Concepts in Machine Learning-CST 383 KTU-Minor Notes-Dr Binu V P

The Perceptron is inspired by the information processing of a single neural cell called a neuron. A neuron accepts input signals via its dendrites, which pass the electrical signal down to the cell body. In a similar way, the Perceptron receives input signals from examples of training data that we weight and combined in a linear equation called the activation. $activation = sum(weight_i * x_i) + bias$ The activation is then transformed into an output value or prediction using a transfer function, such as the step transfer function. $prediction = 1.0$  if activation >= 0.0 else 0.0 In this way, the Perceptron is a classification algorithm for problems with two classes (0 and 1) where a linear equation (like or hyperplane) can be used to separate the two classes. It is closely related to linear regression and logistic regression that make predictions in a similar way (e.g. a weighted sum of inputs). The weights of the Perceptron algorithm must be estimated from your training data

Using the image and cascades Computer vision is an exciting and growing field. There are tons of interesting problems to solve! One of them is face detection: the ability of a computer to recognize that a photograph contains a human face, and tell you where it is located . OpenCV cascade breaks the problem of detecting faces into multiple stages. For each block, it does a very rough and quick test. If that passes, it does a slightly more detailed test, and so on. The algorithm may have 30 to 50 of these stages or cascades, and it will only detect a face if all stages pass. Now we create the cascade and initialize it with our face cascade. This loads the face cascade into memory so it’s ready for use. Remember, the cascade is just an XML file that contains the data to detect faces. import cv2 imagePath = "faces.png" cascPath = "haarcascade_frontalface_default.xml" # Create the haar cascade faceCascade = cv2.CascadeClassifier(cascPath) # Read the image image = cv2.imr