Preface Chapter 1： Getting Started with TensorFiow Current use Installing TensorFIow Ubuntu installation macOS installation Windows installation Virtual machine setup Testing the installation Summary Chapter 2： Your First Classifier The key parts Obtaining training data Downloading training data Understanding classes Automating the training data setup Additional setup Converting images to matrices Logical stopping points The machine learning briefcase Training day Saving the model for ongoing use Why hide the test set? Using the classifier Deep diving into the network Skills learned Summary Chapter 3： The TensorFIow Toolbox A quick preview Installing TensorBoard Incorporating hooks into our code Handwritten digits AlexNet Automating runs Summary Chapter 4： Cats and Dogs Revisiting notMNIST Program configurations Understanding convolutional networks Revisiting configurations Constructing the convolutional network Fulfilment Training day Actual cats and dogs Saving the model for ongoing use Using the classifier Skills learned Summary Chapter 5： Sequence to Sequence Models-Parlez-vous Fran~：ais? A quick preview Drinking from the firehose Training day Summary Chapter 6： Finding Meaning Additional setup Skills learned Summary Chapter 7： Making Money with Machine Learning Inputs and approaches Getting the data Approaching the problem Downloading and modifying data Viewing the data Extracting features Preparing for training and testing Building the network Training Testing Taking it further Practical considerations for the individual Skills learned Summary Chapter 8： The Doctor Will See You Now The challenge The data The pipeline Understanding the pipeline Preparing the dataset Explaining the data preparation Training routine Validation routine Visualize outputs with TensorBoard Inception network Going further Other medical data challenges The ISBI grand challenge Reading medical data Skills Learned Summary Chapter 9： Cruise Control - Automation An overview of the system Setting up the project Loading a pre-trained model to speed up the training Testing the pre-trained model Training the model for our dataset Introduction to the Oxford-lilT Pet dataset Dataset Statistics Downloading the dataset Preparing the data Setting up input pipelines for training and testing Defining the model Defining training operations Performing the training process Exporting the model for production Serving the model in production Setting up TensorFIow Serving Running and testing the model Designing the web sewer Testing the system Automatic fine-tune in production Loading the user-labeled data Performing a fine-tune on the model Setting up cronjob to run every day Summary Chapter 10： Go Live and Go Big Quick look at Amazon Web Services P2 instances G2 instances F1 instances Pricing Overview of the application Datasets Preparing the dataset and input pipeline Pre-processing the video for training Input pipeline with RandomShuffleQueue Neural network architecture Training routine with single GPU Training routine with multiple GPU Overview of Mechanical Turk Summary Chapter 11： Going Further - 21 Problems Dataset and challenges Problem 1 - ImageNet dataset Problem 2 - COCO dataset Problem 3 - Open Images dataset Problem 4 - YouTube-8M dataset Problem 5 - AudioSet dataset Problem 6 - LSUN challenge Problem 7 - MegaFace dataset Problem 8 - Data Science Bowl 2017 challenge Problem 9 - StarCraft Game dataset TensorFIow-based Projects Problem 10 - Human Pose Estimation Problem 11 - Object Detection - YOLO Problem 12 - Object Detection - Faster RCNN Problem 13 - Person Detection - tensorbox Problem 14 - Magenta Problem 15 - Wavenet Problem 16 - Deep Speech Interesting Projects Problem 17 - Interactive Deep Colorization -iDeepColor Problem 18 - Tiny face detector Problem 19 - People search Problem 20 - Face Recognition - MobilelD Problem 21 - Question answering - DrQA Gaffe to TensorFlow TensorFIow-Slim Summary Appendix： Advanced Installation Installation Installing Nvidia driver Installing the CUDA toolkit Installing cuDNN Installing TensorFIow Verifying TensorFIow with GPU support Using TensorFIow with Anaconda Summary Index
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