20 de março de 2024 04:14:34 ART
Welcome to our virtual hub for mastering Artificial Intelligence! At ProgrammingHomeworkHelp.com, we're committed to unraveling the complexities of AI assignments. Today, we're delving into code-based questions, offering expert insights and solutions to elevate your understanding. Join us as we navigate through these challenges and emerge stronger in our AI endeavors.
Question 1:
You're tasked with implementing a simple neural network for binary classification using Python and NumPy. Provide a code snippet illustrating the construction of the neural network architecture, including the forward pass and backward pass for training.
Solution 1:
import numpy as np
class NeuralNetwork:
def __init__(self, input_size, hidden_size, output_size):
self.input_size = input_size
self.hidden_size = hidden_size
self.output_size = output_size
# Initialize weights and biases
self.weights_input_hidden = np.random.randn(input_size, hidden_size)
self.bias_input_hidden = np.zeros((1, hidden_size))
self.weights_hidden_output = np.random.randn(hidden_size, output_size)
self.bias_hidden_output = np.zeros((1, output_size))
def sigmoid(self, x):
return 1 / (1 + np.exp(-x))
def sigmoid_derivative(self, x):
return x * (1 - x)
def forward(self, X):
# Forward pass
self.hidden_input = np.dot(X, self.weights_input_hidden) + self.bias_input_hidden
self.hidden_output = self.sigmoid(self.hidden_input)
self.output = np.dot(self.hidden_output, self.weights_hidden_output) + self.bias_hidden_output
output_probs = self.sigmoid(self.output)
return output_probs
def backward(self, X, y, output_probs, learning_rate):
# Backward pass
error = y - output_probs
output_delta = error * self.sigmoid_derivative(output_probs)
hidden_error = output_delta.dot(self.weights_hidden_output.T)
hidden_delta = hidden_error * self.sigmoid_derivative(self.hidden_output)
# Update weights and biases
self.weights_hidden_output += self.hidden_output.T.dot(output_delta) * learning_rate
self.bias_hidden_output += np.sum(output_delta, axis=0, keepdims=True) * learning_rate
self.weights_input_hidden += X.T.dot(hidden_delta) * learning_rate
self.bias_input_hidden += np.sum(hidden_delta, axis=0, keepdims=True) * learning_rate
Question 2:
You're working on implementing a k-nearest neighbors (k-NN) classifier for image recognition. Write a Python function to classify a new image based on its nearest neighbors from a training dataset using Euclidean distance as the similarity metric.
Solution 2:
import numpy as np
def euclidean_distance(x1, x2):
return np.sqrt(np.sum((x1 - x2) ** 2))
def knn_classify(X_train, y_train, X_test, k):
predictions = []
for test_sample in X_test:
distances = [euclidean_distance(test_sample, train_sample) for train_sample in X_train]
nearest_neighbors = np.argsort(distances)[:k]
k_nearest_labels = [y_train[neighbor] for neighbor in nearest_neighbors]
predicted_label = max(set(k_nearest_labels), key=k_nearest_labels.count)
predictions.append(predicted_label)
return predictions
Conclusion
Mastering Artificial Intelligence entails not just theoretical understanding but also hands-on implementation of algorithms and models. Through our expert solutions, we aim to empower you with the practical skills needed to excel in the field. Remember, at ProgrammingHomeworkHelp.com, we're here to provide unparalleled
online artificial intelligence assignment help, tailored to your learning journey. Let's continue unraveling the mysteries of AI, one code snippet at a time.
Este post foi editado por thomas brown em 20 de março de 2024 04:16:40 ART"
