Детальная информация
| Название | TensorFlow 2. 0 Computer Vision Cookbook: Implement Machine Learning Solutions to Overcome Various Computer Vision Challenges. |
|---|---|
| Авторы | Martinez Jesus. |
| Выходные сведения | Birmingham: Packt Publishing, Limited, 2021 |
| Коллекция | Электронные книги зарубежных издательств ; Общая коллекция |
| Тематика | COMPUTERS — Computer Vision & Pattern Recognition. ; COMPUTERS — Image Processing. ; COMPUTERS — Machine Theory. ; Computer vision. ; Machine learning. ; Image processing — Digital techniques. ; EBSCO eBooks |
| Тип документа | Другой |
| Тип файла | |
| Язык | Английский |
| Права доступа | Доступ по паролю из сети Интернет (чтение, печать, копирование) |
| Ключ записи | on1235592707 |
| Дата создания записи | 30.01.2021 |
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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
- Index
