Planche B. Hands-On Computer Vision with TensorFlow 2..2019

  • CategoryOther
  • TypeE-Books
  • LanguageEnglish
  • Total size70.9 MB
  • Uploaded ByMRKILLER
  • Downloads60
  • Last checkedNov. 01st '19
  • Date uploadedOct. 31st '19
  • Seeders 7
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Textbook in PDF format plus Code

A practical guide to building high performance systems for object detection, segmentation, video processing, smartphone applications, and more.
Key Features
Discover how to build, train, and serve your own deep neural networks with TensorFlow 2 and Keras
Apply modern solutions to a wide range of applications such as object detection and video analysis
Learn how to run your models on mobile devices and webpages and improve their performance
Book Description
Computer vision solutions are becoming increasingly common, making their way in fields such as health, automobile, social media, and robotics. This book will help you explore TensorFlow 2, the brand new version of Google's open source framework for machine learning. You will understand how to benefit from using convolutional neural networks (CNNs) for visual tasks.
Hands-On Computer Vision with TensorFlow 2 starts with the fundamentals of computer vision and deep learning, teaching you how to build a neural network from scratch. You will discover the features that have made TensorFlow the most widely used AI library, along with its intuitive Keras interface, and move on to building, training, and deploying CNNs efficiently. Complete with concrete code examples, the book demonstrates how to classify images with modern solutions, such as Inception and ResNet, and extract specific content using You Only Look Once (YOLO), Mask R-CNN, and U-Net. You will also build Generative Adversarial Networks (GANs) and Variational Auto-Encoders (VAEs) to create and edit images, and LSTMs to analyze videos. In the process, you will acquire advanced insights into transfer learning, data augmentation, domain adaptation, and mobile and web deployment, among other key concepts.
By the end of the book, you will have both the theoretical understanding and practical skills to solve advanced computer vision problems with TensorFlow 2.0

Files:

