A subset of machine learning using neural networks with multiple layers to analyze data representations.

A computational model inspired by the human brain, consisting of interconnected nodes (neurons) that process data.

The task of assigning labels to images based on their content, often using deep learning techniques.

An open-source deep learning library developed by Google, widely used for building and training neural networks.

An open-source machine learning library developed by Facebook, known for its flexibility and ease of use in deep learning.

A mathematical function applied to a neuron's output, introducing non-linearity to the model.

A measure of how well a neural network's predictions match the actual outcomes, guiding the training process.

A modeling error that occurs when a neural network learns the training data too well, failing to generalize to new data.

Techniques used to artificially expand a training dataset by creating modified versions of existing data.

A table used to evaluate the performance of a classification model, showing true vs. predicted classifications.

The process of optimizing model parameters that are not learned during training to improve performance.

A subset of data used to train a model, helping it learn patterns and make predictions.

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

A separate subset of data used to evaluate the final model's performance after training.

A basic unit of a neural network that receives input, processes it, and produces output.

A collection of neurons in a neural network, where each layer processes the input data in stages.

An algorithm used to adjust the weights of a neural network to minimize the loss function during training.

One complete pass through the entire training dataset during the training process.

The number of training examples utilized in one iteration of model training.

Techniques used to prevent overfitting by adding constraints to the model during training.

A method where a pre-trained model is fine-tuned on a new task, saving time and resources.

The process of scaling input data to improve the training speed and performance of a neural network.

A widely used dataset in image classification tasks, containing 60,000 32x32 color images in 10 classes.

Quantitative measures used to evaluate the effectiveness of a model, such as accuracy and loss.