AI Basics

  How training happens in ANN? Training in ML Algorithms: Consider Linear / Logistic Regression models where the training aimed at finding optimum values of coefficients / weights of the linear model (y =WX+B where W denotes the coefficient and C denotes the intercept of the line) Gradient Descent is one of the optimization technique for finding optimum weights of a model using iterative approach          W := W - Learning rate * derivative of Loss w.r.t W          B :=  B   - Learning rate * derivative of Loss w.r.t B W = coefficients of the linear model B = Intercept of the linear model Learning rate = Controllable parameter for getting optimum solution Loss function = Vector difference between predicted and actual outputs once we find optimum W,C value, we can able to perform the predictions in ML through newly trained model. In case of Neural networks, the above coefficients needs to be calculated for every node ...

  Deep Learning is a part of Machine Learning where the learning / model training mimics human brain, this is possible because of the Artificial Network that resembles the human neural network which is thinking pattern of human brain. Deep Learning can be applied to both Structured and Unstructured data. Deep Learning applications are again classified to 3 categories same as Machine Learning Deep Supervised Learning Deep Unsupervised Learning Deep Reinforcement Learning Artificial Neural Networks (ANN): ANN is the basic structure for any model training through Deep Learning Any ANN model contains an input layer, hidden layer/s and an output layer, based on the problem statement, the complexity (nodes,hidden layers) of the model will be increased. Node: Similar to the Neuron of human brain, Node is a basic computational unit of the ANN. Activation Function decides the range of output of the node, below are the types of activation functions Sigmoid Tanh ReLU (Rectified Linear Unit)...