Deep Learning - Nomidl

Denoising Images with Autoencoders Using TensorFlow and Python

In today’s digital world, images play an important role in various applications, from medical imaging to self-driving cars. However, images are often corrupted by noise during transmission or storage, which can hinder the performance of image processing algorithms. In this blog post, we will explore how to use autoencoders to denoise images. We will implement…

10 Underrated AI Tools That Will Change Your Life and Business

In this blog post, we will explore ten underrated and less-known AI tools that have the potential to revolutionize your life and business. These tools cover a wide range of functionalities, from creating customized QR codes to competitor research, photo editing, podcast note-taking, meal planning, essay writing, video summarization, lead magnet generation, article summarization, and…

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…

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…

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…

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…

Alexnet Architecture Explained | Introduction to Alexnet Architecture

In the field of artificial intelligence, image recognition has always been a challenging problem. Until the mid-2010s, traditional methods struggled to achieve the accuracy and efficiency needed for large-scale image classification tasks. However, in 2012, a breakthrough changed the game forever. AlexNet, a deep learning architecture that changed the field of computer vision. AlexNet AlexNet…

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,…

Understanding the Softmax Activation Function: A Detailed Explanation

The Softmax activation function is one of the most important activation function in artificial neural networks. Its primary purpose is to transform a vector of real numbers into a probability distribution, enabling us to make informed decisions based on the output probabilities. In this article, we will figure out the workings of the Softmax activation…

Day 5: Everything you need to know about Activation Functions in Deep learning

Deep learning is a powerful area of artificial intelligence that has received a lot of attention in recent years. One of the main components of deep learning models is the activation function. Activation functions play a crucial role in determining the output of a neural network. In this article, we will dive deep into understanding…