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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.