TensorFlow 2. 0 Computer Vision Cookbook: Implement Machine Learning Solutions to Overcome Various Computer Vision Challenges.

Martinez, Jesus.

Details

Title	TensorFlow 2. 0 Computer Vision Cookbook: Implement Machine Learning Solutions to Overcome Various Computer Vision Challenges.
Creators	Martinez Jesus.
Imprint	Birmingham: Packt Publishing, Limited, 2021
Collection	Электронные книги зарубежных издательств ; Общая коллекция
Subjects	COMPUTERS — Computer Vision & Pattern Recognition. ; COMPUTERS — Image Processing. ; COMPUTERS — Machine Theory. ; Computer vision. ; Machine learning. ; Image processing — Digital techniques. ; EBSCO eBooks
Document type	Other
File type	PDF
Language	English
Rights	Доступ по паролю из сети Интернет (чтение, печать, копирование)
Record key	on1235592707
Record create date	1/30/2021

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Cover
Copyright
About PACKT
Contributors
Table of Contents
Preface
Chapter 1: Getting Started with TensorFlow 2.x for Computer Vision
- Technical requirements
- Working with the basic building blocks of the Keras API
  - Getting ready
  - How to do it…
  - How it works…
  - See also
- Loading images using the Keras API
  - Getting ready
  - How to do it…
  - How it works…
  - See also
- Loading images using the tf.data.Dataset API
  - How to do it…
  - How it works…
  - See also
- Saving and loading a model
  - How to do it…
  - How it works…
  - There's more…
- Visualizing a model's architecture
  - Getting ready
  - How to do it…
  - How it works…
- Creating a basic image classifier
  - Getting ready
  - How to do it…
  - How it works…
  - See also
Chapter 2: Performing Image Classification
- Technical requirements
- Creating a binary classifier to detect smiles
  - Getting ready
  - How to do it…
  - How it works…
  - See also
- Creating a multi-class classifier to play rock paper scissors
  - Getting ready
  - How to do it…
  - How it works…
- Creating a multi-label classifier to label watches
  - Getting ready
  - How to do it…
  - How it works…
  - See also
- Implementing ResNet from scratch
  - Getting ready
  - How to do it…
  - How it works…
  - See also
- Classifying images with a pre-trained network using the Keras API
  - Getting ready
  - How to do it…
  - How it works…
  - See also
- Classifying images with a pre-trained network using TensorFlow Hub
  - Getting ready
  - How to do it…
  - How it works…
  - See also
- Using data augmentation to improve performance with the Keras API
  - Getting ready
  - How to do it…
  - How it works…
  - See also
- Using data augmentation to improve performance with the tf.data and tf.image APIs
  - Getting ready
  - How to do it…
  - How it works…
  - See also
Chapter 3: Harnessing the Power of Pre-Trained Networks with Transfer Learning
- Technical requirements
- Implementing a feature extractor using a pre-trained network
  - Getting ready
  - How to do it…
  - How it works…
  - See also
- Training a simple classifier on extracted features
  - Getting ready
  - How to do it…
  - How it works…
  - See also
- Spot-checking extractors and classifiers
  - Getting ready
  - How to do it…
  - How it works…
- Using incremental learning to train a classifier
  - Getting ready
  - How to do it…
  - How it works…
- Fine-tuning a network using the Keras API
  - Getting ready
  - How to do it…
  - How it works…
  - See also
- Fine-tuning a network using TFHub
  - Getting ready
  - How to do it…
  - How it works…
  - See also
Chapter 4: Enhancing and Styling Images with DeepDream, Neural Style Transfer, and Image Super-Resolution
- Technical requirements
- Implementing DeepDream
  - Getting ready
  - How to do it…
  - How it works…
  - See also
- Generating your own dreamy images
  - Getting ready
  - How to do it…
  - How it works…
- Implementing Neural Style Transfer
  - Getting ready
  - How to do it…
  - How it works…
  - See also
- Applying style transfer to custom images
  - Getting ready
  - How to do it…
  - How it works…
  - See also
- Applying style transfer with TFHub
  - Getting ready
  - How to do it…
  - How it works…
  - See also
- Improving image resolution with deep learning
  - Getting ready
  - How to do it…
  - How it works…
  - See also
Chapter 5: Reducing Noise with Autoencoders
- Technical requirements
- Creating a simple fully connected autoencoder
  - Getting ready
  - How to do it…
  - How it works…
  - See also
- Creating a convolutional autoencoder
  - Getting ready
  - How to do it…
  - How it works…
  - See also
- Denoising images with autoencoders
  - Getting ready
