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Computer Science

Machine Learning

Activation Functions in Neural Networks

Bias-Variance Tradeoff

Computer Vision Fundamental Concepts

Cross-validation Techniques

Data Preprocessing for Machine Learning

Deep Learning Fundamentals

Dimensionality Reduction Methods

Data Imbalance Handling

Ensemble Learning Techniques

Evaluation Metrics for Classification

Feature Engineering Techniques

Generative Models

Gradient Descent Variants

Hyperparameter Tuning

Machine Learning Best Practices

Loss Functions Explained

Machine Learning Security and Privacy

Machine Learning Pipelines

Machine Learning Frameworks

Model Explainability and Interpretability

Model Selection Criteria

Multiclass Classification Strategies

Natural Language Processing Terminology

Neural Network Architectures

Performance Optimization in Machine Learning

Time Series Analysis Techniques

Reinforcement Learning Concepts

Statistical Tests for Model Comparison

Performance Optimization in Machine Learning

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Gradient Descent

A first-order iterative optimization algorithm for finding a local minimum of a differentiable function. Used in minimizing the cost function in models like linear regression.

Mini-batch Gradient Descent

A variation of gradient descent where the model is updated using a subset of the training data, which reduces the variance of the parameter updates and can lead to faster convergence.

Regularization

Techniques such as L1 (Lasso) and L2 (Ridge) regularization that penalize large weights in a model to prevent overfitting and improve generalization.

Cross-validation

A model validation technique to assess how the results of a statistical analysis will generalize to an independent data set, often used in settings where the goal is prediction.

Feature Scaling

The method of normalizing or standardizing the range of independent variables or features of data, which is important for algorithms that calculate distances.

Batch Normalization

A technique to normalize the inputs of each layer to improve the speed, performance, and stability of deep learning networks.

Early Stopping

A form of regularization where you stop training as soon as the performance on a validation set starts to degrade, preventing overfitting.

Ensemble Methods

Combining predictions from multiple machine learning models to improve predictive performance compared to a single model.

Hyperparameter Tuning

The process of finding the optimal set of hyperparameters (parameters that are not learned) for a learning algorithm, typically using methods like grid search or random search.

Dropout

A regularization technique for neural networks that involves randomly setting a fraction of input units to 0 at each update during training to prevent overfitting.

Pruning

Reducing the size of a machine learning model by removing parts that have little impact on the output, such as less important features or weights, to reduce complexity and improve speed.

Transfer Learning

The practice of reusing a pre-trained model on a new, related task or problem, where only the final layers are fine-tuned, saving on training time and resources.

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