Applied Deep Learning: A Case-Based Approach to Understanding Deep Neural Networks

Guides readers through fundamental concepts of deep learning, including computational graphs, single neurons, and feedforward neural networks, often using TensorFlow for practical implementation. The author emphasizes understanding the mathematical underpinnings of algorithms, discusses optimization techniques like gradient descent and Adam, and addresses critical aspects such as regularization to combat overfitting, metric analysis for model evaluation, and hyperparameter tuning. Chapters further explore advanced network architectures like convolutional and recurrent neural networks, and demonstrate their application in real-world scenarios.

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