Neural Networks and TensorFlow - Deep Learning Series [Part 11]
deep-learning·@cristi·
0.000 HBDNeural Networks and TensorFlow - Deep Learning Series [Part 11]
 In this lesson you're going to learn how to implement logistic regression using TensorFlow in Python. Implementing linear regression was relatively easy. Logistic regression is not hard either, nor does it involve something much different than linear regression. In logistic regression, we're simply using a non-linearity for activation. To be more precise, we're using a non-linear function for activation. Most often, this non-linearity is a sigmoid, a hyperbolic tangent, or a rectified linear unit. What's great about TensorFlow is that it comes with methods to simply call these functions, without actually hard coding them. So, it could either be: - tf.sigmoid - tf.tanh - tf.nn.relu And this makes the implementation much more convenient and easier. Of course, you're better off if you know what these function do and how they look under the hood, but not everyone needs to know this in order to work with TensorFlow. Anyhow, please see the video below for doing logistic regression in TensorFlow. ___ <center>https://www.youtube.com/watch?v=LgNpIPYstw0</center> ___ ### <center>To stay in touch with me, follow @cristi</center> ___ [Cristi Vlad](http://cristivlad.com) Self-Experimenter and Author
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