A graph is a non-linear data structure composed of vertices (also known as nodes) connected by edges. It’s used to represent relationships between entities. Here’s a brief overview: Graph Representation: Vertices: Fundamental units of the graph. Edges: Connect two nodes. Directed Graph: Edges have aRead more
A graph is a non-linear data structure composed of vertices (also known as nodes) connected by edges. It’s used to represent relationships between entities. Here’s a brief overview:
- Graph Representation:
- Vertices: Fundamental units of the graph.
- Edges: Connect two nodes.
- Directed Graph: Edges have a direction.
- Undirected Graph: Edges are bidirectional.
- Common Graph Traversal Methods:
- Depth First Search (DFS):
- Starts at a node and explores as far as possible along each branch before backtracking
- Implemented using a stack or recursion.
- Useful for finding connected components, topological sorting, and cycle detection.
- Breadth First Search (BFS):
- Traverses level by level, exploring neighbor nodes at each level
- Implemented using a queue.
- Useful for shortest path, connected components, and network analysis.
- Depth First Search (DFS):
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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: ConvolutRead more
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:
Applications:
CNNs are crucial for many modern AI applications due to their ability to learn and extract complex features from data.
Hope it helps !
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