deep learning - Nomidl

Naveen Pandey
April 20, 2025November 8, 2025

Fine-Tuning BERT for 90%+ Accuracy in Text Classification

What are Pretrained Language Models? Pretrained Language Models (PLMs) are deep learning models trained on large corpus of text to understand the structure and nuances of natural language. These models are used as a foundation for various Natural Language Processing (NLP) tasks, including Fine tuning BERT for text classification, significantly improving performance compared to training from…

Rajesh Yerremshetty
April 13, 2025November 8, 2025

Mastering Reinforcement Learning: From Basics to Cutting-Edge Techniques

Topics in Reinforcement Learning (RL) explore how agents make their moves within environments to obtain the highest combined rewarding outcomes. The learning process of RL operates autonomously through environment interactions because it abstains from relying on labelled data to obtain rewards and punishments for feedback. Mastering the core principles of RL is essential for developing…

Naveen Pandey
November 25, 2023November 11, 2025

How to Become a Prompt Engineer in 2024

In this article, we will explore the role of prompt engineers in the field of AI technology. Prompt engineers play a crucial role in enabling AI models to generate relevant output based on user input. We will discuss the basics of prompt engineering, its importance, and the steps and skills required to become a proficient…

Naveen Pandey
November 2, 2023December 12, 2024

How to Create an Image Classification Model using Hugging Face in 5 Lines

In 2021, when it comes to natural language processing tasks, most people turn to Hugging Face for solutions. However, did you know that Hugging Face now also offers image-related solutions? Yes, you heard it right! The popular Transformers library can now help you classify images as well. In this blog post, I will show you…

Naveen Pandey
October 27, 2023December 12, 2024

Building a Classification Model with VGG19 for Image Recognition

Image classification is a fascinating field of machine learning that involves teaching a computer to recognize and categorize objects or patterns within images. In this article, we will walk through the process of building a classification model using the VGG19 architecture for image recognition. We’ll start from importing the necessary libraries and proceed step by…

Naveen Pandey
October 14, 2023December 12, 2024

Generating new MNIST digits in PyTorch using AutoEncoder

Are you curious about how machines can learn to understand and generate images? In this blog post, we’re going to delve into the fascinating world of Autoencoders using PyTorch, and we’ll explain this concept in simple terms. Autoencoders are a type of artificial neural network that can compress data and then reconstruct it. This article…

Naveen Pandey
September 19, 2023December 12, 2024

Backpropagation in Neural Networks with an Examples

In this article, we will talk about the concept of backpropagation, which can be considered the building block of a neural network. After reading this article, you will understand why backpropagation is important and why it is applied in various fields. What is Back Propagation? Back propagation is an algorithm created to test errors that…

Naveen Pandey
September 8, 2023December 12, 2024

Day 7 – What Are GANs? | Generative Adversarial Networks in Deep Learning

In this article, we will explore an important and popular deep learning neural network called Generative Adversarial Networks (GANs). GANs were introduced in 2014 by Ian J. Goodfellow and co-authors and have since become very popular in the field of machine learning. GANs are an unsupervised learning task that consists of two models, the generator…

Naveen Pandey
September 2, 2023December 12, 2024

Day 6 – What is Loss Function in Deep Learning | Loss Function in Machine Learning | Loss Function Types

In this blog, we will cover the concept of a loss function and its significance in artificial neural networks. Loss functions play a crucial role in model training, as they are used by stochastic gradient descent to minimize the error during the training process. We will discuss how loss functions are calculated and their importance…

Naveen Pandey
August 18, 2023December 12, 2024

LeNet-5 Architecture Explained | Introduction to LeNet-5 Architecture

LeNet-5 is a compact neural network comprising fundamental components of deep learning convolutional layers, pooling layers, and fully connected layers. It serves as a foundational model for other deep learning architectures. Let’s talk about the LeNet-5 and enhance our understanding of convolutional and pooling layers through practical examples. Introduction to LeNet-5 LeNet-5 consists of seven…

Naveen Pandey
August 8, 2023December 15, 2024

Alexnet Architecture Explained | Introduction to Alexnet Architecture

The introduction of AlexNet in 2012 has changed the image recognition field. Thousands of researchers and entrepreneurs were able to approach artificial intelligence in a different manner by using this deep neural network that Alex Krizhevsky, Ilya Sutskever and Geoffrey Hinton created together. Considering how strictly quantitative and limited computer vision technology was: barely classifying…

Naveen Pandey
July 25, 2023December 12, 2024

Sigmoid Activation Function in Detail Explained

When it comes to artificial neural networks, the Sigmoid activation function is a real superstar! It might sound like a fancy term, but don’t worry; we’re going to break it down in a way that even your grandma would understand. What’s the Buzz About Activation Functions? Before we zoom in on the Sigmoid activation function,…