Deep Learning Fundamentals

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Questions and Answers

What is the primary goal of named entity recognition (NER) in NLP?

To generate human-like responses in chatbots

To classify documents into categories

To identify the sentiment of a text

To extract entities such as names, dates, and locations from text (correct)

Which technique is commonly used for text classification tasks in NLP?

Singular Value Decomposition (SVD)

Support Vector Machines (SVM) (correct)

K-Means Clustering

Word Embeddings

What is the primary purpose of stop words removal in text preprocessing for NLP tasks?

To reduce the dimensionality of the data

To identify the syntactic structure of the text

To improve the readability of the text

To eliminate common words that do not carry significant meaning (correct)

Which algorithm is a supervised learning method for regression tasks?

Random Forest Signup and view all the answers

Which technique is used to handle missing values in a dataset before training a machine learning model?

Imputation Signup and view all the answers

Which evaluation metric is typically used for binary classification problems when the classes are imbalanced?

F1-score Signup and view all the answers

Which AI technique is used to model the uncertainty in decision-making processes under incomplete or uncertain information?

Bayesian Inference Signup and view all the answers

Which NLP technique is used to convert words into dense vector representations while preserving semantic relationships?

Word Embeddings Signup and view all the answers

What is the primary purpose of dropout regularization in deep learning models?

To prevent overfitting Signup and view all the answers

What is the primary goal of unsupervised learning in machine learning?

To discover hidden patterns or structures in data Signup and view all the answers

Which type of neural network architecture is specifically designed for processing sequential data?

Recurrent Neural Network (RNN) Signup and view all the answers

What is the primary purpose of reinforcement learning in artificial intelligence?

To learn from feedback and rewards Signup and view all the answers

Which branch of AI focuses on creating systems that can simulate human-like intelligence to perform tasks?

Robotics Signup and view all the answers

What is the term 'bias' referring to in machine learning?

The tendency of a model to consistently underpredict or overpredict Signup and view all the answers

Which of the following is an example of a symbolic AI technique used to represent knowledge using rules and logic?

Rule-Based Systems Signup and view all the answers

Which of the following activation functions is commonly used in deep learning models to introduce non-linearity?

ReLU Signup and view all the answers

Study Notes

Activation Functions

ReLU (Rectified Linear Unit) is commonly used in deep learning models to introduce non-linearity.
Sigmoid and Tanh are also activation functions used in deep learning.

Dropout Regularization

The primary purpose of dropout regularization in deep learning models is to prevent overfitting.

Neural Network Architectures

Recurrent Neural Network (RNN) is specifically designed for processing sequential data, such as time series or text.

Unsupervised Learning

The primary goal of unsupervised learning in machine learning is to discover hidden patterns or structures in data.

Model Evaluation

Cross-validation is used to evaluate the performance of a machine learning model on unseen data.

Bias in Machine Learning

In machine learning, bias refers to the tendency of a model to consistently underpredict or overpredict.

Artificial Intelligence (AI)

The branch of AI that focuses on creating systems that can simulate human-like intelligence to perform tasks is known as Expert Systems.
Reinforcement learning in AI is used to learn from feedback and rewards.

Natural Language Processing (NLP)

The primary goal of named entity recognition (NER) in NLP is to extract entities such as names, dates, and locations from text.
Support Vector Machines (SVM) is a technique commonly used for text classification tasks in NLP.
Stop words removal is used in text preprocessing to eliminate common words that do not carry significant meaning.

Text Classification

Word Embeddings is a technique used to convert words into dense vector representations while preserving semantic relationships.

Handling Missing Values

Imputation is a technique used to handle missing values in a dataset before training a machine learning model.

Evaluation Metrics

F1-score is an evaluation metric typically used for binary classification problems when the classes are imbalanced.

Uncertainty in Decision-Making

Bayesian Inference is an AI technique used to model the uncertainty in decision-making processes under incomplete or uncertain information.

Text Preprocessing

Stemming is used in text preprocessing to reduce words to their root form.

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Description

Test your understanding of key concepts in deep learning, including activation functions, dropout regularization, and neural network architectures.