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Linear Regression – Interview Question & Answers !
Linear Regression – Interview Q & A. Table Of Contents: Beginner-Level (Fundamentals) What is Linear Regression? What is the equation of a simple linear regression model? What are the assumptions of linear regression? What is the difference between simple and multiple linear regression? What do the coefficients in a linear regression model represent? How do you interpret the intercept and slope in a regression line? What is the cost function used in linear regression? What is the difference between correlation and regression? What is Mean Squared Error (MSE)? How is R-squared interpreted? What does an R-squared of 0.85 mean? What
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Deep Learning – What Is Early Stopping ?
Deep Learning – What Is Early Stopping ? Table Of Contents: What Is Early Stopping ? Why Is Early Stopping Is Needed ? How Early Stopping Works ? Benefits Of Early Stopping . Visual Representation. Hyperparameter : Patience . (1) What Is Early Stopping ? (2) Why Is Early Stopping Needed ? (3) How Early Stopping Works ? (4) Benefits of Early Stopping . (5) Visual Representation . (6) Hyperparameter: Patience (7) Implementation in Keras (TensorFlow) from tensorflow.keras.models import Sequential from tensorflow.keras.layers import Dense from tensorflow.keras.callbacks import EarlyStopping # 1. Build the model model = Sequential([ Dense(128, activation='relu', input_shape=(input_dim,)), Dense(64,
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Machine Learning – L1 & L2 Regularization.
Machine Learning – L1 & l2 Regularization Table Of Contents: What Is L1 & L2 Regularization ? How Controlling The Magnitude Of The Model’s Coefficients, Overcome Overfitting ? How Too Large Coefficients More Likely To Fit Random Noise In The Training Set ? What Is Sparsity In The Model ? How The L2 Regularization Handle The Larger Weights ? Explain With Mathematical Example How The Weights Are Getting Zero In L1 Normalization ? Why For L2 Regularization Weight Can’t Be Zero Explain With One Example ? (1) What Is L1 & L2 Regularization ? (2) How Controlling The Magnitude Of
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GenAI – How To Measure Performance Of The RAG Model ?
GenAI – How To Measure Performance Of The RAG Models ? Table Of Contents: Retrieval Metrics. Generation Metrics. End To End Evaluation. (1) Retrieval Metrics (2) Generation Metrics (3) End To End Evaluation. (4) RAG Evaluation Tools.
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GenAI – Open Source LLM Models.
GenAI – Open Sourced LLM Models. Table Of Contents: What Is Open Source LLM Models ? Characteristics Of Open Source LLM. When To Use Open Source LLM. Advantages Of Open Source LLM Models. Dis Advantages Of Open Source LLM Models. List Of Open Source LLM Models. (1) What Is Open Source LLM Models ? (2) Characteristics Of Open Source LLM Models . (3) When To Use Open Source LLM Models ? (4) When Not To Use Open Source LLM Models ? (5) Advantages Of Open Source LLM Models ? (6) Disadvantages Of Open Source LLM Models ? (7) List Of
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GenAI – Close Source LLM Models.
GenAI – Close Sourced LLM Models. Table Of Contents: What Is Close Source LLM Models ? Characteristics Of Close Source LLM. When To Use Close Source LLM. When Not To Use Close Source LLM. Advantages Of Close Source LLM Models. Dis Advantages Of Close Source LLM Models. List Of Close Source LLM Models. (1) What Is Close Source LLM Models ? (2) Characteristics Of Close Source LLM Models . (3) When To Use Close Source LLM ? (4) When Not To Use Close Source LLM ? (5) Advantages Of Close Source LLM. (6) Disadvantages Of Close Source LLM. (7) List
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GenAI – Evaluation Metrics In LLM.
GenAI – LLM Model Evaluation Metrics. Table Of Contents: Automatic Evaluation Metrics. Human Evaluation (Gold Standard). Hallucination Detection. Embedding/Similarity Based. Application-Specific Evaluation. Evaluation Frameworks. (1) Automatic Evaluation Metrics (2) Human Evaluation (Gold Standard). (3) Hallucination Detection (4) Embedding/Similarity Based (5) Application-Specific Evaluation (6) Evaluation Frameworks
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GenAI – RAG Based Fine Tuning.
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GenAI -Adapter Fusion Tuning.
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GenAI – BitFit Tuning.
