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…
Build an Image Dataset in TensorFlow
This guide is a hands-on tutorial to build an image dataset for deep learning in TensorFlow. You'll be familiar with all possible ways to accomplish this task in TensorFlow Using…
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…
Understand Types of Environments in Artificial Intelligence
The Environment is the surrounding world around the agent which is not part of the agent itself. It’s important to understand the nature of the environment when solving a problem…
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…
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…
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…
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…
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…
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…
