Deep Learning - AITUDE

Artificial Intelligence, Deep Learning, Generative AI, LLM Applications, Machine Learning, Technology

Sigmoid vs ReLU: Activation Functions Explained for Deep Learning

Posted on February 5, 2026 by Sandeep Kumar

Sigmoid vs ReLU is one of the most important comparisons in deep learning. Sigmoid limits outputs between 0 and 1 and often slows training due to vanishing gradients, while ReLU allows faster learning and better performance in modern neural networks. Sigmoid vs ReLU is a common question for anyone l...

Artificial Intelligence, Deep Learning, LLM Applications, Machine Learning, Natural Language Processing

Automate Internal Links for better SEO

Posted on September 30, 2025 by Rupendra Choudhary

Internal linking is a powerful yet underutilized SEO strategy. Contextual internal links—those woven into your content’s narrative—connect related pages, boosting site authority and user engagement. Manually managing these links is time-consuming and unscalable. Automation streamlines the proc...

Artificial Intelligence, Deep Learning, LLM Applications, Machine Learning, Natural Language Processing

Top 5 Sentence Transformer Embedding Mistakes and Their Easy Fixes for Better NLP Results

Posted on September 30, 2025 by Rupendra Choudhary

Are you using Sentence Transformers (like SBERT) but not getting the precision you expect? These powerful models transform text into embeddings—numerical representations capturing semantic meaning—for tasks like semantic search, clustering, and recommendation systems. Yet, subtle mistakes can si...

Artificial Intelligence, Deep Learning

The Role of Mathematics in Becoming a Deep Learning Engineer

Posted on September 25, 2025 by Sandeep Kumar

Artificial Intelligence (AI) is no longer a futuristic experiment confined to research labs—it’s the central nervous system of modern business and society. Enterprises use it to predict consumer behavior, detect fraud, power chatbots, personalize healthcare, and even optimize supply chains. The ...

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

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...