To integrate deep learning and modeling for computer vision tasks, you can follow these steps:

1.  Select a suitable deep learning framework: There are several popular deep learning frameworks such as TensorFlow, PyTorch, and Caffe, which you can choose based on your needs and experience.
    
2.  Gather and preprocess the dataset: Collect a large enough dataset to train your model, and preprocess it to prepare it for training. This includes converting images to a suitable format, resizing, normalizing, and splitting into training and validation sets.
    
3.  Design and train the model: Choose an appropriate deep learning architecture for your task, such as a Convolutional Neural Network (CNN) for image classification, or a Generative Adversarial Network (GAN) for image generation. Train the model on your preprocessed dataset using the deep learning framework.
    
4.  Evaluate the model: Evaluate the performance of the model on a test set, and use the results to make any necessary adjustments to the model architecture or training process.
    
5.  Deploy the model: Finally, integrate the trained model into your application or system, so it can be used to make predictions on new images.
    

It's recommended to start with simple computer vision tasks and gradually increase the complexity of the task to get hands-on experience and improve your skills in this field.
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