ports)有 关。我们的训练数据有5句话： 文本 标签 A great game Sports The election was over Not Sports Very clean match Sports A clean but forgettable game Sports It was a close election Not Sports 我们想要计算句子“A very close 的概率为⅗。 ?(Not Sports )是⅖。 文本 标签 A great game Sports The election was over Not Sports Very clean match Sports A clean but forgettable game Sports It was a close election Not Sports 23 3.朴素贝叶斯案例 'very', 'over', 'it', 'but', 'game', 'election', 'clean', 'close', 'the', 'was', 'forgettable', 'match']。 由于可能的单词数是14，因此应用平滑处理可以得到 ?( game | sports ) = 2+1 11+14 14个单词 24 3.朴素贝叶斯案例 拉普拉斯平滑是一种用于平

frequency of data. Clustering helps solve that problem by adapting the allocation of precision to match the distribution of the data, which ensures the decoded value deviates less from the original value networks on a variety of web, mobile, and embedded devices, provided the user can design networks that match their constraints. One might wonder what are the drawbacks of structured sparsity? It’s easy: with lies a smaller network which can be extracted without using the fine-tuned weights and retrained to match or exceed the performance of the larger network. Liu et al. in their work titled "Rethinking the Value

[5.0] ▪ 2. memory consumption ▪ [4, 32, 8] => 1024 ▪ [5.0] => 1 Is it broadcasting-able? ▪ Match from Last dim! ▪ If current dim=1, expand to same ▪ If either has no dim, insert one dim and expand ▪ When it has dim of size 1 ▪ Treat it shared by all ▪ [class, student, scores] + [student, 1] match from last dim It's effective and critically, intuitive ▪ [4, 3, 32, 32] ▪ + [32, 32] ▪ + [3

a higher (2.42%) accuracy with fewer samples. Both the augmented and the baseline 500 runs almost match the sample efficiencies (baseline 500 achieves a higher accuracy one epoch sooner than the augmented distillation technique on single models as well as on ensemble of models. Hinton et al. were able to closely match the accuracy of an ensemble of ten models for a speech recognition task with a single distilled model

input dimension (D1 as stated earlier). The shape of the weights tensor is [3, 5], 3 is chosen to match D1, and 5 is D2. Bias is of shape [5]. The shapes are arbitrarily chosen for illustration purposes

=["‘ “ 「 『]{1,2}|$))') sent_list = [] for ele in sent_sep_pattern.split(text): if sent_sep_pattern.match(ele) and sent_list: sent_list[-1] += ele elif ele: sent_list.append(ele) return sent_list def

RuntimeError: The size of tensor a (3) must match the size of tensor b (2) at non-singleton dimension 3 # 错误 2 RuntimeError: The expanded size of the tensor (3) must match the existing size (2) at non-singleton 35,8]) torch.cat([a,b], dim=0) # 非法拼接，其他维度长度不相同 Out[3]: RuntimeError: Sizes of tensors must match except in dimension 0. Got 32 and 35 in dimension 1 堆叠 拼接操作直接在现有维度上合并数据，并不会创建新的维度。如果在合并数据 时，希望创建一个新的维度，则需要使用