In this lab, we explored the use of Convolutional Neural Networks (CNNs) for image classification tasks. We implemented a simple CNN from scratch and trained it on the MNIST dataset, achieving an accuracy of over 99%. We also learned how to use pre-trained CNNs and fine-tune them for a new task.
In this lab, we learned about transfer learning and fine-tuning in the context of deep learning. We used a pre-trained model and fine-tuned it for a new image classification task, achieving a significant improvement in performance over training a model from scratch on the small dataset.
In this lab, we learned about techniques for improving the performance of deep learning models, including parallelization and optimization of the training process. We used multiple GPUs and distributed training to train a model on a large dataset, achieving a significant speedup over training on a single GPU.
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