Say Hello to Artificial Intelligence
Say Hello to Artificial Intelligence
[Introduction] Traffic signs are everywhere on our roads, ensuring safety and efficient traffic flow. But what if we could teach a computer to understand and interpret these signs, just like humans do? That’s exactly what we’re going to explore in this video. [Project Overview] In this project, we trained a deep learning model to classify …
Traffic Sign Classification with 47 Classes: A Deep Learning Journey Read More »
To classify the Titanic dataset using Python, you will need to follow these steps: Here is an example of how you could classify the Titanic dataset using Python: This code first imports the necessary libraries, then loads the data using Pandas, explores the data, preprocesses the data by dropping missing values and encoding categorical variables, …
Dimensionality reduction is a method used to reduce the number of features or dimensions in a dataset. This is often done to reduce the complexity of the data, make it easier to visualize, or to improve the performance of machine learning algorithms. There are many different techniques for dimensionality reduction, including: Dimensionality reduction can be …
Some common deep learning algorithms include convolutional neural networks (CNNs), recurrent neural networks (RNNs), and long short-term memory (LSTM) networks. These algorithms are all types of neural networks that are designed to work with large amounts of data and to automatically learn from that data. CNNs are often used for image recognition tasks, RNNs are …
Difference between Artificial intelligence, Data Science, Machine Learning, and Deep Learning Artificial intelligence: Artificial intelligence (AI) refers to the simulation of human intelligence in machines that are programmed to think and act like humans. These machines are trained to perform tasks that typically require human intelligence, such as visual perception, speech recognition, decision-making, and language …
Morphological operations are a set of image processing operations that are performed on binary images. These operations are based on the shape of the objects in the image, and are used to extract structural information from the image. Some common morphological operations include dilation, erosion, opening, closing, and skeletonization. These operations can be used to …
A Generative Adversarial Network (GAN) is a type of machine learning model that is composed of two parts: a generator and a discriminator. The generator model generates fake data, such as fake images, while the discriminator model attempts to classify the fake data as real or fake. The generator and discriminator models are trained together …
Image classification is the process of assigning a label or category to an image based on its visual content. It is a common task in computer vision, and there are many algorithms and models that can be used to perform image classification. Convolutional Neural Networks (CNNs) are a type of deep learning model that are …
A convolutional neural network (CNN) is a type of deep learning model that is commonly used for image classification and other tasks that involve analyzing visual data. CNNs are composed of multiple layers, including convolutional layers, pooling layers, and fully connected layers. The convolutional layers of a CNN are responsible for extracting features from the …
As I mentioned in my previous blog, face mask detection uses deep learning techniques. In this blog, you will learn how to accurately detect the face mask and classify it into two categories (face mask and no face mask). For this purpose, first, we trained the deep learning CNN model (MOBILE NET) using the facemask …