Titlebook: Applied Deep Learning with TensorFlow 2; Learn to Implement A Umberto Michelucci Book 2022Latest edition Umberto Michelucci 2022 Deep Learn

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書目名稱Applied Deep Learning with TensorFlow 2讀者反饋學(xué)科排名

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Hands-on with a Single Neuron,In this chapter, you learn what are the main components of the neuron. You also learn how to solve two classical statistical problems (i.e., linear regression and logistic regression) by using a neural network with just one neuron. To make things a bit more fun, you do that using real datasets. We d

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A Brief Introduction to Recurrent Neural Networks,rrent one. Networks with this architecture are called . or RNNs. This chapter is a superficial description of how RNNs work, with one small application that should help you better understand their inner workings. A full explanation of RNNs would require multiple books, so the goal of this chapter is

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Autoencoders, discuss what they are, what their limitations are, the typical use cases, and then look at some examples. We start with a general introduction to autoencoders, and we discuss the role of the activation function in the output layer and the loss function. We then discuss what the reconstruction error

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Generative Adversarial Networks (GANs), was invented by Goodfellow and colleagues in 2014. The two networks help each other with the final goal of being able to generate new data that looks like the data used for training. For example, you may want to train a network to generate human faces that are as realistic as possible. In this case

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Lecture Notes in Computer Sciencerly stopping. You learn how these methods help prevent the problem of overfitting and help you achieve much better results from your models when applied correctly. We look at the mathematics behind the methods and at how to implement them correctly in Python and Keras.