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Artificial Intelligence / Machine Learning

All the posts related to ai / ml learning

Enhancing Image Classification Performance with Data Augmentation Techniques

Introduction: Data augmentation has emerged as a powerful technique in image classification tasks, addressing the challenges of limited training data and overfitting. By generating augmented images, data augmentation increases the diversity and size of the training dataset, leading to improved generalization and performance of image classification models. In simpler terms,

Federated Learning:Empowering AI while Safeguarding Privacy

Introduction: In the era of big data and artificial intelligence (AI), concerns about data privacy and security have grown significantly. Federated Learning has emerged as a ground-breaking solution to address these concerns while unlocking the potential of AI. By enabling model training across distributed devices without sharing raw data, federated

Layer-wise Relevance Propagation (LRP)

Introduction: Layer-wise Relevance Propagation (LRP) is a model explainability technique used to interpret the predictions made by deep learning models. It provides insights into why a model makes certain decisions by attributing relevance scores to individual input features. LRP is particularly useful for understanding complex models such as neural networks,

Reinforcement Learning: Unleashing the Power of Learning through Interaction

Introduction: Reinforcement Learning (RL) is a subfield of machine learning that enables agents to learn optimal decision-making strategies through interaction with an environment. Unlike supervised learning, where the algorithm is trained on labelled data, or unsupervised learning, where the model extracts patterns from unlabelled data, RL revolves around a dynamic

Leveraging Transfer Learning: Boosting Image Classification with Pre-trained Models

Introduction: Image classification is the task of categorizing images into predefined classes based on their visual features. It involves training a model to recognize and assign class labels to images, enabling applications like object recognition and facial recognition, but training deep learning models from scratch can be time-consuming and computationally

Understanding the WGAN: A Breakthrough in Generative Adversarial Networks

Introduction Generative Adversarial Networks (GANs) have revolutionized the field of deep learning by enabling the creation of realistic and high-quality synthetic data. While GANs have demonstrated impressive results, they suffer from certain limitations, such as unstable training and mode collapse. In 2017, a new variant called Wasserstein GAN (WGAN) was

KNN Regressor and its fine-tuning

KNN ( K nearest neighbours) is a popular machine-learning model that is non-parametric and can be used for both categorical and continuous numerical data. KNN Classifier is used for categorical data while KNN Regressor is used for continuous data that predicts the numerical target based on similarity measure. It takes the