Artificial Neural Network – Regularization

Today I want to talk about the most common issue in deep and machine learning problem and that is overfitting and underfitting issue. In case of underfitting, it was a problem in machine learning centuries back, when we have less predictors to predict something. But in the era of Big data, we can avoid it … Read moreArtificial Neural Network – Regularization

Artificial Neural Network – Forward and Backward Propagation

Let’s start the discussion on Forward Propagation in ANN. I will try to explain it in a very simple way. If you have any difficulty to understand then please let me know in the below comment section. Well, first of all we shall decide our neural network layer number. Let’s say 2. Input layer -> … Read moreArtificial Neural Network – Forward and Backward Propagation

Artificial Neural Network – Activation Function

Today’s topic is Activation functions of ANN. Let me list out the most common activation functions. Sigmoid function . ReLU ( Rectified linear unit). Tanh Leaky ReLU. Let’s start the discussion with a simple example: If we try to predict house price then which function can we use? If we use sigmoid function then the … Read moreArtificial Neural Network – Activation Function

Artificial Neural Network – Introduction

Today, I want to discuss about a very important algorithm in Machine learning (or you can say the heart of Deep learning) and that is ANN or Artificial Neural Network. Where did the concept come from ? On the left , let me show you a very simple picture of a human neuron. Dendrites send … Read moreArtificial Neural Network – Introduction

Machine Learning is Fun – Part 17 – Recommendation System Part 3

Let’s start today’s discussion on product recommendation system. Key points as apart of Exploratory Data Analysis: Collect data from source in csv or excel format or in a database. Make sure the customer and product id are well maintained in the table (perform cleansing on null rows). Make the list of most frequent items association … Read moreMachine Learning is Fun – Part 17 – Recommendation System Part 3

Machine Learning is Fun – Part 16 – Recommendation System Part 2

In my last post, I explained the concept and design of recommendation system for movie based products. Today, I want to explain the model for hotel based recommendation system. You must collect demographic data of customers who have booked the hotels and you can get it easily from any online hotel-booking website like makemytrip or … Read moreMachine Learning is Fun – Part 16 – Recommendation System Part 2

Machine Learning is Fun – Part 15 – Recommendation System

Hello! I hope you enjoyed my last few articles on Machine learning. Today, I want to talk about a very interesting topic, that is recommendation system. There are a number of articles on this topic. But I do not want to re-explain any of these. My intention is to make you understand the techniques that … Read moreMachine Learning is Fun – Part 15 – Recommendation System

Machine Learning is Fun – Part 14 – Bias/Variance

Let us try to explain today’s topic without delving into statistics. Consider that we are analysing height of men for a population of m number of dataset. Now, if we are Indian, we shall attempt to collect data from Indian men because it is the easiest way to collect. Am I correct? After collecting a … Read moreMachine Learning is Fun – Part 14 – Bias/Variance

Machine Learning is Fun – Part 13 – Decision Tree

Hi Friends, Today I would like to discuss on the most popular classification and regression algorithm (better to say a set of algorithms) named Decision Tree. What is Decision tree? By the name suggested in the model, it is a tree-based structural model to identify or classify or predict value of any outcome of an … Read moreMachine Learning is Fun – Part 13 – Decision Tree

Machine Learning is Fun – Part 12 – ARIMA

What is ARIMA? Actually, it is a combination of two models – AR and MA. AR – Auto Regressive. MA – Moving Average. Lots of jargons? Let me make it clear. Auto Regressive means when there is some correlation between values in a time series and the values that precede and succeed them. yt = … Read moreMachine Learning is Fun – Part 12 – ARIMA