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is able to represent long term dependencies, the gradient of a long term interaction has exponentially smaller magnitude than the gradient of a short term interaction. it does not mean that it is impossible to learn, but that it might take a very long time to learn long - term dependencies, because the signal about the... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org).pdf | 418 | Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org) | 0 |
chapter 10. sequence modeling : recurrent and recursive nets hundreds of steps ), but the problem of learning long - term dependencies remains one of the main challenges in deep learning. 10. 8 echo state networks the recurrent weights mapping from h ( 1 ) t− to h ( ) t and the input weights mapping from x ( ) t to h (... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org).pdf | 419 | Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org) | 0 |
2002 that it uses spiking neurons ( with binary outputs ) instead of the continuous - valued hidden units used for esns. both esns and liquid state machines are termed reservoir computing ( lukosevicius and jaeger 2009, ) to denote the fact that the hidden units form of reservoir of temporal features which may capture ... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org).pdf | 419 | Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org) | 0 |
##bly with simple learning algorithms (, ). jaeger 2003 the important question is therefore : how do we set the input and recurrent weights so that a rich set of histories can be represented in the recurrent neural network state? the answer proposed in the reservoir computing literature is to view the recurrent net as ... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org).pdf | 419 | Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org) | 0 |
chapter 10. sequence modeling : recurrent and recursive nets to understand the [UNK] of the spectral radius, consider the simple case of back - propagation with a jacobian matrix j that does not change with t. this case happens, for example, when the network is purely linear. suppose that j has an eigenvector v with co... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org).pdf | 420 | Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org) | 0 |
##v diverge by δj nv after n steps of back - propagation. if v is chosen to be a unit eigenvector of j with eigenvalue λ, then multiplication by the jacobian simply scales the [UNK] at each step. the two executions of back - propagation are separated by a distance of δ λ | | n. when v corresponds to the largest value o... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org).pdf | 420 | Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org) | 0 |
work on echo state networks advocates using a spectral radius much larger than unity (, ;, ). yildiz et al. 2012 jaeger 2012 everything we have said about back - propagation via repeated matrix multipli - cation applies equally to forward propagation in a network with no nonlinearity, where the state h ( + 1 ) t = h ( ... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org).pdf | 420 | Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org) | 0 |
+ 1 ) t propagates one step backward, during back - propagation. note that neither w nor j need to be symmetric ( al - though they are square and real ), so they can have complex - valued eigenvalues and eigenvectors, with imaginary components corresponding to potentially oscillatory 405 | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org).pdf | 420 | Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org) | 0 |
chapter 10. sequence modeling : recurrent and recursive nets behavior ( if the same jacobian was applied iteratively ). even though h ( ) t or a small variation of h ( ) t of interest in back - propagation are real - valued, they can be expressed in such a complex - valued basis. what matters is what happens to the mag... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org).pdf | 421 | Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org) | 0 |
the recurrent dynamics to become bounded. note that it is possible for back - propagation to retain unbounded dynamics even when forward propagation has bounded dynamics, for example, when a sequence of tanh units are all in the middle of their linear regime and are connected by weight matrices with spectral radius gre... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org).pdf | 421 | Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org) | 0 |
, ; et al., ). in this setting, an initial spectral radius of 1. 2 performs well, combined 2013 with the sparse initialization scheme described in section. 8. 4 10. 9 leaky units and other strategies for multiple time scales one way to deal with long - term dependencies is to design a model that operates at multiple ti... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org).pdf | 421 | Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org) | 0 |
chapter 10. sequence modeling : recurrent and recursive nets used to model fine - grained time scales. 10. 9. 1 adding skip connections through time one way to obtain coarse time scales is to add direct connections from variables in the distant past to variables in the present. the idea of using such skip connections da... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org).pdf | 422 | Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org) | 0 |
