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DEEP LEARNING#1

A subset of machine learning focused on neural networks with many layers, enabling complex data representation.

NEURAL NETWORK#2

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

CONVOLUTIONAL NEURAL NETWORK (CNN)#3

A specialized neural network architecture designed for processing structured grid data, such as images.

IMAGE RECOGNITION#4

The ability of a system to identify and classify objects within digital images using deep learning.

DATA AUGMENTATION#5

Techniques used to artificially expand training datasets by applying transformations to existing data.

HYPERPARAMETER TUNING#6

The process of optimizing the parameters that govern the training process of a machine learning model.

MODEL DEPLOYMENT#7

The process of integrating a trained machine learning model into a production environment for real-world use.

ACTIVATION FUNCTION#8

A mathematical function applied to a neural network's output to introduce non-linearity into the model.

OVERFITTING#9

A modeling error that occurs when a model learns noise in the training data instead of the actual data distribution.

UNDERFITTING#10

A scenario where a model is too simple to capture the underlying patterns in the data, resulting in poor performance.

TRANSFER LEARNING#11

A technique where a pre-trained model is fine-tuned on a new task, leveraging existing knowledge.

BATCH NORMALIZATION#12

A technique to improve training speed and stability by normalizing the output of a previous activation layer.

LOSS FUNCTION#13

A function that measures the difference between the predicted output and the actual output during model training.

OPTIMIZER#14

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

TRAINING SET#15

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

VALIDATION SET#16

A subset of data used to tune the model's hyperparameters and prevent overfitting.

TEST SET#17

A separate subset of data used to evaluate the performance of a trained model.

EPOCH#18

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

LEARNING RATE#19

A hyperparameter that controls how much to change the model in response to the estimated error each time the model weights are updated.

CNN LAYER#20

Different layers in a CNN, such as convolutional layers, pooling layers, and fully connected layers, each serving specific functions.

REAL-TIME INFERENCE#21

The capability of a model to make predictions on new data instantly as it is received.

RESTful API#22

An architectural style for designing networked applications, allowing interaction with web services.

PERFORMANCE METRICS#23

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

COMPUTER VISION#24

An interdisciplinary field that enables computers to interpret and process visual information from the world.

FRAMEWORK#25

A software library that provides tools and functions to facilitate the development of machine learning models.