Deep Learning Fundamentals

16 Questions

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

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

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

Support Vector Machines (SVM)

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

To eliminate common words that do not carry significant meaning

Which algorithm is a supervised learning method for regression tasks?

Random Forest

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

Imputation

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

F1-score

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

Bayesian Inference

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

Word Embeddings

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

To prevent overfitting

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

To discover hidden patterns or structures in data

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

Recurrent Neural Network (RNN)

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

To learn from feedback and rewards

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

Robotics

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

The tendency of a model to consistently underpredict or overpredict

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

Rule-Based Systems

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

ReLU

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.

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

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