Hands-On Neural Networks with TensorFlow 2.0: Understand TensorFlow, from static graph to eager execution, and design neural networks

Provides an in-depth guide to neural networks and machine learning using TensorFlow 2.0. It begins by explaining fundamental concepts like datasets, supervised and unsupervised learning, and model performance metrics such as accuracy, precision, and recall. The text then explores neural network architectures, including fully connected layers and convolutional neural networks (CNNs), along with optimization techniques like gradient descent and regularization methods like dropout. Finally, it covers practical applications such as image classification, object detection, semantic segmentation, and generative adversarial networks (GANs), offering insights into deploying trained models.

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