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This note is based on Coursera course by Andrew ng. (It is just study note for me. It could be copied or awkward sometimes for sentence anything, because i am not native. But, i want to learn Deep Learning on English. So, everything will be bettter and better :)) INTRO What are hyperparameters? MAIN Parameters our model are W and b, and there are other things we need to know such as the learning..

This note is based on Coursera course by Andrew ng. (It is just study note for me. It could be copied or awkward sometimes for sentence anything, because i am not native. But, i want to learn Deep Learning on English. So, everything will be bettter and better :)) INTRO We have already seen the basic building blocks of forward propagation and back propagtaion, the key components we need to implem..

This note is based on Coursera course by Andrew ng. (It is just study note for me. It could be copied or awkward sometimes for sentence anything, because i am not native. But, i want to learn Deep Learning on English. So, everything will be bettter and better :)) INTRO We have all been hearing that deep neural network work really well for a lot of problems, and it's not just that they needd to b..

This note is based on Coursera course by Andrew ng. (It is just study note for me. It could be copied or awkward sometimes for sentence anything, because i am not native. But, i want to learn Deep Learning on English. So, everything will be bettter and better :)) Let's see how we can perform forward propagation in a deep network. Given a single training example x, here is how we compute the acti..

This note is based on Coursera course by Andrew ng. (It is just study note for me. It could be copied or awkward sometimes for sentence anything, because i am not native. But, i want to learn Deep Learning on English. So, everything will be bettter and better :)) By now, we have actually seen most of the ideas we need to implement a deep neural network. Forward propagation, back propagation with..

This note is based on Coursera course by Andrew ng. (It is just study note for me. It could be copied or awkward sometimes for sentence anything, because i am not native. But, i want to learn Deep Learning on English. So, everything will be bettter and better :)) When we change our neural network, it's import to initialize the weights randomly. For logistic regression, it was okay to initialize ..

This note is based on Coursera course by Andrew ng. (It is just study note for me. It could be copied or awkward sometimes for sentence anything, because i am not native. But, i want to learn Deep Learning on English. So, everything will be bettter and better :)) When we implement back propagation for our neural network, we need to compute the derivative of the activation functions. So, let's ta..

This note is based on Coursera course by Andrew ng. (It is just study note for me. It could be copied or awkward sometimes for sentence anything, because i am not native. But, i want to learn Deep Learning on English. So, everything will be bettter and better :)) When we build our neural network, one of the choices you get to make is what activation function to use in the hidden layers, as well ..

This note is based on Coursera course by Andrew ng. (It is just study note for me. It could be copied or awkward sometimes for sentence anything, because i am not native. But, i want to learn Deep Learning on English. So, everything will be bettter and better :)) Let's see what this image below means. You saw the overview of single hidden layer NN. Now, let's go through the details of extactly h..

This note is based on Coursera course by Andrew ng. (It is just study note for me. It could be copied or awkward sometimes for sentence anything, because i am not native. But, i want to learn Deep Learning on English. So, everything will be bettter and better :)) In the deep learning era, we usually find training on relatively large data sets, because that is when deep learning algorithms tend t..