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TensorFlow in 1 Day: Make your own Neural Network完整版

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TensorFlow in 1 Day:Make your own Neural Network。题目相当霸气，能不能快速掌握tensorflow就在此书了。

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TensorFlowin1Day:Makeyourown

NeuralNetwork

ByKrishnaRungta



ALLRIGHTSRESERVED.Nopartofthispublicationmaybereproducedor

transmittedinanyformwhatsoever,electronic,ormechanical,including

photocopying,recording,orbyanyinformationalstorageorretrievalsystem

withoutexpresswritten,datedandsignedpermissionfromtheauthor.

TableOfContent

Chapter1:WhatisDeeplearning?

1. WhatisDeeplearning?

2. DeeplearningProcess

3. ClassificationofNeuralNetworks

4. TypesofDeepLearningNetworks

5. Feed-forwardneuralnetworks

6. Recurrentneuralnetworks(RNNs)

7. Convolutionalneuralnetworks(CNN)

Chapter2:MachineLearningvsDeepLearning

1. WhatisAI?

2. WhatisML?

3. WhatisDeepLearning?

4. MachineLearningProcess

5. DeepLearningProcess

6. AutomateFeatureExtractionusingDL

7. DifferencebetweenMachineLearningandDeepLearning

8. WhentouseMLorDL?

Chapter3:WhatisTensorFlow?

1. WhatisTensorFlow?

2. HistoryofTensorFlow

3. TensorFlowArchitecture

4. WherecanTensorflowrun?

5. IntroductiontoComponentsofTensorFlow

6. WhyisTensorFlowpopular?

7. ListofProminentAlgorithmssupportedbyTensorFlow

Chapter4:ComparisonofDeepLearningLibraries

1. 8BestDeeplearningLibraries/Framework

2. MICROSOFTCOGNITIVETOOLKIT(CNTK)

3. TenserFlowVsTheanoVsTorchVsKerasVsinfer.netVsCNTKVs

MXNetVsCaffe:KeyDifferences

Chapter5:HowtoDownloadandInstallTensorFlowWindowsandMac

1. TensorFlowVersions

2. InstallAnaconda

3. Create.ymlfiletoinstallTensorflowanddependencies

4. LaunchJupyterNotebook

5. Jupyterwiththemaincondaenvironment

Chapter6:JupyterNotebookTutorial

1. WhatisJupyterNotebook?

2. JupyterNotebookApp

3. HowtouseJupyter

Chapter7:TensorflowonAWS

1. PART1:Setupakeypair

2. PART2:Setupasecuritygroup

3. Launchyourinstance(Windowsusers)

4. Part4:InstallDocker

5. Part5:InstallJupyter

6. Part6:Closeconnection

Chapter8:TensorFlowBasics:Tensor,Shape,Type,Graph,Sessions&

Operators

1. WhatisaTensor?

2. RepresentationofaTensor

3. TypesofTensor

4. Shapeoftensor

5. Typeofdata

6. Creatingoperator

7. Variables

Chapter9:Tensorboard:GraphVisualizationwithExample

Chapter10:NumPy

1. WhatisNumPy?

2. WhyuseNumPy?

3. HowtoinstallNumPy?

4. MathematicalOperationsonanArray

5. ShapeofArray

6. np.zerosandnp.ones

7. ReshapeandFlattenData

8. hstackandvstack

Chapter11:Pandas

1. WhatisPandas?

2. WhyusePandas?

3. HowtoinstallPandas?

4. Whatisadataframe?

5. WhatisaSeries?

6. Concatenation

剩余448页未读，继续阅读

wgwgwgwg

2020-01-12

可以使用。

帮我解释一下下面的代码：class NeuralNetwork(nn.Module): def init(self): super(NeuralNetwork, sel...

这段代码定义了一个名为"NeuralNetwork"的类，该类继承自PyTorch的nn.Module类。在类的初始化函数__init__()中，首先调用了父类nn.Module的初始化函数super()来初始化神经网络模型。接着，定义了神经网络的各个...

