QuAIT

Hello, everyone! We are back again with another interesting concept of Applied deep learning. Last week we came across CNN and its implementation. Today we are about to see a new concept known as RNN (Recurrent Neural Networks). So let’s get started!! Introduction: Recurrent Neural Network(RNN) is the finest state of art algorithm used for the representation of sequential data. It has a unique ability to break through the field of NLP. it’s considered a splendid algorithm because of its ability to remember inputs through internal memory and ideally suitable for solving machine learning problems related to sequential data. It’s accomplished as the most powerful and robust neural network. These networks are precise in predicting future data more efficiently. RNN connections are interlinked with the help of a directed graph sequence. Forward and backward propagation is implemented simultaneously to recover the wrong predictions with a few gradual changes. It capable of providing an extens...

Churn Modelling Problem In this part, you'll be solving a knowledge analytics challenge for a bank. you'll tend a dataset with an outsized sample of the bank’s customers. To form this dataset, the bank gathered information like customer id, credit score, gender, age, tenure, balance, if the customer is active, features a master card , etc. During 6 months, the bank observed if these customers left or stayed within the bank. Your goal is to form a man-made Neural Network which will predict, supported geo-demographic and transactional information given above, if a person customer will leave the bank or stay (customer churn). Besides, you're asked to rank all the purchasers of the bank, supporting their probability of leaving. to try to do that, you'll have to use the proper Deep Learning model, one that's supported by a probabilistic approach. If you achieve this project, you'll create significant added value to the bank. By applying your Deep Learning model the b...

  Classification Types  In machine learning, classification is supervised learning in which the machine learns from data, and by using this trained data it classifies the new data. -We have a few types of classification algorithms in machine learning: Linear classifiers ● Logistic regression ● Naive Bayes classifier Non-Linear classifiers ● K- nearest neighbor ● Support vector machines ● Decision trees ● Random forest ● Neural networks  Logistic regression(Predictive learning model) Logistic Regression is a mathematical model used in statistics to predict the probability of an event using the trained data. The predicted output always lies between 0 and 1. The goal of logistic regression is to find the best fitting model to describe the relationship between the dependent and independent variables. Naive Bayes Classifiers(Generative learning model) The Naive Bayes model is mostly used for large datasets. This is a method of assigning class labels...