Difference between Compiled and Interpreted Languages are mentioned below :

Compiled Languages:

• Translation: Convert the entire source code into machine code before execution.
• Performance: Generally faster since code is directly executed by the computer’s CPU.
• Error Detection: Errors are caught during the compilation process.
• Examples: C, C++, Rust.

Interpreted Languages:

• Translation: Convert source code into intermediate code, which is then executed line-by-line.
• Performance: Generally slower due to the overhead of interpretation.
• Error Detection: Errors are caught at runtime.
• Examples: Python, JavaScript, Ruby.

Both types have their use cases and choosing between them depends on the specific requirements of a project.

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Convolutional Neural Network (CNN)

Convolutional Neural Networks (CNNs) are a class of deep learning algorithms specifically designed for processing grid-like data, such as images. They are particularly effective in tasks where spatial hierarchies and patterns are important.

Key Components:

• Convolutional Layers: Apply convolution operations to detect features such as edges and textures. They use filters or kernels that slide over the input data.
• Activation Functions: Non-linear functions (e.g., ReLU) that introduce non-linearity into the model.
• Pooling Layers: Reduce the spatial dimensions of feature maps, helping to reduce computation and control overfitting.
• Fully Connected Layers: Classify the data based on extracted features.

Applications:

• Image Classification: Identifying objects or categories within an image (e.g., classifying images of animals).
• Object Detection: Locating objects within an image and classifying them (e.g., detecting faces in photos).
• Image Segmentation: Dividing an image into regions or segments for detailed analysis (e.g., medical imaging).
• Facial Recognition: Identifying or verifying individuals based on facial features.
• Video Analysis: Recognizing patterns or actions in video frames (e.g., surveillance).

CNNs are crucial for many modern AI applications due to their ability to learn and extract complex features from data.

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The ACID properties ensure reliable transactions in databases:

1. Atomicity: Ensures that a transaction is indivisible; it either completes entirely or has no effect. If any part of the transaction fails, the entire transaction is rolled back, maintaining data integrity.
2. Consistency: Guarantees that a transaction brings the database from one valid state to another, adhering to all defined rules, constraints, and relationships. This ensures that any transaction only results in valid database states.
3. Isolation: Ensures that transactions are executed independently, without interference. Intermediate transaction results are not visible to other transactions until the transaction is committed, preventing conflicts and ensuring data consistency.
4. Durability: Ensures that once a transaction is committed, its changes are permanent and survive system failures. The data changes are written to non-volatile storage, guaranteeing that committed transactions remain intact even after crashes or restarts.

These properties collectively maintain the integrity, consistency, and reliability of database transactions.

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