Convolutional Neural Network (CNN):
- Convolutional Layers:
- Convolutional layers apply filters (also known as kernels) to the input data.
- Filters slide over the input to detect spatial hierarchies of features, capturing patterns like edges, textures, and more complex structures.
- The output of a convolutional layer is called a feature map.
- Activation Function:
- After the convolution operation, an activation function (commonly ReLU – Rectified Linear Unit) is applied element-wise to introduce non-linearity.
- This helps the network learn complex relationships and representations.
- Pooling Layers:
- Pooling layers reduce the spatial dimensions of the feature maps, helping to decrease computation and control overfitting.
- Max pooling, for example, selects the maximum value from a group of neighboring pixels.
- Flattening and Fully Connected Layers:
- The output from the convolutional and pooling layers is flattened into a vector.
- Fully connected layers use these vectors to make predictions by learning hierarchical representations.
- Backpropagation and Training:
- CNNs are trained using backpropagation and optimization algorithms (e.g., stochastic gradient descent) to minimize the difference between predicted and actual outputs.
K-Nearest Neighbors (KNN):
- Distance Metric:
- KNN relies on a distance metric (e.g., Euclidean distance) to measure the similarity between data points.
- The choice of distance metric can significantly impact the algorithm’s performance.
- Choosing ‘k’:
- The parameter ‘k’ represents the number of nearest neighbors to consider.
- A smaller ‘k’ can make the model sensitive to noise, while a larger ‘k’ can smooth out local variations.
- Decision Rule:
- For classification, KNN typically uses majority voting among the ‘k’ neighbors to determine the class of a test point.
- For regression, the average (or weighted average) of the ‘k’ neighbors’ values is used.
- Lazy Learning:
- KNN is a lazy learner because it doesn’t build a model during training. It memorizes the entire training dataset and performs computations at runtime.
- Scalability:
- KNN can be computationally expensive, especially as the size of the training dataset grows since it requires comparing the test point to all training examples.
Understanding these internal details can help you grasp the underlying mechanisms of CNN and KNN algorithms and make more informed decisions when applying them to various machine-learning tasks.