標(biāo)題: Titlebook: Beginning Deep Learning with TensorFlow; Work with Keras, MNI Liangqu Long,Xiangming Zeng Book 2022 Liangqu Long and Xiangming Zeng 2022 T [打印本頁] 作者: 帳簿 時間: 2025-3-21 17:18
書目名稱Beginning Deep Learning with TensorFlow影響因子(影響力)
書目名稱Beginning Deep Learning with TensorFlow影響因子(影響力)學(xué)科排名
書目名稱Beginning Deep Learning with TensorFlow網(wǎng)絡(luò)公開度
書目名稱Beginning Deep Learning with TensorFlow網(wǎng)絡(luò)公開度學(xué)科排名
書目名稱Beginning Deep Learning with TensorFlow被引頻次
書目名稱Beginning Deep Learning with TensorFlow被引頻次學(xué)科排名
書目名稱Beginning Deep Learning with TensorFlow年度引用
書目名稱Beginning Deep Learning with TensorFlow年度引用學(xué)科排名
書目名稱Beginning Deep Learning with TensorFlow讀者反饋
書目名稱Beginning Deep Learning with TensorFlow讀者反饋學(xué)科排名
作者: facilitate 時間: 2025-3-21 23:24
Valerie J. H. Powell,Franklin M. Ding 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.作者: 花費 時間: 2025-3-22 01:50
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.) assumption.作者: 教義 時間: 2025-3-22 08:20
Monitoring of membrane bioreactorso implement. It is very stable when trained using neural networks, and the resulting images are more approximate, but the human eyes can still easily distinguish real pictures and machine-generated pictures.作者: 道學(xué)氣 時間: 2025-3-22 10:31
Neural Networks,om the training set and use the trained relationship to predict new samples. Neural networks belong to a branch of research in machine learning. It specifically refers to a model that uses multiple neurons to parameterize the mapping function ..作者: Coterminous 時間: 2025-3-22 15:29 作者: HERE 時間: 2025-3-22 20:48
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.) assumption.作者: RENAL 時間: 2025-3-22 23:25
Generative Adversarial Networks,o implement. It is very stable when trained using neural networks, and the resulting images are more approximate, but the human eyes can still easily distinguish real pictures and machine-generated pictures.作者: 神圣在玷污 時間: 2025-3-23 03:27 作者: 一瞥 時間: 2025-3-23 09:07 作者: comely 時間: 2025-3-23 13:01
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作者: outer-ear 時間: 2025-3-23 14:22
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 作者: 奇思怪想 時間: 2025-3-23 20:08
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作者: 提名 時間: 2025-3-24 00:14 作者: 記憶法 時間: 2025-3-24 02:39 作者: 嚴(yán)厲批評 時間: 2025-3-24 06:51 作者: Veneer 時間: 2025-3-24 14:05
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作者: SEVER 時間: 2025-3-24 18:31
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.)作者: 芭蕾舞女演員 時間: 2025-3-24 22:57
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作者: SLUMP 時間: 2025-3-25 02:27 作者: aphasia 時間: 2025-3-25 05:15
Transport phenomena in membrane separationsonditional probability .(.| .) given the sample .. With the booming social network today, it is relatively easy to obtain massive sample data ., such as photos, voices, and texts, but the difficulty is to obtain the label information corresponding to these data. For example, in addition to collectin作者: 漫步 時間: 2025-3-25 09:47 作者: interior 時間: 2025-3-25 12:55 作者: 健談 時間: 2025-3-25 16:48
Fatty Acids and Growth Regulation Internet and mobile terminals. When we introduced the algorithm earlier, most of the datasets were commonly used classic datasets. The downloading, loading, and preprocessing of the dataset can be completed with a few lines of TensorFlow code, which greatly improves the research efficiency. In actu作者: GEAR 時間: 2025-3-25 22:22 作者: 混亂生活 時間: 2025-3-26 01:26 作者: Lethargic 時間: 2025-3-26 04:55
https://doi.org/10.1007/978-1-4842-7915-1TensorFlow; Deep Learning; Machine Learning; Keras; Deep Learning Fundamentals; Deep Learning real exampl作者: IOTA 時間: 2025-3-26 09:05 作者: faculty 時間: 2025-3-26 15:29
