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fix broken links in jupyter notebooks (#2033)
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iamarunbrahma authored Jan 25, 2025
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"KerasHub provides a number of pre-trained models, such as [Google\n",
"Bert](https://ai.googleblog.com/2018/11/open-sourcing-bert-state-of-art-pre.html)\n",
"and [GPT-2](https://openai.com/research/better-language-models). You can see\n",
"the list of models available in the [KerasHub repository](https://github.com/keras-team/keras-hub/tree/master/keras_hub/models).\n",
"the list of models available in the [KerasHub repository](https://github.com/keras-team/keras-hub/tree/master/keras_hub/src/models).\n",
"\n",
"It's very easy to load the GPT-2 model as you can see below:"
]
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"with for GPT2.\n",
"\n",
"The code of GPT2 can be found\n",
"[here](https://github.com/keras-team/keras-hub/blob/master/keras_hub/models/gpt2/).\n",
"[here](https://github.com/keras-team/keras-hub/tree/master/keras_hub/src/models/gpt2).\n",
"Conceptually the `GPT2CausalLM` can be hierarchically broken down into several\n",
"modules in KerasHub, all of which have a *from_preset()* function that loads a\n",
"pretrained model:\n",
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},
"source": [
"For more details on KerasHub `Sampler` class, you can check the code\n",
"[here](https://github.com/keras-team/keras-hub/tree/master/keras_hub/samplers)."
"[here](https://github.com/keras-team/keras-hub/tree/master/keras_hub/src/samplers)."
]
},
{
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2 changes: 1 addition & 1 deletion guides/ipynb/keras_cv/coco_metrics.ipynb
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"\n",
"Utility functions to manipulate bounding boxes, transform between formats, and\n",
"pad bounding box Tensors with `-1s` are available from the\n",
"[`keras_cv.bounding_box`](https://github.com/keras-team/keras-cv/blob/master/keras_cv/bounding_box)\n",
"[`keras_cv.bounding_box`](https://github.com/keras-team/keras-cv/tree/master/keras_cv/src/bounding_box)\n",
"package."
]
},
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10 changes: 4 additions & 6 deletions guides/ipynb/keras_tuner/custom_tuner.ipynb
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"In this guide, we will subclass the `HyperModel` class and write a custom\n",
"training loop by overriding `HyperModel.fit()`. For how to write a custom\n",
"training loop with Keras, you can refer to the guide\n",
"[Writing a training loop from scratch](https://keras.io/guides/writing_a_training_loop_from_scratch/).\n",
"[Writing a training loop from scratch](https://keras.io/guides/writing_a_custom_training_loop_in_tensorflow/).\n",
"\n",
"First, we import the libraries we need, and we create datasets for training and\n",
"validation. Here, we just use some random data for demonstration purposes."
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" best_epoch_loss = min(best_epoch_loss, epoch_loss)\n",
"\n",
" # Return the evaluation metric value.\n",
" return best_epoch_loss\n",
""
" return best_epoch_loss\n"
]
},
{
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" directory=\"results\",\n",
" project_name=\"custom_training\",\n",
" overwrite=True,\n",
")\n",
""
")\n"
]
},
{
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},
"nbformat": 4,
"nbformat_minor": 0
}
}

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