C2 W1 Lab01 Neurons And Layers吴恩达中英 Pytorch版 Csdn博客

There was an error while loading. Please reload this page. In this lab we will explore the inner workings of neurons/units and layers. In particular, the lab will draw parallels to the models you have mastered in Course 1, the regression/linear model and the logistic model. The lab will introduce Tensorflow and demonstrate how these models are implemented in that framework. Tensorflow and Keras Tensorflow is a machine learning package developed by Google.

In 2019, Google integrated Keras into Tensorflow and released Tensorflow 2.0. Keras is a framework developed independently by François Chollet that creates a simple, layer-centric interface to Tensorflow. This course will be using the Keras interface. We'll use an example from Course 1, linear regression on house prices. The function implemented by a neuron with no activation is the same as in Course 1, linear regression: $$ f_{\mathbf{w},b}(x^{(i)}) = \mathbf{w}\cdot x^{(i)} + b \tag{1}$$ We can define a layer with one neuron or unit and compare it to the familiar linear regression function.

There was an error while loading. Please reload this page. In this lab we will explore the inner workings of neurons/units and layers. In particular, the lab will draw parallels to the models you have mastered in Course 1, the regression/linear model and the logistic model. The lab will introduce Tensorflow and demonstrate how these models are implemented in that framework. Tensorflow and Keras Tensorflow is a machine learning package developed by Google.

In 2019, Google integrated Keras into Tensorflow and released Tensorflow 2.0. Keras is a framework developed independently by François Chollet that creates a simple, layer-centric interface to Tensorflow. This course will be using the Keras interface. We'll use an example from Course 1, linear regression on house prices. The function implemented by a neuron with no activation is the same as in Course 1, linear regression: fw,b(x(i))=w⋅x(i)+b(1) f_{\mathbf{w},b}(x^{(i)}) = \mathbf{w}\cdot x^{(i)} + b \tag{1}fw,b​(x(i))=w⋅x(i)+b(1) We can define a layer with one neuron or unit and compare it to the familiar linear regression function.

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