuracy: {ops.mean(matches):.2f}\""
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"colab_type": "text"
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},
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"source": [
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"### Summary"
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]
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}
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],
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"metadata": {

chapter03_introduction-to-ml-frameworks.ipynb

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"predictions = model.predict(val_inputs, batch_size=128)\n",
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"print(predictions[:10])"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"colab_type": "text"
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},
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"source": [
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"### Summary"
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]
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}
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],
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"metadata": {

chapter04_classification-and-regression.ipynb

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"source": [
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"#### Wrapping up"
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]
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{
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"cell_type": "markdown",
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"metadata": {
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"colab_type": "text"
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},
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"source": [
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"### Summary"
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}
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],
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"metadata": {

chapter05_fundamentals-of-ml.ipynb

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"plt.legend()\n",
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"plt.show()"
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]
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{
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"cell_type": "markdown",
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"metadata": {
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"colab_type": "text"
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},
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"source": [
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"### Summary"
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}
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],
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chapter07_deep-dive-keras.ipynb

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" )\n",
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" return logs, state"
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]
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{
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"cell_type": "markdown",
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"metadata": {
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"colab_type": "text"
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},
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"source": [
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"### Summary"
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]
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}
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],
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"metadata": {

chapter08_image-classification.ipynb

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"test_loss, test_acc = model.evaluate(test_dataset)\n",
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"print(f\"Test accuracy: {test_acc:.3f}\")"
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]
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{
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"cell_type": "markdown",
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"metadata": {
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"colab_type": "text"
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},
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"source": [
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"### Summary"
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]
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}
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],
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"metadata": {

chapter09_convnet-architecture-patterns.ipynb

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"source": [
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"### Beyond convolution: Vision Transformers"
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{
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"cell_type": "markdown",
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"metadata": {
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"colab_type": "text"
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},
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"source": [
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"### Summary"
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]
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}
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],
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"metadata": {

chapter10_interpreting-what-convnets-learn.ipynb

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"source": [
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"### Visualizing the latent space of a convnet"
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{
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"cell_type": "markdown",
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"metadata": {
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"colab_type": "text"
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"source": [
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"### Summary"
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}
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],
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chapter11_image-segmentation.ipynb

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"show_box(input_box, plt.gca())\n",
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"plt.show()"
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]
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"cell_type": "markdown",
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"metadata": {
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"colab_type": "text"
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"source": [
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"### Summary"
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}
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chapter12_object-detection.ipynb

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" draw_box(ax, box, label_name, label_to_color(label))\n",
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"plt.show()"
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"cell_type": "markdown",
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"metadata": {
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"colab_type": "text"
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"source": [
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"### Summary"
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]
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}
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],
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"metadata": {

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