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A
Attribute
B
Bias-variance Tradeoff
Binary Classification
C
Categorical Variables
CI/CD for Machine Learning
Classification Threshold
Clustering in Machine Learning
Confusion Matrix in Machine Learning
Continuous Validation
D
Data Augmentation
Data Cleaning
Deep Learning
E
Epoch in Machine Learning
F
False Positive Rate
Feature Vector
I
Independent and Identically Distributed Data (IDD)
L
Learning Rate in Machine Learning
Long Short-Term Memory (LSTM)
M
Machine Learning
Machine Learning Algorithm
Machine Learning Bias
Machine Learning Model Accuracy
MLOps
Model Monitoring
Model Parameters
Multi-class Classification
N
Neural Networks
Noise in Machine Learning
Normalization in Machine Learning
O
One-hot Encoding
Out-of-distribution
Overfitting in Machine Learning
P
Precision in Machine Learning
R
Random Initialization
Recall in Machine Learning
Rectified Linear Unit (ReLU)
Recurrent Neural Network
Regression
Regularization in Machine Learning
Reinforcement Learning
ResNet
ROC (Receiver Operating Characteristic) Curve
S
Segmentation in Machine Learning
Semi-supervised Learning
Softmax Function
Supervised Learning
T
Test Set in Machine Learning
Top-1 error rate
Triplet Loss
True Positive Rate
Type 1 Error
Type 2 Error
U
Underfitting in Machine Learning
V
Validation Set in Machine Learning
VGGNet
Y
YOLO (object detection algorithm)
Z
Zero-shot learning
Why Deepchecks?
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