Tag Archives: machine learning

### Understanding Generative Models and Applications

Generative Models are a form of unsupervised learning models and the outcome goal is to generate new data points by understanding underlying distributions of data points in the dataset. After the deep learning revolution, Generative models are very popular and widely used in applications and researc...

### Comparison of Sigmoid, Tanh and ReLU Activation Functions

Introduction In Artificial Neural network (ANN), activation functions are the most informative ingredient of Deep Learning which is fundamentally used for to determine the output of the deep learning models. In this blog, we will discuss the working of the ANN and different types of the Activat...

### 8 Important Evaluation Metrics for Classification Models

This post explains important evaluation metrics to check while measuring the performance of a classification model. These are Accuracy, Precision, Recall, Sensitivity, Specificity, False Positive Rate, False Negative Rate, and F1 Score. We’ll cover one by one each metric by calculating its value u...

### Understand Confusion Matrix Using Real-life Classification Example

First I’d like to explain what is the use of a  confusion matrix. Classification Problems are solved using Supervised Machine learning algorithms. In these problems, our goal is to categories an object using its features. For e.g, Identify a fruit using its taste, color and size or check out ...

### Explain Echelon Form of a Matrix

Echelon Form of a matrix is used to solve a linear equation by converting a complex matrix to a simple matrix. A matrix is in an Echelon Form if it satisfies some conditions which we’ll discuss in this post. We must know how to convert a matrix into Echelon Form and simplify our matrix for [&h...

### Working of Bagging and Boosting in Ensemble Learning

Bagging and Boosting both are Ensemble Learning techniques. Ensemble Methods are an important addition to Data Scientists toolbox. Here, weak learners combined together to become strong learners. And offers better performance than an individual one. What is an Ensemble Method The main logic behind t...

### What is the difference between Variance and Bias in Machine Learning?

From the perspective of Supervised Machine Learning, we know all models have errors. We need to minimize the error so as to make the model useful. For this, we need to minimize two major sources of error- Bias and Variance. What is Bias? The tendency of the algorithm to learn wrong details from the ...

### Decision Tree vs Random Forest in Machine Learning

Machine Learning is the sub-branch of Artificial Intelligence. It gives a system the ability to learn and become better from past experiences. Decision tree and random forest are two Supervised Machine Learning techniques. A decision tree is a simple and decision-making diagram. Certainly, for a muc...

### Decision Tree in Machine Learning with Example

Decision Tree algorithm belongs to the Supervised Machine Learning. It can use to solve Regression and Classification problems. It creates a training model which predicts the value of target variables by learning decision rules inferred from training data. What is Decision Tree? It is easy...

### Solving Regression Problems Using Neural Network

This tutorial explains solving regression problems using a neural network approach instead of using supervised machine learning algorithms. You’ll be able to solve simple regression problems at the end of this tutorial. We’ll use Jupyter Notebook to write the code and TensorFlow as our m...