  - How to do it…
  - How it works…
- Spotting outliers using autoencoders
  - Getting ready
  - How to do it…
  - How it works…
- Creating an inverse image search index with deep learning
  - Getting ready
  - How to do it…
  - How it works…
  - See also
- Implementing a variational autoencoder
  - Getting ready
  - How to do it…
  - How it works…
  - See also
Chapter 6: Generative Models and Adversarial Attacks
- Technical requirements
- Implementing a deep convolutional GAN
  - Getting ready
  - How to do it…
  - How it works…
  - See also
- Using a DCGAN for semi-supervised learning
  - Getting ready
  - How to do it…
  - How it works…
  - See also
- Translating images with Pix2Pix
  - Getting ready
  - How to do it…
  - How it works…
  - See also
- Translating unpaired images with CycleGAN
  - Getting ready
  - How to do it…
  - How it works…
  - See also
- Implementing an adversarial attack using the Fast Gradient Signed Method
  - Getting ready
  - How to do it
  - How it works…
  - See also
Chapter 7: Captioning Images with CNNs and RNNs
- Technical requirements
- Implementing a reusable image caption feature extractor
  - Getting ready
  - How to do it…
  - How it works…
  - See also
- Implementing an image captioning network
  - Getting ready
  - How to do it…
  - How it works…
  - See also
- Generating captions for your own photos
  - Getting ready
  - How to do it…
  - How it works…
- Implementing an image captioning network on COCO with attention
  - Getting ready
  - How to do it…
Chapter 8: Fine-Grained Understanding of Images through Segmentation
- Technical requirements
- Creating a fully convolutional network for image segmentation
  - Getting ready
  - How to do it…
  - How it works…
  - See also
- Implementing a U-Net from scratch
  - Getting ready
  - How to do it…
  - How it works…
  - See also
- Implementing a U-Net with transfer learning
  - Getting ready
  - How to do it…
  - How it works…
  - See also
- Segmenting images using Mask-RCNN and TensorFlow Hub
  - Getting ready
  - How to do it…
  - How it works…
  - See also
Chapter 9: Localizing Elements in Images with Object Detection
- Technical requirements
- Creating an object detector with image pyramids and sliding windows
  - Getting ready
  - How to do it…
  - How it works…
  - See also
- Detecting objects with YOLOv3
  - Getting ready
  - How it works…
  - See also
- Training your own object detector with TensorFlow's Object Detection API
  - Getting ready
  - How to do it…
  - How it works…
  - See also
- Detecting objects using TFHub
  - Getting ready
  - How to do it…
  - How it works…
  - See also
Chapter 10: Applying the Power of Deep Learning to Videos
- Technical requirements
- Detecting emotions in real time
  - Getting ready
  - How to do it…
  - How it works…
  - See also
- Recognizing actions with TensorFlow Hub
  - Getting ready
  - How to do it…
  - How it works…
  - See also
- Generating the middle frames of a video with TensorFlow Hub
  - Getting ready
  - How to do it…
  - How it works…
  - See also
- Performing text-to-video retrieval with TensorFlow Hub
  - Getting ready
  - How to do it…
  - How it works…
  - See also
Chapter 11: Streamlining Network Implementation with AutoML
- Technical requirements
- Creating a simple image classifier with AutoKeras
  - How to do it…
  - How it works…
  - See also
- Creating a simple image regressor with AutoKeras
  - Getting ready
  - How to do it…
  - How it works…
  - See also
- Exporting and importing a model in AutoKeras
  - How to do it…
  - How it works…
  - See also
- Controlling architecture generation with AutoKeras' AutoModel
  - How to do it…
  - How it works…
  - See also
- Predicting age and gender with AutoKeras
  - Getting ready
  - How to do it…
  - How it works…
  - See also
Chapter 12: Boosting Performance
- Technical requirements
- Using convolutional neural network ensembles to improve accuracy
  - Getting ready
  - How to do it…
  - How it works…
  - See also
- Using test time augmentation to improve accuracy
  - Getting ready
  - How to do it…
  - How it works…
- Using rank-N accuracy to evaluate performance
  - Getting ready
  - How to do it…
  - How it works…
  - See also
- Using label smoothing to increase performance
  - Getting ready
  - How to do it…
  - How it works…
- Checkpointing model
  - How to do it…
- Customizing the training process using tf.GradientTape
  - How to do it…
  - How it works…
  - Getting ready
  - How to do it…
  - How it works…
  - See also
Other Books You May Enjoy
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