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2015年7月2日 · B: "Well, I have attended quite a few training sessions in/on/about/of health and safety." I do realise the conversation sounds corny, but it is too hot to be creative and imaginative. I would opt for on. To me, about sounds as if it gives some kind of basic information
2006年2月9日 · yeah in training not on. If you were on traning, you would be using the word on as expressing an action, like you were literally on training like "that boy is on drugs" but if we are involved in something, or doing something it would be in "i am in bed" "i am in training"
2010年3月7日 · Hi, I would like to phrase an Out Of Office letter. I'm in a training during this week. Pelease expect some delay in my responses. I'm on training during this week. Pelease expect a delay in my response. I'm in a course during this week. Pelease expect some delay in my responses. Which...
2008年9月24日 · Hello, Here's the context: a new committee has been created in a company. A consultant is invited to provide a one-day training (for the members of the committee) in/on the missions and operation of the committee. Could you please tell me which preposition is right? Many thanks!
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2018年9月5日 · The problem I find is that the models, for various hyperparameters I try (e.g. number of hidden units, LSTM or GRU) the training loss decreases, but the validation loss stays quite high (I use dropout, the rate I use is 0.5), e.g. My dataset contains about 1000
2019年12月6日 · validation_split: Float between 0 and 1. Fraction of the training data to be used as validation data. The model will set apart this fraction of the training data, will not train on it, and will evaluate the loss and any model metrics on this data at the end of each epoch.
I want to train a deep model with a large amount of training data, but my desktop does not have that power to train such a deep model with these abundant data. I'd like to know whether there are any free cloud services that can be used for training machine
2015年5月7日 · That is, the ReLU units can irreversibly die during training since they can get knocked off the data manifold. For example, you may find that as much as 40% of your network can be "dead" (i.e. neurons that never activate across the entire training dataset) if the
