標(biāo)題: Titlebook: Applied Deep Learning with TensorFlow 2; Learn to Implement A Umberto Michelucci Book 2022Latest edition Umberto Michelucci 2022 Deep Learn [打印本頁] 作者: Flippant 時間: 2025-3-21 17:54
書目名稱Applied Deep Learning with TensorFlow 2影響因子(影響力)
書目名稱Applied Deep Learning with TensorFlow 2影響因子(影響力)學(xué)科排名
書目名稱Applied Deep Learning with TensorFlow 2網(wǎng)絡(luò)公開度
書目名稱Applied Deep Learning with TensorFlow 2網(wǎng)絡(luò)公開度學(xué)科排名
書目名稱Applied Deep Learning with TensorFlow 2被引頻次
書目名稱Applied Deep Learning with TensorFlow 2被引頻次學(xué)科排名
書目名稱Applied Deep Learning with TensorFlow 2年度引用
書目名稱Applied Deep Learning with TensorFlow 2年度引用學(xué)科排名
書目名稱Applied Deep Learning with TensorFlow 2讀者反饋
書目名稱Applied Deep Learning with TensorFlow 2讀者反饋學(xué)科排名
作者: 圓錐體 時間: 2025-3-22 00:02
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作者: 忘川河 時間: 2025-3-22 03:22 作者: insightful 時間: 2025-3-22 06:34 作者: 場所 時間: 2025-3-22 09:42 作者: 機(jī)構(gòu) 時間: 2025-3-22 16:25 作者: GENRE 時間: 2025-3-22 19:21
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作者: 平庸的人或物 時間: 2025-3-22 23:26
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作者: 娘娘腔 時間: 2025-3-23 03:54
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作者: entrance 時間: 2025-3-23 06:12
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.作者: 驕傲 時間: 2025-3-23 12:18 作者: Override 時間: 2025-3-23 17:39 作者: 蛙鳴聲 時間: 2025-3-23 22:05
Analgesia During Surgery (Medications)In this chapter we will look at how to check the generalisation properties of trained neural networks. We will look especially at how to recognise overfitting and problems with data coming from different distributions.作者: bizarre 時間: 2025-3-24 00:57 作者: Brain-Imaging 時間: 2025-3-24 05:12 作者: 不開心 時間: 2025-3-24 09:45 作者: 反省 時間: 2025-3-24 11:12
Advanced Optimizers,ked at the gradient descent optimizer and its variations (mini-batch and stochastic). In this chapter, we look at more advanced and efficient optimizers. We look in particular at ., . and .. We cover the mathematics behind them and then explain how to implement and use them in Keras.作者: formula 時間: 2025-3-24 14:50 作者: 空中 時間: 2025-3-24 20:34 作者: enmesh 時間: 2025-3-24 23:30
https://doi.org/10.1007/978-3-319-66188-9ral networks comes into light when several (thousands, even millions) neurons interact with each other to solve a specific problem. The network architecture (how neurons are connected to each other, how they behave, and so on) plays a crucial role in how efficient the learning of a network is, how g作者: 難解 時間: 2025-3-25 07:08 作者: 莊嚴(yán) 時間: 2025-3-25 10:38 作者: follicle 時間: 2025-3-25 15:24 作者: Cursory 時間: 2025-3-25 18:44 作者: 裝入膠囊 時間: 2025-3-25 22:39
Tonia M. Young-Fadok,Ryan C. Craner 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作者: neutralize 時間: 2025-3-26 04:04
Enhanced Recovery after Surgery 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作者: 突變 時間: 2025-3-26 06:05 作者: BOLUS 時間: 2025-3-26 10:55 作者: Yourself 時間: 2025-3-26 13:36
https://doi.org/10.1007/978-1-4842-8020-1Deep Learning; TensorFlow 2; 0; Skilearn; Regularization; Dropout; Convolutional Neural Networks; Recursive作者: 合唱團(tuán) 時間: 2025-3-26 19:26
978-1-4842-8019-5Umberto Michelucci 2022作者: accrete 時間: 2025-3-26 22:14
itask learning.Explains how to build models and deploy them .Understand how neural networks work and learn how to implement them using TensorFlow 2.0 and Keras. This new edition focuses on the fundamental concepts and at the same time on practical aspects of implementing neural networks and deep lea作者: 無力更進(jìn) 時間: 2025-3-27 04:28 作者: Thyroid-Gland 時間: 2025-3-27 06:00
Enhanced Quality of Life and Smart Livinggression 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 discuss the two models and explain how to implement the two algorithms in Keras.作者: induct 時間: 2025-3-27 13:20 作者: 輕浮思想 時間: 2025-3-27 17:37 作者: Polydipsia 時間: 2025-3-27 20:13 作者: Exterior 時間: 2025-3-28 00:32 作者: 種植,培養(yǎng) 時間: 2025-3-28 04:41 作者: 不如樂死去 時間: 2025-3-28 07:45
https://doi.org/10.1007/978-3-319-66188-9e to start with a ., where data enters at the input layer and passes through the network, layer by layer, until it arrives at the output layer (this gives the networks their name: feed-forward neural networks).作者: arsenal 時間: 2025-3-28 12:56
Feed-Forward Neural Networks,e to start with a ., where data enters at the input layer and passes through the network, layer by layer, until it arrives at the output layer (this gives the networks their name: feed-forward neural networks).作者: 無關(guān)緊要 時間: 2025-3-28 14:58 作者: 機(jī)警 時間: 2025-3-28 21:35
Generative Adversarial Networks (GANs),, one network will generate human faces as good as it can, and the second network will criticize the results and tell the first network how to improve upon the faces. The two networks learn from each other, so to speak. This chapter looks in detail at how this works and explains how to implement an easy example in Keras.作者: GROWL 時間: 2025-3-28 23:52
Perioperative Smoking and Alcohol Cessation discuss only the very basic components of RNNs to elucidate the very fundamental aspects. I hope you find it useful. At the end of the chapter, I suggest further reading in case you find the subject interesting and want to better understand RNNs.作者: ectropion 時間: 2025-3-29 04:17 作者: 暗語 時間: 2025-3-29 08:01 作者: grounded 時間: 2025-3-29 15:16
Enhanced Recovery after Surgery, one network will generate human faces as good as it can, and the second network will criticize the results and tell the first network how to improve upon the faces. The two networks learn from each other, so to speak. This chapter looks in detail at how this works and explains how to implement an easy example in Keras.作者: SLING 時間: 2025-3-29 16:32 作者: 遠(yuǎn)地點(diǎn) 時間: 2025-3-29 21:03 作者: Hla461 時間: 2025-3-30 00:56 作者: 業(yè)余愛好者 時間: 2025-3-30 07:07
