valid_ds

it likely leads to larger values in the Gram matrix

the image printing function requires that each pixel has a floating point value from 0 to 1

The best way to do this is by first using tesseract to get OCR text in whatever languages you might feel are in there, using langdetect to find what languages are included in the OCR text and then run OCR again with the languages found.

For comparison, we define an identical model, but initialize all of its model parameters to random values.

As is observed in the above results, after an nn.Sequential instance is scripted using the torch.jit.script function, computing performance is improved through the use of symbolic programming.

In the context of computer vision this schedule can lead to improved results.

The photorealistic text-to-image examples in Fig. 11.9.5 suggest that the T5 encoder alone may effectively represent text even without fine-tuning.

Since we use the fixed positional encoding whose values are always between −1 and 1,

position

To compute multiple heads of multi-head attention in parallel, proper tensor manipulation is needed.

Note that h heads can be computed in parallel if we set the number of outputs of linear transformations for the query, key, and value to pqh=pkh=pvh=po.

In the case of a (scalar) regression with observations (xi,yi) for features and labels respectively, vi=yi are scalars, ki=xi are vectors, and the query q denotes the new location where f should be evaluated.

the conditional probability of each token at time step 3 has also changed in Fig. 10.8.2

Using word-level tokenization, the vocabulary size will be significantly larger than that using character-level tokenization, but the sequence lengths will be much shorter.

we can easily get a deep-gated RNN by replacing the hidden state computation in (10.3.1) with that from an LSTM or a GRU.

Reset gates help capture short-term dependencies in sequences. Update gates help capture long-term dependencies in sequences.

Note that only the hidden state is passed to the output layer.

For instance, if the first token is of great importance we will learn not to update the hidden state after the first observation.

neuron

detaching the gradient

Using the chain rule yields

Whenever ξt=0 the recurrent computation terminates at that time step t.

While we can use the chain rule to compute ∂ht/∂wh recursively, this chain can get very long whenever t is large. Let’s discuss a number of strategies for dealing with this problem.

where computation of ht−1 also depends on wh

Having a small value for this upper bound might be viewed as good or bad. On the downside, we are limiting the speed at which we can reduce the value of the objective. On the bright side, this limits by just how much we can go wrong in any one gradient step.

exponentially

There will be many plausible three-word combinations that we likely will not see in our dataset.

formulae

After all, we will significantly overestimate the frequency of the tail, also known as the infrequent words.

frequency

Even today’s massive RNN- and Transformer-based language models seldom incorporate more than thousands of words of context.

probabilistic classifier

compare