Machine Learning Archives - Page 6 of 8

A comparison between Google Colab and Kaggle

Posted on September 1, 2022 by Sandeep Kumar

Google Colab and Kaggle are two of the widely used platforms that provide the programming environments to its users. In this article, a comparison between Google Colab and Kaggle is presented. Google Colab: Colaboratory, or “Colab” for short, is a Google Research product. Colab allows an...

Annotation Converters for Object Detection

Posted on March 28, 2022 by Sandeep Kumar

Data labelling is an important task in Machine Learning. The quality of data we feed in the model will determine how well our model performs. Image annotation is the process of labeling images of a dataset for the machine learning model. It is used to label the features we need our model to recogniz...

Multiclass Classification on Highly Imbalanced Dataset

Posted on February 28, 2022 by Sandeep Kumar

Multiclass classification is a classification problem where more than two classes are present. It is a fundamental machine learning task which aims to classify each instance into one of a predefined set of classes. For instance, classifying a set of images of animals as dogs, cats or rabbits. Each s...

Deep Learning, Machine Learning, TensorFlow

How to Program Generative Adversarial Network with Tensorflow 2.0

Posted on April 27, 2021 by Sandeep Kumar

This guide is a hands-on tutorial to program a generative adversarial network with TensorFlow 2.0 to generate new data using the past data. We need to train two networks, Generator, and Discriminator, using alternative approach where for each batch, we train the discriminator first such that it iden...

Deep Learning, Machine Learning, TensorFlow, Uncategorized

Step-by-Step Guide to Build CNN Model with Tensorflow

Posted on April 24, 2021 by Sandeep Kumar

This tutorial is a step-by-step guide to create, train and evaluate a CNN Model with TensorFlow. Mainly there are 3 approaches to define a convolutional neural network with TensorFlow. The best way to become comfortable to define a CNN at the end of this post is to try each step yourself while going...

Deep Learning, Machine Learning

How Does Variational Autoencoder Work? Explained!

Posted on April 19, 2021 by Sandeep Kumar

Variational Autoencoder is a an explicit type generative model which is used to generate new sample data using past data. VAEs do a mapping between latent variables, dominate to explain the training data and underlying distribution of the training data. These latent variables vectors can be used to ...

Deep Learning, Machine Learning

Basics of Generative Adversarial Network Model

Posted on April 10, 2021 by Sandeep Kumar

Generative Adversarial Network a.k.a GANs is a generative model which is used to generate new samples using training data. The output samples are similar to the training data but not exactly the same as our goal is to generate more diverse data from what we already have. Goal of GANs The distributio...

Comparison of Sigmoid, Tanh and ReLU Activation Functions

Posted on August 19, 2020 by Sandeep Kumar

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...

Machine Learning

8 Important Evaluation Metrics for Classification Models

Posted on March 26, 2020 by Sandeep Kumar

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...

Machine Learning

Understand Confusion Matrix Using Real-life Classification Example

Posted on March 25, 2020 by Sandeep Kumar

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 ...