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utkuufuk.com | ||
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teddykoker.com
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| | | | | A few posts back I wrote about a common parameter optimization method known as Gradient Ascent. In this post we will see how a similar method can be used to create a model that can classify data. This time, instead of using gradient ascent to maximize a reward function, we will use gradient descent to minimize a cost function. Lets start by importing all the libraries we need: | |
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glowingpython.blogspot.com
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| | | | | Using regularization has many benefits, the most common are reduction of overfitting and solving multicollinearity issues. All of this is co... | |
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www.arrsingh.com
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| | | | | Linear Regression predicts the value of a dependent variable (y) given one or more independent variables (x1, x2, x3...xn). In this case, y is continuous - i.e. it can hold any value. In many real world problems[1], however, we often want to predict a binary value instead | |
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blog.keras.io
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| | | [AI summary] The text discusses various types of autoencoders and their applications. It starts with basic autoencoders, then moves to sparse autoencoders, deep autoencoders, and sequence-to-sequence autoencoders. The text also covers variational autoencoders (VAEs), explaining their structure and training process. It includes code examples for each type of autoencoder and mentions the use of tools like TensorBoard for visualization. The VAE section highlights how to generate new data samples and visualize the latent space. The text concludes with references and a note about the potential for further topics. | ||
