Pooling is a form of non-linear down-sampling(image reduction) with an aggregation function. Max pooling is the most common – It partitions the input image into a set of non-overlapping rectangles(N x N) and, for each such sub-region, outputs the maximum, thus, practically extracting the most significant features. The non-overlapping poolingContinue Reading
Artificial neural network is a structure composed of artificial neurons or nodes which are connected one to another. An artificial neuron receives one or more inputs that are separately weighted, and sums them all to produce an output. The output is passed through a non-linear function, known as an activationContinue Reading
Loss functions in ML(Machine Learning) are computational functions. These functions evaluate the price paid for inaccuracy of predictions in classification problems. Therefore, thier output should be minimized. The Loss Function used in Logistic Regression is cross entropy function: L(yˆ,y) = -(y*log(yˆ) + (1-y)*log(1-yˆ)) Where y is the ground truth outcomeContinue Reading
Logistic Regression can help predict specific values or categories with probability scores underlying the model’s classifications.Continue Reading
A classifier is a method for determining the most likely class of an unknown object or event based on a number of instances of each of the classes (AKA the training set). There are two main ways to determine this, either by prior knowledge of what the output valuesContinue Reading
Yair Shinar for Clarity and Solutions