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# Simple sentiment analysis with TensorFlow
import tensorflow as tf
from tensorflow import keras
# Build model
model = keras.Sequential([
keras.layers.Embedding(10000, 128),
keras.layers.LSTM(64, dropout=0.5),
keras.layers.Dense(1, activation='sigmoid')
])
# Compile and train
model.compile(
optimizer='adam',
loss='binary_crossentropy',
metrics=['accuracy']
)
# Make predictions
predictions = model.predict(test_data)# Object detection with YOLOv5
import torch
import cv2
# Load model
model = torch.hub.load(
'ultralytics/yolov5',
'yolov5s'
)
# Process image
img = cv2.imread('image.jpg')
results = model(img)
# Extract detections
detections = results.pandas().xyxy[0]
for _, det in detections.iterrows():
label = det['name']
confidence = det['confidence']
print(f"{label}: {confidence:.2%}")Comprehensive guides for implementing AI solutions
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