Tweak cross in binary smoothly

Aug 6th, 2022
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How to Tweak cross in binary

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hey guys welcome to digital screening channel on youtube and in this short video im going to talk about binary cross entropy using plain english mostly and a very little bit of math and the reason im talking about this is binary cross entropy of course we use this as a loss function for classification purposes and even in semantic segmentation and of course even in generative adversarial networks if you actually look at your discriminators or generators this is a quite common loss function that we use so it only makes sense for us to understand this a little bit better so lets jump in and before doing that a quick request whenever you watch any of my videos for example in this case this is one of the recent videos go ahead and hit the like button if you really like it and that tells me what type of content youre actually into so ill put more effort into liking those and also the style or the way that i cover these topics so feedback is very important also do subscribe to my channe

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Uses an LSTM to predict the next days stock movement based on sequence of previous days. I used this project to gain experience working with LSTMs and time series data.
Binary cross entropy compares each of the predicted probabilities to actual class output which can be either 0 or 1. It then calculates the score that penalizes the probabilities based on the distance from the expected value. That means how close or far from the actual value.
Yes but usually RNN works best with the time series data where past information needs to be incorporated. But if sole classification is the end goal and data is non-time series, a simple algorithm from logistic regression for binary classification should be suffice as it will reduce implementation algorithm complexity.
Loss Function: Binary Cross-Entropy / Log Loss For each green point (y=1), it adds log(p(y)) to the loss, which means that the log probability of it being green. On the other hand, it will add log(1-p(y)), which means that the log probability of it being red, for each red point (y=0).
For binary classification problems that give output in the form of probability, binarycrossentropy is usually the optimizer of choice. meansquarederror may also be used instead of binarycrossentropy as well.
Support Vector Machine You can use an SVM with more than two classes, in which case the model will create a set of binary classification subproblems (with one SVM learner for each subproblem). There are a couple of strong advantages of using an SVM. First, it is extremely accurate and tends not to overfit data.
For binary classification problems that give output in the form of probability, binarycrossentropy is usually the optimizer of choice. meansquarederror may also be used instead of binarycrossentropy as well. Metrics used is accuracy. The model is trained for 50 epochs with a batch size of 1.
To train a deep neural network to classify sequence data, you can use an LSTM network. An LSTM network enables you to input sequence data into a network, and make predictions based on the individual time steps of the sequence data.

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