It can not only process single data points (such as images), but also entire sequences of data (such as speech or video). For example, LSTM is applicable to tasks such as unsegmented, connected handwriting recognition or speech recognition. Unlike standard feedforward neural networks, LSTM has feedback connections that make itContinue Reading
Images – A table of pixels, usually of RGB(3D columns – a.k.a a tensor) – can be ravled/unrolled/flattened to a 1D longer array. Sound – long series of numbers(1D array) when digitally recorded(x = time(duration column), y = amplitude of the sound) – usually represented by frequences. Text – partsContinue Reading
* Support vector machines analysis are supervised learning models that analyze data, and are used for classification analysis and regression analysis. * The minimization function is the error of how much misclassified points are there in the model(graph), (either inside the margins or of course outside of them), and alsoContinue Reading
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Transforming a signal from a function of time or space to frequency(ω) which holds within it’s unique (ω) key also the respected phase(φ) and a respected amplitude/magnitude (A), is called a fourier transform. This is possible because any signal can be represented as a sum of sinusoids. f(x) –> TransformContinue Reading
Regression is estimation(correlation) – of the independent variable with the dependent variable.(We can make a scatter plot whenever you have data that relates two values together). We need an equation – which models the data for y(dependent) for any x(independent), that is, the correlation between them. If the data pointsContinue Reading
Given a line ‘a’ and a point ‘b’ we want to find a scaled version(λ)’a’that is as close to ‘b’ as possible. The right answer is where the line of λa to b is at a right angle to line a. This line is the difference of the line λa and theContinue Reading
Bayes’ Theorem – Is the probability Event A will happen given (|) Event B happened. P(A|B) = P(B|A) * P(A) / P(B) For example (Assume we have 3 out of 4 probabilities): Calculate a probability that an event A(Eating) will happen given (|) that an event B(Exercising) happened. P(A|B) = P(B|A)Continue Reading
Computer machine rounding errors produce values that are close to zero, but are not zero, thus they increase the ranks of a matrix when they shouldn’t. In order to solve this problem a threshold is determined so that the values become zero if they are below the threshold and thusContinue Reading
Decrease the learning rate. Increasing (multiplying) the number of filters in each convolution layer in the ANN. Add extra convolutional layers (can duplicate the existing ones). Add more DropOut layers. All , and any of these steps lead to improving generality accuracy of the ANN model.Continue Reading