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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Matin Qaim,Carl E. Pray,David Zilbermanreatly facilitated people‘s daily lives. Through programming, humans can hand over the interaction logic designed in advance to the machine to execute repeatedly and quickly, thereby freeing humans from simple and tedious repetitive labor. However, for tasks that require a high level of intelligence

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Juan Ferré,Jeroen Van Rie,Susan C. Macintosh neurons are interconnected to form a huge neural network, thus forming the human brain, the basis of perception and consciousness. Figure 2-1 is a typical biological neuron structure. In 1943, the psychologist Warren McCulloch and mathematical logician Walter Pitts proposed a mathematical model of

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https://doi.org/10.1007/978-3-319-55581-2al application of the classification problem is to teach computers how to automatically recognize objects in images. Let’s consider one of the simplest tasks in image classification: 0–9 digital picture recognition, which is relatively simple and also has a very wide range of applications, such as p

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Franklin M. Din,Valerie J. H. Powell designed as a highly modular and extensible high-level neural network interface, so that users can quickly complete model building and training without excessive professional knowledge. The Keras library is divided into a frontend and a backend. The backend generally calls the existing deep learnin

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Stephen Foreman,Joseph Kilsdonk,Kelly Boggs 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.)

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Neel Shimpi,Ram Pathak,Amit Acharyave and in-depth understanding of neural networks. But for deep learning, we still have a little doubt. The depth of deep learning refers to the deeper layers of the network, generally more than five layers, and most of the neural network layers introduced so far are implemented within five layers. S

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