function of τ d rather than τ. since there are both delayed and single step connections, gradients may still explode exponentially in τ. this allows the learning algorithm to capture longer dependencies although not all long - term dependencies may be represented well in this way. 10. 9. 2 leaky units and a spectrum of... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org).pdf | 422 | Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org) | 0 |
with linear self - connections can behave similarly to such running averages. such hidden units are called leaky units. skip connections through d time steps are a way of ensuring that a unit can always learn to be influenced by a value from d time steps earlier. the use of a linear self - connection with a weight near ... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org).pdf | 422 | Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org) | 0 |
chapter 10. sequence modeling : recurrent and recursive nets there are two basic strategies for setting the time constants used by leaky units. one strategy is to manually fix them to values that remain constant, for example by sampling their values from some distribution once at initialization time. another strategy is... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org).pdf | 423 | Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org) | 0 |
on a long time scale. skip connections through time edges. units receiving such add new connections may learn to operate on a long time scale but may also choose to focus on their other short - term connections. there are [UNK] ways in which a group of recurrent units can be forced to operate at [UNK] time scales. one ... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org).pdf | 423 | Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org) | 0 |
practical applications are called gated rnns. these include the long short - term memory and networks based on the. gated recurrent unit like leaky units, gated rnns are based on the idea of creating paths through time that have derivatives that neither vanish nor explode. leaky units did this with connection weights t... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org).pdf | 423 | Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org) | 0 |
chapter 10. sequence modeling : recurrent and recursive nets at each time step. × input input gate forget gate output gate output state self - loop × + × figure 10. 16 : block diagram of the lstm recurrent network “ cell. ” cells are connected recurrently to each other, replacing the usual hidden units of ordinary recu... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org).pdf | 424 | Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org) | 0 |
evidence for a particular feature or category ) over a long duration. however, once that information has been used, it might be useful for the neural network to forget the old state. for example, if a sequence is made of sub - sequences and we want a leaky unit to accumulate evidence inside each sub - subsequence, we n... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org).pdf | 424 | Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org) | 0 |
chapter 10. sequence modeling : recurrent and recursive nets is what gated rnns do. 10. 10. 1 lstm the clever idea of introducing self - loops to produce paths where the gradient can flow for long durations is a core contribution of the initial long short - term memory ( lstm ) model ( hochreiter and schmidhuber 1997, )... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org).pdf | 425 | Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org) | 0 |
handwriting recognition ( graves et al., ), speech recognition ( 2009 graves 2013 graves and jaitly 2014 et al., ;, ), handwriting generation ( graves 2013, ), machine translation ( sutskever 2014 et al., ), image captioning (, ; kiros et al. 2014b vinyals 2014b xu 2015 et al., ; et al., ) and parsing ( vinyals 2014a e... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org).pdf | 425 | Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org) | 0 |
- loop ), in addition to the outer recurrence of the rnn. each cell has the same inputs and outputs as an ordinary recurrent network, but has more parameters and a system of gating units that controls the flow of information. the most important component is the state unit s ( ) t i that has a linear self - loop similar ... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org).pdf | 425 | Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org) | 0 |
chapter 10. sequence modeling : recurrent and recursive nets internal state is thus updated as follows, but with a conditional self - loop weight f ( ) t i : s ( ) t i = f ( ) t i s ( 1 ) t− i + g ( ) t i σ b i + j ui, jx ( ) t j + j wi, jh ( 1 ) t− j, ( 10. 41 ) where b, u and w respectively denote the biases, input w... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org).pdf | 426 | Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org) | 0 |
of the lstm cell can also be shut [UNK], via the output gate q ( ) t i, which also uses a sigmoid unit for gating : h ( ) t i = tanh s ( ) t i q ( ) t i ( 10. 43 ) q ( ) t i = σ bo i + j uo i, jx ( ) t j + j w o i, j h ( 1 ) t− j ( 10. 44 ) which has parameters bo, u o, w o for its biases, input weights and recurrent w... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org).pdf | 426 | Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org) | 0 |
data sets designed for testing the ability to learn long - term dependencies (, ; bengio et al. 1994 hochreiter and schmidhuber 1997 hochreiter 2001, ; et al., ), then on challenging sequence processing tasks where state - of - the - art performance was obtained ( graves 2012, ; graves 2013 sutskever 2014 et al., ; et ... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org).pdf | 426 | Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org) | 0 |