Planche B. Hands-On Computer Vision with TensorFlow 2..2019 Code Chapter01
  • __init__.py (0.0 KB)
  • README.md (1.7 KB)
  • neuron.py (2.7 KB)
  • fully_connected_layer.py (5.4 KB)
    • mnist
    • __init__.py (7.3 KB)
  • simple_network.py (8.1 KB)
  • ch1_nb1_build_and_train_neural_network_from_scratch.ipynb (58.5 KB)
    • Chapter04
    • __init__.py (0.0 KB)
    • keras_custom_callbacks.py (2.8 KB)
    • README.md (3.4 KB)
    • classification_utils.py (4.0 KB)
    • cifar_utils.py (4.9 KB)
    • tiny_imagenet_utils.py (12.0 KB)
    • resnet_functional.py (12.4 KB)
    • resnet_objectoriented.py (14.9 KB)
      • res
      • sea_by_benjamin_planche.png (34.7 KB)
      • snow_bicycle_by_benjamin_planche.png (61.9 KB)
      • bridge_by_benjamin_planche.png (70.9 KB)
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      • castle_pic_by_benjamin_planche.png (100.8 KB)
      • bed_pic_by_benjamin_planche.png (104.5 KB)
      • mountain_pic_by_benjamin_planche.png (108.2 KB)
      • schoolbus_pic_by_benjamin_planche.png (123.2 KB)
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      • rabbit_pic_by_benjamin_planche.png (130.0 KB)
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      • rain_forest_pic_by_benjamin_planche.png (131.9 KB)
      • man_pic_by_benjamin_planche.png (135.0 KB)
      • forrest_pic_by_benjamin_planche.png (136.0 KB)
      • palm_tree_and_lamp_pic_by_benjamin_planche.png (136.3 KB)
    • ch4_nb5_explore_imagenet_and_its_tiny_version.ipynb (60.1 KB)
      • notebook_images
      • tf_hub_tensorboard_training.png (107.3 KB)
      • resnet_keras_app_transfer_learning_training.png (122.7 KB)
      • resnet_keras_app_tensorboard_training.png (124.7 KB)
    • ch4_nb2_reuse_models_from_keras_apps.ipynb (668.2 KB)
    • ch4_nb1_implement_resnet_from_scratch.ipynb (717.0 KB)
    • ch4_nb4_apply_transfer_learning.ipynb (744.1 KB)
    • ch4_nb3_fetch_models_from_tf_hub.ipynb (1.1 MB)
      • Chapter06
      • __init__.py (0.0 KB)
      • fcn.py (3.5 KB)
      • tf_math.py (4.0 KB)
      • plot_utils.py (4.1 KB)
      • README.md (4.2 KB)
      • mnist_utils.py (4.4 KB)
      • unet.py (7.0 KB)
      • keras_custom_callbacks.py (8.4 KB)
      • tf_losses_and_metrics.py (13.4 KB)
      • cityscapes_utils.py (14.4 KB)
        • notebook_images
        • tensorboard_result_grid.png (30.7 KB)
        • unet.png (177.6 KB)
        • fcn.png (190.7 KB)
      • ch6_nb2_denoise_with_autoencoders.ipynb (272.5 KB)
      • ch6_nb1_discover_autoencoders.ipynb (379.9 KB)
      • ch6_nb3_improve_image_quality_with_dae.ipynb (1.4 MB)
      • ch6_nb4_preparing_data_for_smart_car_apps.ipynb (1.4 MB)
      • ch6_nb6_build_and_train_a_unet_for_urban_object_and_instance_segmentation.ipynb (2.6 MB)
      • ch6_nb5_build_and_train_a_fcn8s_semantic_segmentation_model_for_smart_cars.ipynb (2.8 MB)
        • Chapter03
        • __init__.py (0.0 KB)
        • README.md (1.5 KB)
        • ch3_nb2_build_and_train_first_cnn_with_tf2.ipynb (30.7 KB)
        • ch3_nb3_experiment_with_optimizers.ipynb (90.8 KB)
        • ch3_nb4_apply_regularization_methods_to_cnns.ipynb (132.7 KB)
          • res
          • bird_pic_by_benjamin_planche.png (405.9 KB)
        • ch3_nb1_discover_cnns_basic_ops.ipynb (441.8 KB)
          • Chapter05
          • .gitignore (0.0 KB)
          • README.md (0.4 KB)
          • coco.names (0.6 KB)
          • convert.py (0.7 KB)
          • utils.py (7.1 KB)
          • ch5_nb1_yolo_inference.ipynb (8.4 KB)
          • models.py (9.0 KB)
          • dog_example.jpg (159.9 KB)
          Chapter09 coreml_ios
          • .gitignore (0.0 KB)
            • Core ML Demo Assets.xcassets
            • Contents.json (0.1 KB)
              • AppIcon.appiconset
              • Contents.json (1.6 KB)
              CoreMLHelpers
              • CoreMLHelpers.h (0.5 KB)
              • Info.plist (0.8 KB)
              • Math.swift (1.2 KB)
              • MLMultiArray+Image.swift (1.3 KB)
              • Predictions.swift (1.9 KB)
              • Array.swift (2.0 KB)
              • CVPixelBuffer+Helpers.swift (5.0 KB)
              • NonMaxSuppression.swift (6.7 KB)
              • UIImage+CVPixelBuffer.swift (7.0 KB)
              • MultiArray.swift (10.2 KB)
              • Info.plist (1.5 KB)
                • Base.lproj
                • LaunchScreen.storyboard (1.6 KB)
                • Main.storyboard (4.4 KB)
              • AppDelegate.swift (2.1 KB)
              • ViewController.swift (5.4 KB)
              • VisionObjectRecognitionViewController.swift (10.2 KB)
                • Core ML Demo.xcodeproj project.xcworkspace
                • contents.xcworkspacedata (0.2 KB)
                  • xcshareddata
                  • IDEWorkspaceChecks.plist (0.2 KB)
                  • project.pbxproj (25.6 KB)
                    • Core ML Demo.xcworkspace
                    • contents.xcworkspacedata (0.2 KB)
                      • xcshareddata
                      • IDEWorkspaceChecks.plist (0.2 KB)
                    • Podfile (0.3 KB)
                    • Podfile.lock (0.5 KB)
                      • tf_lite_android demo
                      • settings.gradle (0.0 KB)

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