解释这行代码：from sklearn.neural_network import MLPRegressor clf=MLPRegressor

这行代码导入了`sklearn.neural_network`模块中的`MLPRegressor`类，并将它赋值给变量`clf`。`MLPRegressor`是一个多层感知机回归器，用于解决回归问题。多层感知机是一种人工神经网络，它包含一个或多个隐藏层，每...

matlab中2022b版本含有neural network toolbox么

是的，MATLAB 2022b版本包含Neural Network Toolbox。Neural Network Toolbox是MATLAB的一个工具箱，用于开发和应用神经网络模型。它提供了各种功能和工具，用于设计、训练和评估神经网络，以及进行模型预测和分析。...

pytorch框架转为TensorFlow框架

1. ONNX：使用ONNX (Open Neural Network Exchange)将PyTorch模型转换为TensorFlow模型。ONNX是一种跨平台的开放式格式，它允许在不同的深度学习框架之间共享模型。 2. TensorFlow的PyTorch接口：TensorFlow提供了...

This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations: AVX AVX2 To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.

这是 TensorFlow 二进制文件的信息提示，告诉你该二进制文件是使用 oneAPI Deep Neural Network Library (oneDNN) 进行优化的。oneDNN 可以在 CPU 上加速 TensorFlow 的运行。提示还提到了 AVX 和 AVX2 指令，这些...

MATLAB中下载Neural Network Toolbox

在MATLAB中下载Neural Network Toolbox，您可以按照以下步骤进行操作： 1. 打开MATLAB并登录到您的MathWorks账户。 2. 在MATLAB命令窗口中输入"ver"，确认您的MATLAB版本是否支持Neural Network Toolbox。确保您有...

TypeError: neuralNetwork.init() missing 4 required positional arguments: 'inputnodes', 'hiddennodes', 'outputnodes', and 'learningrate'

这个错误提示表明在创建 `neuralNetwork` 对象时，缺少了4个必需的参数：`inputnodes`（输入节点数）、`hiddennodes`（隐藏节点数）、`outputnodes`（输出节点数）和 `learningrate`（学习率）。你需要提供这些参数...

class NeuralNet(nn.Module): ''' A simple fully-connected deep neural network '''

In this example, the neural network has two fully connected layers (`fc1` and `fc2`) with a ReLU activation function applied after the first layer. The `input_size` parameter specifies the size of the...

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TensorFlow in 1 Day: Make your own Neural Network完整版

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TensorFlow in 1 Day: Make your own Neural Network完整版

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Make Your Own Neural Network

Make Your Own Neural Network - Tariq Rashid

TensorFlow in 1 Day: Make your own Neural Network

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AttributeError: 'bp_neural_network' object has no attribute 'train'

给我推荐20个比较流行的部署机器学习模型的源码

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AttributeError: NeuralNetwork object has no attribute train

帮我解释一下下面的代码：class NeuralNetwork(nn.Module): def __init__(self): super(NeuralNetwork, sel...

解释这行代码：from sklearn.neural_network import MLPRegressor clf=MLPRegressor

matlab中2022b版本含有neural network toolbox么

pytorch框架转为TensorFlow框架

This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations: AVX AVX2 To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.

MATLAB中下载Neural Network Toolbox

TypeError: neuralNetwork.__init__() missing 4 required positional arguments: 'inputnodes', 'hiddennodes', 'outputnodes', and 'learningrate'

class NeuralNet(nn.Module): ''' A simple fully-connected deep neural network '''

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帮我解释一下下面的代码：class NeuralNetwork(nn.Module): def init(self): super(NeuralNetwork, sel...

TypeError: neuralNetwork.init() missing 4 required positional arguments: 'inputnodes', 'hiddennodes', 'outputnodes', and 'learningrate'