chapter 10. sequence modeling : recurrent and recursive nets some answers to these questions are given with the recent work on gated rnns, whose units are also known as gated recurrent units or grus (, ; cho et al. 2014b chung 2014 2015a jozefowicz 2015 chrupala 2015 et al.,, ; et al., ; et al., ). the main [UNK] with ... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org).pdf | 427 | Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org) | 0 |
value is defined as usual : u ( ) t i = σ bu i + j uu i, jx ( ) t j + j wu i, jh ( ) t j ( 10. 46 ) and r ( ) t i = σ br i + j u r i, jx ( ) t j + j wr i, jh ( ) t j. ( 10. 47 ) the reset and updates gates can individually “ ignore ” parts of the state vector. the update gates act like conditional leaky integrators that... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org).pdf | 427 | Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org) | 0 |
around this theme can be designed. for example the reset gate ( or forget gate ) output could be shared across multiple hidden units. alternately, the product of a global gate ( covering a whole group of units, such as an entire layer ) and a local gate ( per unit ) could be used to combine global control and local con... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org).pdf | 427 | Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org) | 0 |
chapter 10. sequence modeling : recurrent and recursive nets 10. 11 optimization for long - term dependencies section and section have described the vanishing and exploding gradient 8. 2. 5 10. 7 problems that occur when optimizing rnns over many time steps. an interesting idea proposed by martens and sutskever 2011 ( ... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org).pdf | 428 | Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org) | 0 |
sutskever 2013 et al. ( ) found that simpler methods such as nesterov momentum with careful initialization could achieve similar results. see sutskever 2012 ( ) for more detail. both of these approaches have largely been replaced by simply using sgd ( even without momentum ) applied to lstms. this is part of a continui... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org).pdf | 428 | Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org) | 0 |
separated by tiny regions where the objective function changes quickly, forming a kind of [UNK]. the [UNK] that arises is that when the parameter gradient is very large, a gradient descent parameter update could throw the parameters very far, into a region where the objective function is larger, undoing much of the wor... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org).pdf | 428 | Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org) | 0 |
chapter 10. sequence modeling : recurrent and recursive nets decay slowly enough that consecutive steps have approximately the same learning rate. a step size that is appropriate for a relatively linear part of the landscape is often inappropriate and causes uphill motion if we enter a more curved part of the landscape... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org).pdf | 429 | Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org) | 0 |
moderate ( right ) reaction to the [UNK]. while it does ascend the [UNK], the step size is restricted so that it cannot be propelled away from steep region near the solution. figure adapted with permission from pascanu 2013 et al. ( ). a simple type of solution has been in use by practitioners for many years : clipping... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org).pdf | 429 | Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org) | 0 |
chapter 10. sequence modeling : recurrent and recursive nets where v is the norm threshold and g is used to update parameters. because the gradient of all the parameters ( including [UNK] groups of parameters, such as weights and biases ) is renormalized jointly with a single scaling factor, the latter method has the a... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org).pdf | 430 | Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org) | 0 |
. clipping the gradient norm per - minibatch will not change the direction of the gradient for an individual minibatch. however, taking the average of the norm - clipped gradient from many minibatches is not equivalent to clipping the norm of the true gradient ( the gradient formed from using all examples ). examples t... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org).pdf | 430 | Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org) | 0 |
also been proposed ( graves 2013, ) to clip the back - propagated gradient ( with respect to hidden units ) but no comparison has been published between these variants ; we conjecture that all these methods behave similarly. 10. 11. 2 regularizing to encourage information flow gradient clipping helps to deal with explo... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org).pdf | 430 | Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org) | 0 |
chapter 10. sequence modeling : recurrent and recursive nets maintain its magnitude, even if the loss function only penalizes the output at the end of the sequence. formally, we want ( ∇h ( ) t l ) ∂h ( ) t ∂h ( 1 ) t− ( 10. 50 ) to be as large as ∇h ( ) t l. ( 10. 51 ) with this objective, pascanu 2013 et al. ( ) prop... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org).pdf | 431 | Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org) | 0 |
back - propagate through them ). the experiments with this regularizer suggest that, if combined with the norm clipping heuristic ( which handles gradient explosion ), the regularizer can considerably increase the span of the dependencies that an rnn can learn. because it keeps the rnn dynamics on the edge of explosive... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org).pdf | 431 | Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org) | 0 |
