Enhance Your Understanding with Neural Networks Programming Concept Cards for quick learning
A computational model inspired by the structure and function of the human brain, consisting of interconnected nodes (neurons) that process and transmit information.
A mathematical function applied to the output of a neuron, determining its level of activation and introducing non-linearity to the neural network.
A type of neural network where information flows in one direction, from the input layer through hidden layers to the output layer, without cycles or loops.
A supervised learning algorithm used to train neural networks by adjusting the weights and biases based on the difference between predicted and actual outputs.
A specialized type of neural network designed for processing structured grid-like data, commonly used in image and video recognition tasks.
A type of neural network that can process sequential data by introducing feedback connections, allowing information to persist over time.
A type of neural network consisting of a generator and a discriminator, trained together in a competitive setting to generate realistic data.
A subfield of machine learning that focuses on training neural networks with multiple hidden layers, enabling them to learn hierarchical representations.
The structure and organization of a neural network, including the number of layers, the number of neurons in each layer, and the connections between them.
Methods used to improve the performance and efficiency of neural networks, such as gradient descent, stochastic gradient descent, and adaptive learning rate algorithms.
Techniques used to prevent overfitting in neural networks, such as L1 and L2 regularization, dropout, and early stopping.
The process of finding the optimal values for hyperparameters, such as learning rate, batch size, and number of hidden units, to improve the performance of a neural network.
The process of feeding input data to a neural network, adjusting the weights and biases during training, and evaluating the performance on unseen test data.
Measures used to assess the performance of a neural network, such as accuracy, precision, recall, F1 score, and area under the receiver operating characteristic curve (AUC-ROC).
Neural networks are used in various fields, including image and speech recognition, natural language processing, recommendation systems, and autonomous vehicles.
An optimization algorithm used to minimize the loss function of a neural network by iteratively adjusting the weights and biases in the direction of steepest descent.
A variant of gradient descent that randomly selects a subset of training examples (mini-batch) to estimate the gradient, reducing computational requirements.
A function that measures the difference between predicted and actual outputs, used to guide the training of a neural network by minimizing the error.
A regularization technique used to prevent overfitting in neural networks by randomly disabling a fraction of neurons during training.
A complete pass through the entire training dataset during the training of a neural network.
A hyperparameter that determines the step size at each iteration of the optimization algorithm, influencing the speed and convergence of training.
The number of training examples processed in one iteration of the optimization algorithm, affecting the trade-off between computational efficiency and convergence speed.
A phenomenon where a neural network performs well on the training data but fails to generalize to unseen test data, indicating excessive memorization of training examples.
A phenomenon where a neural network fails to capture the underlying patterns in the training data, resulting in poor performance on both training and test data.
Rectified Linear Unit, an activation function commonly used in neural networks that outputs the input if it is positive, and zero otherwise.
An activation function that maps the input to a value between 0 and 1, commonly used in the output layer of a binary classification neural network.
An activation function that normalizes the outputs of a neural network into a probability distribution over multiple classes, commonly used in multi-class classification tasks.
A regularization technique that adds a penalty term to the loss function based on the absolute values of the weights, promoting sparsity and feature selection.
A regularization technique that adds a penalty term to the loss function based on the squared values of the weights, encouraging smaller weights and reducing overfitting.
A technique used to prevent overfitting by monitoring the performance on a validation set during training and stopping the training when the performance starts to deteriorate.
A loss function commonly used in classification tasks that measures the dissimilarity between predicted and actual probability distributions.
A loss function commonly used in regression tasks that measures the average squared difference between predicted and actual values.
A metric that measures the proportion of true positive predictions out of all positive predictions, indicating the model's ability to avoid false positives.
A metric that measures the proportion of true positive predictions out of all actual positive instances, indicating the model's ability to avoid false negatives.
A metric that combines precision and recall into a single value, providing a balanced measure of a model's performance.
A metric that measures the performance of a binary classification model by plotting the true positive rate against the false positive rate at various thresholds.
The task of identifying and classifying objects or patterns in digital images using neural networks, commonly used in applications like facial recognition and object detection.
The task of converting spoken language into written text using neural networks, enabling applications like voice assistants and transcription services.
The field of study that focuses on the interaction between computers and human language, using neural networks for tasks like sentiment analysis and machine translation.
Systems that suggest items or content to users based on their preferences and behavior, utilizing neural networks to learn patterns and make personalized recommendations.
Vehicles capable of operating without human intervention, relying on neural networks for tasks like object detection, lane keeping, and decision making.
A computational model inspired by the structure and function of biological neural networks, used to solve complex problems by learning from data.
A neural network with multiple hidden layers, enabling it to learn hierarchical representations of data and solve more complex tasks.
A type of machine learning where an agent learns to interact with an environment and maximize rewards through trial and error, often using neural networks as function approximators.
A type of machine learning where the model learns patterns and structures in data without explicit labels, often used for tasks like clustering and dimensionality reduction.
A technique where a pre-trained neural network is used as a starting point for a new task, leveraging the learned features and weights to improve performance with limited data.
A type of recurrent neural network that addresses the vanishing gradient problem and can learn long-term dependencies, commonly used in tasks like speech recognition and language modeling.
A type of recurrent neural network that simplifies the architecture of LSTM by combining the forget and input gates into a single update gate, reducing computational requirements.
A type of neural network used for unsupervised learning and dimensionality reduction, consisting of an encoder and a decoder that reconstructs the input data.
A type of autoencoder that learns a latent representation of the input data and generates new samples by sampling from the learned distribution, enabling tasks like image generation and data synthesis.
A layer in a convolutional neural network that applies a set of learnable filters to the input data, capturing local patterns and spatial hierarchies.
A layer in a convolutional neural network that reduces the spatial dimensions of the input data, preserving the most important features and improving computational efficiency.
The region of the input data that a neuron in a convolutional neural network is sensitive to, determined by the size of the filters and the number of pooling layers.
A technique used to artificially increase the size of the training dataset by applying random transformations to the input data, reducing overfitting and improving generalization.
The component of a generative adversarial network that learns to generate realistic data samples, often using a deconvolutional neural network architecture.
The component of a generative adversarial network that learns to distinguish between real and generated data samples, often using a convolutional neural network architecture.
A combination of deep learning and reinforcement learning, where a neural network learns to make decisions by interacting with an environment and receiving rewards.
A model-free reinforcement learning algorithm that learns an action-value function (Q-function) by iteratively updating the Q-values based on the observed rewards and next states.