Technical Interview Questions
Overfitting can be prevented by using the following methodologies: Cross-validation: The idea behind cross-validation is to split the training data in order to generate multiple mini train-test splits. These splits can then be used to tune your model. More training data: Feeding more data to the machine learning model can help in better analysis and … Read more
Grid Search Grid search trains the network for every combination by using the two set of hyperparameters, learning rate and the number of layers. Then evaluates the model by using Cross Validation techniques. Random Search It randomly samples the search space and evaluates sets from a particular probability distribution. For example, instead of checking all … Read more
Hyperparameters are variables that define the structure of the network. For example, variables such as the learning rate, define how the network is trained. They are used to define the number of hidden layers that must be present in a network. More hidden units can increase the accuracy of the network, whereas a lesser number … Read more
Feedforward Neural Network The simplest form of ANN, where the data or the input travels in one direction. The data passes through the input nodes and exit on the output nodes. This neural network may or may not have the hidden layers. Convolutional Neural Network Here, input features are taken in batch wise like a … Read more
Reactive Machines AI: Based on present actions, it cannot use previous experiences to form current decisions and simultaneously update their memory. Example: Deep Blue Limited Memory AI: Used in self-driving cars. They detect the movement of vehicles around them constantly and add it to their memory. Theory of Mind AI: Advanced AI that has the … Read more