words — every day commonsense knowledge, like “ a cat is a kind of animal, ” or very specific facts that you need to know to accomplish your current goals, like “ the meeting with the sales team is at 3 : 00 pm in room 141. ” neural networks excel at storing implicit knowledge. however, they struggle to memorize facts. ... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org).pdf | 431 | Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org) | 0 |
chapter 10. sequence modeling : recurrent and recursive nets task network, controlling the memory memory cells writing mechanism reading mechanism figure 10. 18 : a schematic of an example of a network with an explicit memory, capturing some of the key design elements of the neural turing machine. in this diagram we di... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org).pdf | 432 | Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org) | 0 |
chapter 10. sequence modeling : recurrent and recursive nets same input before it can be stored in a neural network parameters, and even then, that input will not be stored especially precisely. graves 2014b et al. ( ) hypothesized that this is because neural networks lack the equivalent of the working memory system th... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org).pdf | 433 | Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org) | 0 |
mecha - nism. memory networks originally required a supervision signal instructing them how to use their memory cells. graves 2014b et al. ( ) introduced the neural turing machine, which is able to learn to read from and write arbitrary content to memory cells without explicit supervision about which actions to underta... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org).pdf | 433 | Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org) | 0 |
grus. the [UNK] is that the network outputs an internal state that chooses which cell to read from or write to, just as memory accesses in a digital computer read from or write to a specific address. it is [UNK] to optimize functions that produce exact, integer addresses. to alleviate this problem, ntms actually read to... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org).pdf | 433 | Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org) | 0 |
chapter 10. sequence modeling : recurrent and recursive nets the single scalar stored by an lstm or gru memory cell. there are two reasons to increase the size of the memory cell. one reason is that we have increased the cost of accessing a memory cell. we pay the computational cost of producing a [UNK] for many cells,... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org).pdf | 434 | Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org) | 0 |
instruction as saying, “ retrieve the lyrics of the song that has the chorus ‘ we all live in a yellow submarine. ’ ” content - based addressing is more useful when we make the objects to be retrieved large — if every letter of the song was stored in a separate memory cell, we would not be able to find them this way. by... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org).pdf | 434 | Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org) | 0 |
a memory. although that task neural network could be feedforward or recurrent, the overall system is a recurrent network. the task network can choose to read from or write to specific memory addresses. explicit memory seems to allow models to learn tasks that ordinary rnns or lstm rnns cannot learn. one reason for this ... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org).pdf | 434 | Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org) | 0 |
chapter 10. sequence modeling : recurrent and recursive nets mechanism for choosing an address is in its form identical to the attention mechanism which had been previously introduced in the context of machine translation (, ) and discussed in section. the idea bahdanau et al. 2015 12. 4. 5. 1 of attention mechanisms f... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org).pdf | 435 | Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org) | 0 |
chapter 11 practical methodology successfully applying deep learning techniques requires more than just a good knowledge of what algorithms exist and the principles that explain how they work. a good machine learning practitioner also needs to know how to choose an algorithm for a particular application and how to moni... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org).pdf | 436 | Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org) | 0 |
learning techniques and being good at [UNK] kinds of math. in prac - tice, one can usually do much better with a correct application of a commonplace algorithm than by sloppily applying an obscure algorithm. correct application of an algorithm depends on mastering some fairly simple methodology. many of the recommendat... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org).pdf | 436 | Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org) | 0 |
chapter 11. practical methodology estimation of the appropriate performance metrics. • instrument the system well to determine bottlenecks in performance. diag - nose which components are performing worse than expected and whether it is due to overfitting, underfitting, or a defect in the data or software. • repeatedly m... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org).pdf | 437 | Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org) | 0 |
