Blog - AITUDE

Computer Vision, Deep Learning, TensorFlow

Implement Non-linear Independent Components Estimation with Tensorflow

Posted on June 1, 2021 by Sandeep Kumar

Non-linear Independent Components Estimation is a normalizing flow-based generative model which is used to learn the probability distribution of the real samples and then generate the new sample data. This algorithm can be categorized as generative models as GAN and VAE but using a different approac...

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...

Deep Learning

Understanding Generative Models and Applications

Posted on April 5, 2021 by Sandeep Kumar

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...

TensorFlow

Build an Image Dataset in TensorFlow

Posted on September 30, 2020 by Sandeep Kumar

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 ImageDataGenerator This is the most easiest way to prepare an image dataset. You just need to specify the dataset f...

ANN, Artificial Intelligence, Deep Learning, Machine Learning

Comparison of Sigmoid, Tanh and ReLU Activation Functions

Posted on August 19, 2020 by Divyang Goswami

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...

Artificial Intelligence

Understand Types of Environments in Artificial Intelligence

Posted on March 26, 2020 by Sandeep Kumar

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 using artificial intelligence. For example, program a chess bot, the environment is a chessboard and creating a room cl...

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...

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