To prevent overfitting in neural networks, the _______ technique can be used, which involves dropping out random neurons during training.
- Normalization
- L1 Regularization
- Dropout
- Batch Normalization
The technique used to prevent overfitting in neural networks is called "Dropout." During training, dropout randomly removes a fraction of neurons, helping to prevent overreliance on specific neurons and improving generalization.
In time series analysis, what is a sequence of data points measured at successive points in time called?
- Time steps
- Data snapshots
- Data vectors
- Time series data
In time series analysis, a sequence of data points measured at successive points in time is called "time series data." This data structure is used to analyze and forecast trends, patterns, and dependencies over time. It's fundamental in fields like finance, economics, and climate science.
In the context of neural networks, what does the term "backpropagation" refer to?
- Training a model using historical data
- Forward pass computation
- Adjusting the learning rate
- Updating model weights
"Backpropagation" in neural networks refers to the process of updating the model's weights based on the computed errors during the forward pass. It's a key step in training neural networks and involves minimizing the loss function.
You're building a system that needs to store vast amounts of unstructured data, like user posts, images, and comments. Which type of database would be the best fit for this use case?
- Relational Database
- Document Database
- Graph Database
- Key-Value Store
A document database, like MongoDB, is well-suited for storing unstructured data with variable schemas, making it an ideal choice for use cases involving user posts, images, and comments.
Considering the evolution of data privacy, which technology allows computation on encrypted data without decrypting it?
- Blockchain
- Homomorphic Encryption
- Quantum Computing
- Data Masking
Homomorphic Encryption allows computation on encrypted data without the need for decryption. It's a significant advancement in data privacy because it ensures that sensitive data remains encrypted during processing, reducing the risk of data exposure and breaches while still enabling useful computations.
How does transfer learning primarily benefit deep learning models in terms of training time and data requirements?
- Increases training time
- Requires more data
- Decreases training time
- Requires less data
Transfer learning benefits deep learning models by decreasing training time and data requirements. It allows models to leverage pre-trained knowledge, saving time and reducing the need for large datasets. The model starts with knowledge from a source task and fine-tunes it for a target task, which is often faster and requires less data than training from scratch.
While training a deep neural network for a regression task, the model starts to memorize the training data. What's a suitable approach to address this issue?
- Increase the learning rate
- Add more layers to the network
- Apply dropout regularization
- Decrease the batch size
Memorization indicates overfitting. Applying dropout regularization (Option C) is a suitable approach to prevent overfitting in deep neural networks. Increasing the learning rate (Option A) can lead to convergence issues. Adding more layers (Option B) can worsen overfitting. Decreasing the batch size (Option D) may not directly address memorization.
A company wants to deploy a deep learning model in an environment with limited computational resources. What challenge related to deep learning models might they face, and what potential solution could address it?
- Overfitting due to small training datasets
- High memory and processing demands of deep models
- Lack of labeled data for training deep models
- Slow convergence of deep models due to early stopping or small batch sizes
In a resource-constrained environment, one major challenge is the high memory and processing demands of deep learning models. They can be computationally expensive. A potential solution could be model optimization techniques like quantization, pruning, or using smaller network architectures to reduce memory and processing requirements.
For applications requiring ACID transactions across multiple documents or tables, which database type would you lean towards?
- NoSQL Database
- Relational Database
- In-memory Database
- Columnar Database
In cases where ACID (Atomicity, Consistency, Isolation, Durability) transactions across multiple documents or tables are required, relational databases are typically preferred. They provide strong data consistency and support complex transactions.
A streaming platform is receiving real-time data from various IoT devices. The goal is to process this data on-the-fly and produce instantaneous analytics. Which Big Data technology is best suited for this task?
- Apache Flink
- Apache HBase
- Apache Hive
- Apache Pig
Apache Flink is designed for real-time stream processing and analytics, making it a powerful choice for handling data from IoT devices in real-time and producing instantaneous analytics.