of the steps in this process. 11. 1 performance metrics determining your goals, in terms of which error metric to use, is a necessary first step because your error metric will guide all of your future actions. you should also have an idea of what level of performance you desire. keep in mind that for most applications, ... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org).pdf | 437 | Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org) | 0 |
of collecting more data. data collection can require time, money, or human [UNK] ( for example, if your data collection process involves performing invasive medical tests ). when your goal is to answer a scientific question about which algorithm performs better on a fixed benchmark, the benchmark 422 | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org).pdf | 437 | Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org) | 0 |
chapter 11. practical methodology specification usually determines the training set and you are not allowed to collect more data. how can one determine a reasonable level of performance to expect? typically, in the academic setting, we have some estimate of the error rate that is attainable based on previously published... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org).pdf | 438 | Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org) | 0 |
system. however, many applications require more advanced metrics. sometimes it is much more costly to make one kind of a mistake than another. for example, an e - mail spam detection system can make two kinds of mistakes : incorrectly classifying a legitimate message as spam, and incorrectly allowing a spam message to ... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org).pdf | 438 | Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org) | 0 |
report that the disease is absent. clearly, accuracy is a poor way to characterize the performance of such a system. one way to solve this problem is to instead measure precision and recall. precision is the fraction of detections reported by the model that were correct, while recall is the fraction of true events that... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org).pdf | 438 | Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org) | 0 |
chapter 11. practical methodology network designed to detect a disease outputs [UNK] = p ( y = 1 | x ), estimating the probability that a person whose medical results are described by features x has the disease. we choose to report a detection whenever this score exceeds some threshold. by varying the threshold, we can... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org).pdf | 439 | Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org) | 0 |
an example of this situation. the task is to transcribe the address number from a photograph in order to associate the location where the photo was taken with the correct address in a map. because the value of the map degrades considerably if the map is inaccurate, it is important to add an address only if the transcri... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org).pdf | 439 | Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org) | 0 |
goal for the project was to reach human - level transcription accuracy while maintaining 95 % coverage. human - level performance on this task is 98 % accuracy. many other metrics are possible. we can for example, measure click - through rates, collect user satisfaction surveys, and so on. many specialized application ... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org).pdf | 439 | Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org) | 0 |
chapter 11. practical methodology 11. 2 default baseline models after choosing performance metrics and goals, the next step in any practical application is to establish a reasonable end - to - end system as soon as possible. in this section, we provide recommendations for which algorithms to use as the first baseline ap... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org).pdf | 440 | Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org) | 0 |
supervised learning with fixed - size vectors as input, use a feedforward network with fully connected layers. if the input has known topological structure ( for example, if the input is an image ), use a convolutional network. in these cases, you should begin by using some kind of piecewise linear unit ( relus or their... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org).pdf | 440 | Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org) | 0 |
and networks with sigmoidal nonlinearities. while it is reasonable to omit batch normalization from the very first baseline, it should be introduced quickly if optimization appears to be problematic. unless your training set contains tens of millions of examples or more, you should include some mild forms of regularizat... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org).pdf | 440 | Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org) | 0 |
chapter 11. practical methodology if your task is similar to another task that has been studied extensively, you will probably do well by first copying the model and algorithm that is already known to perform best on the previously studied task. you may even want to copy a trained model from that task. for example, it i... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org).pdf | 441 | Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org) | 0 |
, when the number of labeled examples is very small (, ; kingma et al. 2014 rasmus 2015 et al., ). if your application is in a context where unsupervised learning is known to be important, then include it in your first end - to - end baseline. otherwise, only use unsupervised learning in your first attempt if the task yo... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org).pdf | 441 | Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org) | 0 |
