人工智能在機(jī)器學(xué)習(xí)中的八大應(yīng)用領(lǐng)域

import nltkfrom nltk.tokenize import word_tokenize
sentence = "Natural language processing is fascinating!"tokens = word_tokenize(sentence)print("Tokenized words:", tokens)

import tensorflow as tffrom tensorflow import kerasfrom tensorflow.keras.preprocessing.image import load_img, img_to_array
model = keras.applications.MobileNetV2(weights='imagenet')
image_path = 'cat.jpg'image = load_img(image_path, target_size=(224, 224))image_array = img_to_array(image)image_array = tf.expand_dims(image_array, 0)image_array = keras.applications.mobilenet_v2.preprocess_input(image_array)
predictions = model.predict(image_array)decoded_predictions = keras.applications.mobilenet_v2.decode_predictions(predictions.numpy())print("Top predictions:", decoded_predictions[0])

import torchimport torch.nn as nnimport torchvision.transforms as transformsfrom torchvision.models import resnet18from PIL import Image
class MedicalImageClassifier(nn.Module):    def __init__(self, num_classes):        super(MedicalImageClassifier, self).__init__()        self.model = resnet18(pretrained=True)        self.model.fc = nn.Linear(512, num_classes)
    def forward(self, x):        return self.model(x)
transform = transforms.Compose([    transforms.Resize((224, 224)),    transforms.ToTensor(),    transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])])
model = MedicalImageClassifier(num_classes=2)model.load_state_dict(torch.load('medical_classifier.pth', map_location=torch.device('cpu')))model.eval()
image_path = 'xray.jpg'image = Image.open(image_path)image_tensor = transform(image).unsqueeze(0)
with torch.no_grad():    output = model(image_tensor)
print("Predicted class probabilities:", torch.softmax(output, dim=1))

import pandas as pdfrom sklearn.model_selection import train_test_splitfrom sklearn.ensemble import RandomForestClassifierfrom sklearn.metrics import accuracy_score
data = pd.read_csv('credit_data.csv')X = data.drop('default', axis=1)y = data['default']
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
model = RandomForestClassifier(n_estimators=100, random_state=42)model.fit(X_train, y_train)
y_pred = model.predict(X_test)accuracy = accuracy_score(y_test, y_pred)print("Accuracy:", accuracy)

import numpy as np
movies = ['Movie A', 'Movie B', 'Movie C', 'Movie D', 'Movie E']user_ratings = np.array([4.5, 3.0, 5.0, 0.0, 2.5])
# Calculate similarity using cosine similaritydef cosine_similarity(a, b):    dot_product = np.dot(a, b)    norm_a = np.linalg.norm(a)    norm_b = np.linalg.norm(b)    return dot_product / (norm_a * norm_b)
similarities = [cosine_similarity(user_ratings, np.array(ratings)) for ratings in movie_ratings]recommended_movie = movies[np.argmax(similarities)]print("Recommended movie:", recommended_movie)

import numpy as npfrom sklearn.model_selection import train_test_splitfrom sklearn.ensemble import RandomForestClassifierfrom sklearn.metrics import accuracy_score
data = np.load('sensor_data.npy')X = data[:, :-1]y = data[:, -1]
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
model = RandomForestClassifier(n_estimators=100, random_state=42)model.fit(X_train, y_train)
y_pred = model.predict(X_test)accuracy = accuracy_score(y_test, y_pred)print("Accuracy:", accuracy)

import pandas as pdfrom sklearn.model_selection import train_test_splitfrom sklearn.linear_model import LinearRegressionfrom sklearn.metrics import mean_squared_error
data = pd.read_csv('energy_consumption.csv')X = data.drop('consumption', axis=1)y = data['consumption']
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
model = LinearRegression()model.fit(X_train, y_train)
y_pred = model.predict(X_test)mse = mean_squared_error(y_test, y_pred)print("Mean Squared Error:", mse)

from sklearn.datasets import load_irisfrom sklearn.tree import DecisionTreeClassifierfrom sklearn.model_selection import train_test_splitfrom sklearn.metrics import accuracy_score
iris = load_iris()X = iris.datay = iris.target
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
model = DecisionTreeClassifier()model.fit(X_train, y_train)
y_pred = model.predict(X_test)accuracy = accuracy_score(y_test, y_pred)print("Accuracy:", accuracy)