What developers Need to Know About Generative AI?

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Linear regression predicts numeric values using supervised learning. It's a good starting point but should not be the final solution.

Linear Regression  

Gradient descent optimizes machine learning models, including neural networks. It helps avoid local minima and adapts learning rates.

Gradient Descent  

Logistic regression solves categorical classification problems. It uses linear regression within a sigmoid function to provide probabilities.

Logistic Regression  

SVMs classify data by finding the best separating hyperplane. They handle both simple and complex classification tasks.

Support Vector Machines (SVM)  

Decision trees predict target variables by learning decision rules from data features. They're easy to interpret but prone to overfitting.

Decision Trees  

Random forest combines multiple decision trees to improve accuracy. It's used for classification and regression by aggregating tree results.

Random Forest  

XGBoost enhances decision trees through gradient boosting. It optimizes trees iteratively, building on residuals for better performance.


K-means clustering divides data into clusters by minimizing variance. It’s an unsupervised method useful for feature learning.

K-means Clustering  

Transformers use attention mechanisms for better language understanding. They power advanced models like GPT and BERT in generative AI.