Found 377 Articles for Artificial Intelligence

In just a few short years, Artificial Intelligence (AI) technology has been widely used. One of the most revolutionary innovations of the information age is artificial intelligence. At the current rate of growth, it won't be long until AI is the main attraction in hundreds of companies and organizations throughout the world. In the future, the main consulting and research organizations predict a massive economic effect of multiple trillion dollars. Future economic growth rates are expected to treble, thanks to this new technology. Artificial intelligence, or AI, is a subfield of computer science concerned with simulating human intelligence. Simply said, ... Read More

Once thought of as science fiction or a technology of the future, artificial intelligence (AI) is now a reality. What was formerly reserved for academic inquiry has broken out into the open. Artificial intelligence (AI) is already present in nearly every aspect of modern life, from the workplace to the classroom to the bank to the hospital to your mobile phone. These are the minds behind the sensors in self-driving vehicles, the personalities of virtual assistants like Siri and Alexa, the experts who predict the weather and perform robotic surgery, and so much more. These days, AI can be found ... Read More

Introduction Two well-liked regularization methods for linear regression models are ridge and lasso regression. They help to solve the overfitting issue, which arises when a model is overly complicated and fits the training data too well, leading to worse performance on fresh data. Ridge regression reduces the size of the coefficients and prevents overfitting by introducing a penalty element to the cost function of linear regression. The squared coefficient total is directly proportional to this penalty component. Adversely, a penalty term is added in lasso regression that is proportionate to the total of the absolute values of the coefficients. This ... Read More

Introduction Based on Bayes' theorem, the naive Bayes algorithm is a probabilistic classification technique. It is predicated on the idea that a feature's presence in a class is unrelated to the presence of other features. Applications for this technique include text categorization, sentiment analysis, spam filtering, and picture recognition, among many others. A key concept in probability theory, the Bayes theorem provides a method for calculating the likelihood of an event given the chance of related events. Conditional probability, or the possibility of an event happening in the presence of another occurrence, serves as the theoretical foundation. Prior, likelihood and ... Read More

In neural network models, the learning rate is a crucial hyperparameter that regulates the magnitude of weight updates applied during training. It is crucial in influencing the rate of convergence and the caliber of a model's answer. To make sure the model is learning properly without overshooting or converging too slowly, an adequate learning rate must be chosen. The notion of learning rate in neural networks, its significance, and numerous methods to choose an optimal learning rate will all be covered in this article. We will also go through how to identify and resolve typical learning rate issues that develop ... Read More

Introduction The categorization of images and the identification of objects are two computer vision tasks that frequently employ convolutional neural networks (CNNs). Yet, it can be difficult to train a CNN model, particularly if the validation accuracy approaches a plateau and stays that way for a long time. Several factors, including insufficient training data, poor hyperparameter tuning, model complexity, and overfitting, might contribute to this problem. In this post, we'll talk about a few tried-and-true methods for improving constant validation accuracy in CNN training. These methods involve data augmentation, learning rate adjustment, batch size tuning, regularization, optimizer selection, initialization, and ... Read More

Introduction Due to the non-linearity that can introduce towards the output of neurons, activation functions are essential to the functioning of neural networks. Sigmoid and tanh are two of the most often employed activation functions in neural networks. Binary classification issues frequently employ the sigmoid function in the output layer to transfer input values to a range between 0 and 1. In the deep layers of neural networks, the tanh function, which translates input values to a range between -1 and 1, is frequently applied. The usage of either function relies on the particular needs of the issue being handled ... Read More

Introduction There has been a lot of interest in creating efficient machine-learning models for picture categorization due to its growing significance in several industries, including security, autonomous driving, and healthcare. Artificial neural networks (ANNs) and convolutional neural networks (CNNs) are two common models for classifying images. While both CNNs and ANNs can perform image classification tasks with high accuracy, their architectural designs and learning methods vary. ANN vs CNN Identifying the elements or objects in a picture is the process of image classification. It is a key job in computer vision, having uses in anything from autonomous vehicles to ... Read More

Optimization algorithms are frequently used in machine learning models to identify the best collection of parameters that minimize a particular cost function. Momentum is a common optimization technique that is frequently utilized in machine learning. Momentum is a strategy for accelerating the convergence of the optimization process by including a momentum element in the update rule. This momentum factor assists the optimizer in continuing to go in the same direction even if the gradient changes direction or becomes zero. This can aid in improving convergence speed, reducing oscillations, avoiding becoming trapped in local minima, and making the optimization process more ... Read More

Introduction Federated machine learning allows machine learning models to be trained across various dispersed devices without requiring data to be sent to a central server. The weight transmission protocol is a critical component of federated machine learning since it is in charge of communicating model weights between client devices and the central server throughout the training process. In this article, we look at the significance of weight transmission protocols in machine learning and explain essential approaches like differential privacy, secure aggregation, and compression that are used to assure privacy, security, and efficiency in model weight transfer. We also discuss the ... Read More