Category: Deep Learning
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…
Read MoreAlexnet 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…
Read MoreSigmoid 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,…
Read MoreUnderstanding 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…
Read MoreDay 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…
Read MoreThe Differences between Sigmoid and Softmax Activation function?
In the field of neural networks, activation functions play an important role in transforming linear output into nonlinear, allowing models to learn complex patterns efficiently. Two commonly used activation functions are the Sigmoid and Softmax functions. In this article, we will be looking at the differences between these two activation functions and their respective use…
Read MoreDay 4: Exploring the Fundamentals of Artificial Neural Networks
Artificial neural networks (ANN) have revolutionized the field of artificial intelligence, enabling computers to perform complex tasks such as image recognition, natural language processing, and speech synthesis. ANNs are designed to mimic the structure and function of the human brain, allowing machines to learn from data and make intelligent decisions. In this article, we will…
Read MoreFrom Zero to Hero: The Ultimate PyTorch Tutorial for Machine Learning Enthusiasts
Machine learning has revolutionized a number of industries, from healthcare to finance, by allowing computers to learn from data and make intelligent predictions. PyTorch, a popular open source machine learning framework, has gained significant traction among researchers and practitioners due to its flexibility and ease of use. In this comprehensive tutorial, we’ll take you from beginner…
Read MoreDay 3: Deep Learning vs. Machine Learning: Key Differences Explained
In the world of artificial intelligence, two terms often mentioned are “deep learning” and “machine learning.” Both technologies play significant roles in the development of intelligent systems, but what sets them apart? In this article, we will delve into the key differences between deep learning and machine learning, exploring their applications, methodologies, and unique characteristics.…
Read MoreDay 2: 14 Types of Neural Networks and their Applications
1 – Introduction to Neural Networks Artificial intelligence has changed drastically because of neural networks, which are now an important part of various applications. They can be used to solve complex problems and make predictions by mimicking the structure and functioning of the human brain. So, in this article, we will talk about different types…
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