Journey of Neural Network Optimization — Avoiding Pitfalls, Progressing Step by Step
This article explores common pitfalls and solutions in neural network optimization, including issues such as vanishing gradients, overfitting, and slow convergence. The article introduces optimization strategies like LSTM, GRU, regularization techniques, and neural architecture search, emphasizing the importance of practical experience. It uses the TensorFlow framework as an example to illustrate key points in tensor operations and network architecture design.