the performance on the training set is acceptable. if performance on the training set is poor, the learning algorithm is not using the training data that is already available, so there is no reason to gather more data. instead, try increasing the size of the model by adding more layers or adding more hidden units to ea... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org).pdf | 441 | Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org) | 0 |
chapter 11. practical methodology formance on a test set. if the performance on the test set is also acceptable, then there is nothing left to be done. if test set performance is much worse than training set performance, then gathering more data is one of the most [UNK] solutions. the key considerations are the cost an... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org).pdf | 442 | Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org) | 0 |
to reduce the size of the model or improve regularization, by adjusting hyperparameters such as weight decay [UNK], or by adding regularization strategies such as dropout. if you find that the gap between train and test performance is still unacceptable even after tuning the regularization hyperparameters, then gatherin... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org).pdf | 442 | Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org) | 0 |
way to improve generalization error is to improve the learning algorithm itself. this becomes the domain of research and not the domain of advice for applied practitioners. 11. 4 selecting hyperparameters most deep learning algorithms come with many hyperparameters that control many aspects of the algorithm ’ s behavio... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org).pdf | 442 | Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org) | 0 |
chapter 11. practical methodology manually requires understanding what the hyperparameters do and how machine learning models achieve good generalization. automatic hyperparameter selection algorithms greatly reduce the need to understand these ideas, but they are often much more computationally costly. 11. 4. 1 manual... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org).pdf | 443 | Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org) | 0 |
constrained by three factors : the representational capacity of the model, the ability of the learning algorithm to successfully minimize the cost function used to train the model, and the degree to which the cost function and training procedure regularize the model. a model with more layers and more hidden units per l... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org).pdf | 443 | Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org) | 0 |
generalization error is high because the gap between training and test error is high. somewhere in the middle lies the optimal model capacity, which achieves the lowest possible generalization error, by adding a medium generalization gap to a medium amount of training error. for some hyperparameters, overfitting occurs ... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org).pdf | 443 | Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org) | 0 |
chapter 11. practical methodology because increasing the number of hidden units increases the capacity of the model. for some hyperparameters, overfitting occurs when the value of the hyperparame - ter is small. for example, the smallest allowable weight decay [UNK] of zero corresponds to the greatest [UNK] capacity of ... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org).pdf | 444 | Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org) | 0 |
or maximum value that prevents them from exploring some part of the curve. for example, the minimum weight decay [UNK] is zero. this means that if the model is underfitting when weight decay is zero, we can not enter the overfitting region by modifying the weight decay [UNK]. in other words, some hyperparameters can only... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org).pdf | 444 | Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org) | 0 |
this occurs if the learning rate is at least twice as large as its optimal value (, ). when the learning rate is too small, training lecun et al. 1998a is not only slower, but may become permanently stuck with a high training error. this [UNK] is poorly understood ( it would not happen for a convex loss function ). tun... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org).pdf | 444 | Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org) | 0 |
chapter 11. practical methodology 10−2 10−1 100 learning rate ( logarithmic scale ) 0 1 2 3 4 5 6 7 8 training error figure 11. 1 : typical relationship between the learning rate and the training error. notice the sharp rise in error when the learning is above an optimal value. this is for a fixed training time, as a sm... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org).pdf | 445 | Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org) | 0 |
) and the test error is primarily driven by the gap between train and test error. your goal is to reduce this gap without increasing training error faster than the gap decreases. to reduce the gap, change regularization hyperparameters to reduce [UNK] model capacity, such as by adding dropout or weight decay. usually t... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org).pdf | 445 | Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org) | 0 |
