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Titlebook: Deep Learning with Python; A Hands-on Introduct Nikhil Ketkar Book 2017 Nikhil Ketkar 2017 Deep Learning.Python.Keras.Theano.Caffe.Deep Lea

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樓主: dilate
21#
發(fā)表于 2025-3-25 05:04:53 | 只看該作者
Overview: Background and ApplicationsThroughout the book we have covered the theoretical aspects of models and introduced the reader to a number of frameworks for deep learning. In this chapter we will cover the process of training deep learning models.
22#
發(fā)表于 2025-3-25 10:37:08 | 只看該作者
Introduction to Deep Learning,This chapter provides a broad overview and an historical context on the subject of deep learning. It also gives the reader a roadmap for navigating the book, its prerequisites, and further reading to dive deeper into the subject matter.
23#
發(fā)表于 2025-3-25 14:01:07 | 只看該作者
24#
發(fā)表于 2025-3-25 19:13:56 | 只看該作者
25#
發(fā)表于 2025-3-25 20:14:47 | 只看該作者
26#
發(fā)表于 2025-3-26 01:17:59 | 只看該作者
Convolutional Neural Networks,Convolution Neural Networks (CNNs) in essence are neural networks that employ the convolution operation (instead of a fully connected layer) as one of its layers.
27#
發(fā)表于 2025-3-26 06:00:04 | 只看該作者
28#
發(fā)表于 2025-3-26 10:13:57 | 只看該作者
29#
發(fā)表于 2025-3-26 14:32:41 | 只看該作者
30#
發(fā)表于 2025-3-26 19:53:23 | 只看該作者
Automatic Differentiation,In the chapter on Stochastic Gradient Descent, we treated the computation of gradients of the loss function as a black box.
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