TensorFlow for Computer vision Tasks
TensorFlow for computer vision tasks enables the development of powerful machine learning models for image analysis and understanding. It offers a comprehensive suite of tools and libraries tailored for tasks such as image classification, object detection, segmentation, and more. With TensorFlow, developers can leverage pre-trained models, build custom architectures, and fine-tune models for specific tasks. Its flexibility, scalability, and extensive community support make it a popular choice for computer vision applications in various domains.
- Image preprocessing
- tf.keras.layers.Resizing
- tf.keras.layers.Rescaling
- tf.keras.layers.CenterCrop
- Image data augmentation with TensorFLow
- tf.keras.preprocessing.image.ImageDataGenerator
- tf.keras.layers.RandomCrop
- tf.keras.layers.RandomFlip
- tf.keras.layers.RandomTranslation
- tf.keras.layers.RandomRotation
- tf.keras.layers.RandomZoom
- tf.keras.layers.RandomContrast
- tf.keras.preprocessing.image.random_brightness
- Convolutional Neural Network (CNN)
- Convolutions layers
- tf.keras.layers.Conv1D
- tf.keras.layers.Conv2D
- tf.keras.layers.Conv3D()
- tf.keras.layers.DepthwiseConv1D
- tf.keras.layers.DepthwiseConv2D
- Deconvolutions Layers
- tf.keras.layers.Conv1DTranspose
- tf.keras.layers.Conv2DTranspose
- tf.keras.layers.Conv3DTranspose
- Convolutional LSTM layers in Tensorflow
- tf.keras.layers.SeparableConv1D
- tf.keras.layers.SeparableConv2D
- tf.keras.layers.SeparableConv3D
- Pooling Layers in Tensorflow
- tf.keras.layers.MaxPooling1D
- tf.keras.layers.MaxPooling2D
- tf.keras.layers.MaxPooling3D
- tf.keras.layers.AveragePooling1D
- tf.keras.layers.AveragePooling2D
- tf.keras.layers.AveragePooling2D
- tf.keras.layers.GlobalAveragePooling3D
- tf.keras.layers.GlobalMaxPooling3D
- Convolutions layers
- Computer vision Tasks:
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- Image segmentation using TensorFlow
- Deep Convolutional GAN for Image generations in TensorFlow
- Human Pose Detection using MoveNet with Tensorflowhub
TensorFlow Tutorial
TensorFlow has evolved as a popular deep learning framework, allowing developers and academics to quickly design and deploy machine learning models. In this complete TensorFlow Tutorial, we’ll explore TensorFlow with python from its fundamentals to advanced approaches, equipping with the knowledge and skills to harness the full potential of this powerful framework.
Table of Content
- TensorFlow for Neural Network
- 1. TensorFlow Introduction
- 2. TensorFlow installations
- 3. Tensorflow Basics
- 4. Preprocessing, Model Construction, and Training in TensorFlow
- 5. TensorBoard
- 6. Multi-GPU and Distributed training using Tensorflow
- 7. TensorFlow for Computer vision Tasks
- 8. TensorFlow for NLP Tasks
- 9. Cutting-Edge Applications and Future Trends in TensorFlow
- Conclusion
- Frequently Asked Questions on TensorFlow for Neural Network