chapter 11. practical methodology hyperparameter increases capacity when... reason caveats number of hid - den units increased increasing the number of hidden units increases the representational capacity of the model. increasing the number of hidden units increases both the time and memory cost of essentially every op... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org).pdf | 446 | Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org) | 0 |
large increased time and mem - ory cost of most opera - tions. weight decay co - [UNK] decreased decreasing the weight de - cay [UNK] frees the model parameters to be - come larger dropout rate decreased dropping units less often gives the units more oppor - tunities to “ conspire ” with each other to fit the train - in... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org).pdf | 446 | Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org) | 0 |
chapter 11. practical methodology principle, this approach could fail due to optimization [UNK], but for many problems optimization does not seem to be a significant barrier, provided that the model is chosen appropriately. 11. 4. 2 automatic hyperparameter optimization algorithms the ideal learning algorithm just takes... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org).pdf | 447 | Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org) | 0 |
applied to similar tasks. however, for many applications, these starting points are not available. in these cases, automated algorithms can find useful values of the hyperparameters. if we think about the way in which the user of a learning algorithm searches for good values of the hyperparameters, we realize that an op... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org).pdf | 447 | Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org) | 0 |
usually easier to choose, in the sense that acceptable performance may be achieved on a wide range of tasks using the same secondary hyperparameters for all tasks. 11. 4. 3 grid search when there are three or fewer hyperparameters, the common practice is to perform grid search. for each hyperparameter, the user selects... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org).pdf | 447 | Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org) | 0 |
chapter 11. practical methodology grid random figure 11. 2 : comparison of grid search and random search. for illustration purposes we display two hyperparameters but we are typically interested in having many more. ( left ) to perform grid search, we provide a set of values for each hyperparameter. the search algorith... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org).pdf | 448 | Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org) | 0 |
case where only some hyperparameters have a significant influence on the result. in this illustration, only the hyperparameter on the horizontal axis has a significant [UNK]. grid search wastes an amount of computation that is exponential in the number of non - influential hyperparameters, while random search tests a uniqu... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org).pdf | 448 | Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org) | 0 |
chapter 11. practical methodology set error is then chosen as having found the best hyperparameters. see the left of figure for an illustration of a grid of hyperparameter values. 11. 2 how should the lists of values to search over be chosen? in the case of numerical ( ordered ) hyperparameters, the smallest and largest... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org).pdf | 449 | Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org) | 0 |
values of { −1, 0, 1 }. if the best value found is, then we underestimated the range in which the best 1 α lies and we should shift the grid and run another search with α in, for example, { 1, 2, 3 }. if we find that the best value of α is, then we may wish to refine our 0 estimate by zooming in and running a grid search... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org).pdf | 449 | Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org) | 0 |
11. 4. 4 random search fortunately, there is an alternative to grid search that is as simple to program, more convenient to use, and converges much faster to good values of the hyperparameters : random search (, ). bergstra and bengio 2012 a random search proceeds as follows. first we define a marginal distribution for ... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org).pdf | 449 | Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org) | 0 |
chapter 11. practical methodology unlike in the case of a grid search, one should not discretize or bin the values of the hyperparameters. this allows one to explore a larger set of values, and does not incur additional computational cost. in fact, as illustrated in figure, a 11. 2 random search can be exponentially mor... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org).pdf | 450 | Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org) | 0 |
a hyperparameter ( given values of the other hyperparameters ) would give the same result. in the case of grid search, the other hyperparameters would have the same values for these two runs, whereas with random search, they would usually have [UNK] values. hence if the change between these two values does not marginal... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org).pdf | 450 | Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org) | 0 |
this gradient (, ;, ;, bengio et al. 1999 bengio 2000 maclaurin et al. 2015 ). unfortunately, in most practical settings, this gradient is unavailable, either due to its high computation and memory cost, or due to hyperparameters having intrinsically non - [UNK] interactions with the validation set error, as in the cas... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org).pdf | 450 | Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org) | 0 |
