When regular Q-learning takes too much time to converge in a high-dimensional state space (e.g., autonomous vehicle parking), what modification could help it learn faster?
- Deep Q-Networks (DQNs)
- Policy Gradient Methods
- Fitted Q-Iteration (FQI)
- Temporal Difference (TD) Learning
Using Deep Q-Networks (DQNs) is a modification of Q-learning, which employs neural networks to handle high-dimensional state spaces efficiently. DQNs can approximate the Q-values, expediting learning in complex environments.
Techniques like backward elimination, forward selection, and recursive feature elimination are used for ________ in machine learning.
- Cross-Validation
- Data Preprocessing
- Feature Selection
- Model Training
Techniques like backward elimination, forward selection, and recursive feature elimination are used for feature selection in machine learning. Feature selection helps identify the most relevant features for building accurate models and can improve model efficiency.
Which tool or technique is often used to make complex machine learning models more understandable for humans?
- Explainable AI (XAI)
- Reinforcement Learning
- Principal Component Analysis
- Gradient Boosting
Explainable AI (XAI) techniques are employed to simplify complex machine learning models, making them interpretable, and providing insights into model decisions.
One of the applications of NLP in healthcare is to assist in ________, which involves the conversion of voice-recorded notes into text format.
- Transcription
- Speech Recognition
- Note Parsing
- Audio Conversion
NLP (Natural Language Processing) can be employed for transcription tasks in healthcare, where voice-recorded notes from medical professionals are converted into text format. This conversion makes the notes more accessible and searchable for medical records.
Which technique involves setting a fraction of input units to 0 at each update during training time, which helps to prevent overfitting?
- Dropout
- Batch Normalization
- Data Augmentation
- Early Stopping
Dropout involves setting a fraction of input units to 0 during training, which helps prevent overfitting by making the model more robust and reducing reliance on specific neurons.
A finance company wants to analyze sequences of stock prices to predict future market movements. Given the long sequences of data, which RNN variant would be more suited to capture potential long-term dependencies in the data?
- Simple RNN
- Bidirectional RNN
- Gated Recurrent Unit (GRU)
- Long Short-Term Memory (LSTM)
A Long Short-Term Memory (LSTM) is a suitable choice for capturing long-term dependencies in stock price sequences. LSTM's memory cell and gating mechanisms make it capable of handling long sequences and understanding potential trends in financial data.
How does ICA differ from Principal Component Analysis (PCA) in terms of data independence?
- ICA finds statistically independent components
- PCA finds orthogonal components
- ICA finds the most significant features
- PCA reduces dimensionality
Independent Component Analysis (ICA) seeks statistically independent components, meaning they are as unrelated as possible, while PCA seeks orthogonal components that explain the most variance but are not necessarily independent. ICA focuses on data independence, making it suitable for source separation tasks.
In reinforcement learning, what term describes the dilemma of choosing between trying out new actions and sticking with known actions that work?
- Exploration-Exploitation Dilemma
- Action Selection Dilemma
- Reinforcement Dilemma
- Policy Dilemma
The Exploration-Exploitation Dilemma is the challenge of balancing exploration (trying new actions) with exploitation (using known actions). It's crucial in RL for optimal decision-making.
How do the generator and discriminator components of a GAN interact during training?
- The generator produces real data.
- The discriminator generates fake data.
- The generator tries to fool the discriminator.
- The discriminator generates real data.
In a GAN (Generative Adversarial Network), the generator creates fake data to deceive the discriminator, which aims to distinguish between real and fake data. This adversarial process improves the quality of the generated data.
What is the primary objective of Generative Adversarial Networks (GANs)?
- Data Classification
- Data Generation
- Data Storage
- Data Analysis
The primary objective of GANs is data generation. GANs consist of a generator that creates data samples to closely resemble real data, aiding in tasks like image generation.