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
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
Principal Component Analysis – PCA is the process of projecting variance of data using minimal dimensions. The direction of dimension along which the variance is maximised(highest) will be chosen as the first principal component. The next best direction as the second P.C. and should be orthogonal to the first P.C,Continue Reading
Auto Encoders try to find the relations of the input with itself. These relations able the Auto Encoder algorithm to reproduce the same input via the relations it found. Auto Encoders – try to find the identity function f(x) using the latent factors of the data via f(x) = g(L)Continue Reading
Yair Shinar for Clarity and Solutions