The process of identifying and categorizing objects within an image using machine learning algorithms.

A subset of artificial intelligence that enables systems to learn from data and improve over time without explicit programming.

A field of study that enables computers to interpret and understand visual information from the world.

A popular programming language widely used for data science, machine learning, and image processing.

An open-source machine learning framework developed by Google for building and training neural networks.

A widely used dataset containing 60,000 32x32 color images in 10 different classes for training image classification models.

Techniques used to artificially expand the size of a training dataset by creating modified versions of images.

The process of scaling image pixel values to a common range, improving model training stability.

The design and structure of a machine learning model, including its layers and connections.

A mathematical function that measures how well a model's predictions match the actual outcomes.

An algorithm that adjusts model parameters to minimize the loss function during training.

A subset of data used to train a machine learning model, allowing it to learn patterns.

A subset of data used to tune model parameters and prevent overfitting during training.

A table used to evaluate the performance of a classification model by comparing predicted and actual labels.

Quantitative measures used to assess the performance of a machine learning model, such as accuracy and F1 score.

A technique where a pre-trained model is fine-tuned on a new task to improve learning efficiency.

Techniques that combine multiple models to improve overall prediction accuracy.

Settings that govern the training process and model structure, requiring tuning for optimal performance.

A modeling error where a model learns noise in the training data, leading to poor performance on unseen data.

A modeling error where a model is too simple to capture the underlying patterns in the data.

An interactive coding environment that allows for live code execution, visualization, and documentation.

A self-contained directory that allows you to install packages and dependencies for a specific project without conflicts.

The graphical representation of data to identify patterns, trends, and insights.

Comprehensive written descriptions of code, processes, and methodologies to facilitate understanding and reproducibility.