Introduction to Artificial Intelligence,reatly 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作者: 和音 時間: 2025-3-26 16:56 作者: Perceive 時間: 2025-3-26 22:41 作者: Simulate 時間: 2025-3-27 01:20
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作者: 知識分子 時間: 2025-3-27 05:44 作者: 發(fā)生 時間: 2025-3-27 09:38
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.作者: nominal 時間: 2025-3-27 16:28 作者: Breach 時間: 2025-3-27 20:54
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.)作者: 值得贊賞 時間: 2025-3-28 01:30 作者: 平靜生活 時間: 2025-3-28 03:37
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作者: 會犯錯誤 時間: 2025-3-28 10:06 作者: entail 時間: 2025-3-28 11:11 作者: Oligarchy 時間: 2025-3-28 16:29
Reinforcement Learning,ith the environment in order to learn strategies that can achieve good results. Different from supervised learning, the action of reinforcement learning does not have clear label information. It only has the reward information from the feedback of the environment. It usually has a certain lag and is作者: 品牌 時間: 2025-3-28 22:41
Customized Dataset, Internet and mobile terminals. When we introduced the algorithm earlier, most of the datasets were commonly used classic datasets. The downloading, loading, and preprocessing of the dataset can be completed with a few lines of TensorFlow code, which greatly improves the research efficiency. In actu作者: resilience 時間: 2025-3-28 23:48
Book 2022king with a wide variety of neural network types such as GANs andRNNs.??.Deep Learning is a new area of Machine Learning research widely used in popular applications, such as voice assistant and self-driving cars. Work through the hands-on material in this book and become a TensorFlow programmer!? ?作者: 拋射物 時間: 2025-3-29 04:05 作者: 包租車船 時間: 2025-3-29 08:37 作者: 淘氣 時間: 2025-3-29 14:12 作者: Redundant 時間: 2025-3-29 16:27 作者: 可憎 時間: 2025-3-29 23:35 作者: 膠狀 時間: 2025-3-30 01:53 作者: 可能性 時間: 2025-3-30 04:50
Autoencoder,o complete customer data labeling tasks. The scale of data required for deep learning is generally very large. This method of relying heavily on manual data annotation is expensive and inevitably introduces the subjective prior bias of the annotator.作者: DEAWL 時間: 2025-3-30 08:16 作者: Panacea 時間: 2025-3-30 14:35
Juan Ferré,Jeroen Van Rie,Susan C. Macintoshartificial neural networks to simulate the mechanism of biological neurons [1]. This research was further developed by the American neurologist Frank Rosenblatt into the perceptron model [2], which is also the cornerstone of modern deep learning.作者: needle 時間: 2025-3-30 19:15
https://doi.org/10.1007/978-1-4615-4269-8xt we are reading, the speech signal emitted when we speak, and the stock market that changes over time. This type of data does not necessarily have local relevance, and the length of the data in the time dimension is also variable. Convolutional neural networks are not good at processing such data.作者: 紋章 時間: 2025-3-30 21:47
Franklin M. Din,Valerie J. H. Powellns abstracted by Keras. Users can easily switch between different backend operations through Keras. Because of Keras’s high abstraction and ease of use, according to KDnuggets, Keras market share reached 26.6% as of 2019, an increase of 19.7%, second only to TensorFlow in deep learning frameworks.作者: 不合 時間: 2025-3-31 01:26 作者: Maximize 時間: 2025-3-31 07:38 作者: Lignans 時間: 2025-3-31 10:44
Customized Dataset,esigning excellent network model training process, and deploying the trained model to platforms such as mobile and the Internet network is an indispensable link for the implementation of deep learning algorithms.作者: Affection 時間: 2025-3-31 15:27
Take advantage of years of online research to learn TensorFlIncorporate deep learning into your development projects through hands-on coding and the latest versions of deep learning software, such as TensorFlow 2 and Keras. The materials used in this book are based on years of successful online educ
