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_notebooks/03_classification.ipynb

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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"**Chapter 3 – Classification**\n",
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"# Classification\n",
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"> Chapter 3\n",
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"\n",
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"- permalink: /03_classification\n",
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"\n",
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"_This notebook contains all the sample code and solutions to the exercises in chapter 3._"
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]

_notebooks/09_unsupervised_learning.ipynb

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"# Unsupervised Learning\n",
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"> Chapter 9\n",
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"\n",
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"- permalink: /09_unsupervised_learning\n",
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"\n",
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"_This notebook contains all the sample code in chapter 9._"
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]
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},

_notebooks/13_loading_and_preprocessing_data.ipynb

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"# Loading and Preprocessing Data with TensorFlow\n",
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"> Chapter 13\n",
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"\n",
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"permalink: /13_loading_and_preprocessing_data\n",
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"- permalink: /13_loading_and_preprocessing_data\n",
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"\n",
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"_This notebook contains all the sample code and solutions to the exercises in chapter 13._"
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_notebooks/14_deep_computer_vision_with_cnns.ipynb

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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Title: Deep Computer Vision Using Convolutional Neural Networks\n",
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"# Deep Computer Vision Using Convolutional Neural Networks\n",
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"> Chapter 14\n",
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"\n",
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"- permalink: /14_deep_computer_vision_with_cnns"

index.html

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<li><a href="14_deep_computer_vision_with_cnns">Deep Computer Vision Using Convolutional Neural Networks</a></li>
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<li><a href="15_processing_sequences_using_rnns_and_cnns">Processing Sequences Using RNNs and CNNs</a></li>
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<li><a href="16_nlp_with_rnns_and_attention">Natural Language Processing with RNNs and Attention</a></li>
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<li><a href="17_autoencoders">Representation Learning Using Autoencoders</a></li>
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<li><a href="17_autoencoders_and_gans">Representation Learning Using Autoencoders</a></li>
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<li><a href="18_reinforcement_learning">Reinforcement Learning</a></li>
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<li><a href="19_training_and_deploying_at_scale">Training and Deploying TensorFlow Models at Scale</a></li>
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</ol>

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