chapter 11. practical methodology for which there is high uncertainty, which may lead to a large improvement but may also perform poorly ) and exploitation ( proposing hyperparameters which the model is confident will perform as well as any hyperparameters it has seen so far — usually hyperparameters that are very simil... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org).pdf | 451 | Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org) | 0 |
hyperparameter optimization is an important field of research that, while often driven primarily by the needs of deep learning, holds the potential to benefit not only the entire field of machine learning but the discipline of engineering in general. one drawback common to most hyperparameter optimization algorithms with ... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org).pdf | 451 | Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org) | 0 |
promising, or to “ thaw ” and resume an experiment that was earlier frozen but now appears promising given more information. 11. 5 debugging strategies when a machine learning system performs poorly, it is usually [UNK] to tell whether the poor performance is intrinsic to the algorithm itself or whether there is a bug ... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org).pdf | 451 | Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org) | 0 |
chapter 11. practical methodology neural network on a classification task and it achieves 5 % test error, we have new no straightforward way of knowing if this is the expected behavior or sub - optimal behavior. a further [UNK] is that most machine learning models have multiple parts that are each adaptive. if one part ... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org).pdf | 452 | Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org) | 0 |
distribution of the input, the weights may be able to adapt to compensate for the negative biases. most debugging strategies for neural nets are designed to get around one or both of these two [UNK]. either we design a case that is so simple that the correct behavior actually can be predicted, or we design a test that ... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org).pdf | 452 | Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org) | 0 |
##d you into believing your system is performing well when it is not. visualize the worst mistakes : most models are able to output some sort of confidence measure for the task they perform. for example, classifiers based on a softmax output layer assign a probability to each class. the probability assigned to the most l... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org).pdf | 452 | Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org) | 0 |
chapter 11. practical methodology but they are somewhat useful in the sense that examples that are actually less likely to be correctly labeled receive smaller probabilities under the model. by viewing the training set examples that are the hardest to model correctly, one can often discover problems with the way the da... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org).pdf | 453 | Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org) | 0 |
obtained from the train and test error. if training error is low but test error is high, then it is likely that that the training procedure works correctly, and the model is overfitting for fundamental algorithmic reasons. an alternative possibility is that the test error is measured incorrectly due to a problem with sa... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org).pdf | 453 | Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org) | 0 |
##es of the output layer correctly. usually if you cannot train a classifier to correctly label a single example, an autoencoder to successfully reproduce a single example with high fidelity, or a generative model to consistently emit samples resembling a single example, there is a software defect preventing successful o... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org).pdf | 453 | Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org) | 0 |
chapter 11. practical methodology is to compare the derivatives computed by your implementation of automatic [UNK] to the derivatives computed by a. because finite [UNK] f ( ) = lim x →0 f x f x ( + ) − ( ), ( 11. 5 ) we can approximate the derivative by using a small, finite : f ( ) x ≈f x f x ( + ) − ( ). ( 11. 6 ) we ... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org).pdf | 454 | Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org) | 0 |
a time. we can either run finite [UNK] mn times to evaluate all of the partial derivatives of g, or we can apply the test to a new function that uses random projections at both the input and output of g. for example, we can apply our test of the implementation of the derivatives to f ( x ) where f ( x ) = ut g ( vx ), w... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org).pdf | 454 | Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org) | 0 |
. the method is based on the observation that f x i f x if ( + ) = ( ) + ( ) + ( x o 2 ) ( 11. 8 ) real ( ( + ) ) = ( ) + ( f x i f x o 2 ) imag (, f x i ( + ) ) = f ( ) + ( x o 2 ), ( 11. 9 ) where i = √−1. unlike in the real - valued case above, there is no cancellation [UNK] due to taking the [UNK] between the value... | /home/ricoiban/GEMMA/mnlp_chatsplaining/RAG/Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org).pdf | 454 | Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville (z-lib.org) | 0 |
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