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Titlebook: Beginning Deep Learning with TensorFlow; Work with Keras, MNI Liangqu Long,Xiangming Zeng Book 2022 Liangqu Long and Xiangming Zeng 2022 T

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31#
發(fā)表于 2025-3-26 22:41:13 | 只看該作者
32#
發(fā)表于 2025-3-27 01:20:46 | 只看該作者
Basic TensorFlow, algorithms are essentially a combination of basic operations such as multiplication and addition of tensors. Therefore, it is important to get familiar with the basic tensor operation in TensorFlow. Only by mastering these operations can we realize various complex and novel network models at will a
33#
發(fā)表于 2025-3-27 05:44:40 | 只看該作者
34#
發(fā)表于 2025-3-27 09:38:53 | 只看該作者
Backward Propagation Algorithm,g the perceptron model, multi-input and multi-output fully connected layers; and then expanding to multilayer neural networks. We also introduced the design of the output layer under different scenarios and the commonly used loss functions and their implementation.
35#
發(fā)表于 2025-3-27 16:28:14 | 只看該作者
36#
發(fā)表于 2025-3-27 20:54:24 | 只看該作者
Overfitting, We call this the generalization ability. Generally speaking, the training set and the test set are sampled from the same data distribution. The sampled samples are independent of each other, but come from the same distribution. We call this assumption the independent identical distribution (i.i.d.)
37#
發(fā)表于 2025-3-28 01:30:44 | 只看該作者
38#
發(fā)表于 2025-3-28 03:37:50 | 只看該作者
Recurrent Neural Network,is very suitable for pictures with spatial and local correlation. It has been successfully applied to a series of tasks in the field of computer vision. In addition to the spatial dimension, natural signals also have a temporal dimension. Signals with a time dimension are very common, such as the te
39#
發(fā)表于 2025-3-28 10:06:44 | 只看該作者
40#
發(fā)表于 2025-3-28 11:11:34 | 只看該作者
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