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The Lakers' offense often leaned on its star player's ability to drive to the basket, draw defenders, and either score or pass to open teammates. They also utilized post-ups from their big men, creating scoring opportunities near the basket. The team was successful in establishing an inside-out approach. They would pass the ball inside, drawing the defense and then kicking it out to the perimeter for open three-point shots. The Trail Blazers focused on ball movement and player movement, creating opportunities for open shots. They relied heavily on their guards to handle the ball. They would also use off-ball screens to free up shooters. Their objective was to create open looks. They aimed to get the ball into the hands of their shooters. Both teams demonstrated a solid understanding of offensive principles. The Lakers' approach centered on power and size. The Trail Blazers prioritized speed and precision, each with unique advantages.
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**Selamat menonton!**
Next, load a pre-trained image classification model. You can find pre-trained models online from various sources. A popular choice is the MobileNet model, which is a lightweight and efficient model ideal for edge devices. Download the pre-trained model and save it in a location accessible to your Python script. Inside your script, load the model using TensorFlow's `tf.keras.models.load_model()` function. Then, load an image that you want to classify. You can either use an image file or capture an image using a camera connected to your Jetson Nano. Use OpenCV's `cv2.imread()` function to load the image. Preprocess the image to match the input requirements of your pre-trained model. This usually involves resizing the image and normalizing the pixel values. Use the model to make predictions on the image. Call the `model.predict()` function, passing in the preprocessed image. The function will return a set of probabilities for each object class. Extract the class with the highest probability. Use the output probabilities to determine the most likely object in the image. Finally, display the predicted object label. Print the predicted label to the console or display it on the screen. Running this code gives you a very basic AI application, and it proves that your system is working. This is the heart of the **Jetson Nano tutorial**. Congratulations, you have successfully run your first AI application on the Jetson Nano! This is just a starting point, of course. There is so much more to explore. With these building blocks, you're well on your way to building more sophisticated AI applications on your Jetson Nano. This **Jetson Nano tutorial PDF** will guide you.
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