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  • What is pre training a neural network? - Cross Validated
    Using a pre-trained network generally makes sense if both tasks or both datasets have something in common The bigger the gap, the less effective pre-training will be It makes little sense to pre-train a network for image classification by training it on financial data first
  • How to use pre trained word2vec model? - Cross Validated
    The pre-trained Google vectors are in binary format $\endgroup$ – Alexandre Commented Mar 13, 2017 at
  • Where to find pre-trained models for transfer learning
    Also, I am actually looking for a pre-trained texture classification model, but reckoning such a question to be too specific, I thought a general idea on where people look for pre-trained models would have been a good starting point $\endgroup$
  • How to Fine Tune a pre-trained network - Cross Validated
    In this paper, which I read many months back, I understood that transfer learning was a process where you took the first n layers from a pre-trained model, added on your own final layers for your task, and then fine tuning was where you did NOT freeze the weights from layers you transferred from the pre-trained model, but instead allowed them
  • Fine Tuning vs. Transferlearning vs. Learning from scratch
    Using a pre-trained model in a similar task, usually have great results when we use Fine-tuning However, if you do not have enough data in the new dataset or even your hyperparameters are not the best ones, you can get unsatisfactory results Machine learning always depends on its dataset and network's parameters
  • Difference between non-contextual and contextual word embeddings
    However, when people say contextual embeddings, they don't mean the vectors from the look-up table, they mean the hiden states of the pre-trained model As you said these states are contextualized, but it is kind of confusing to call them word embeddings
  • How should I train my CNN with a tiny dataset - Cross Validated
    You should start from a pre-trained model, replace it's output layer with a 3 class classification layer and finetune your model on your images This is a standard procedure Here's an example in pytorch for you
  • Validation loss increases while Training loss decrease
    When fine-tuning the pre-trained model the optimizer starts right at the beginning of your training rate schedule, so starts out with a high training rate causing your loss to decrease rapidly as it overfits the training data, and conversely, the validation loss rapidly increases
  • How do you add knowledge to LLMs? - Cross Validated
    From LIMA: Less Is More for Alignment (2023) by Chunting Zhou et al : These results strongly suggest that almost all knowledge in large language models is learned during pretraining, and only limited instruction tuning data is necessary to teach models to produce high quality output
  • Is there a way to incorporate new data into an already trained neural . . .
    Stack Exchange Network Stack Exchange network consists of 183 Q A communities including Stack Overflow, the largest, most trusted online community for developers to learn, share their knowledge, and build their careers





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