algorithm (CNN, RNN, etc.,) can be represented in a simple and efficient manner. The following diagram depicts the relationship between model, layer and core modules: Let us see the overview of Keras suitable for machine learning. We can use it in data preparation phase of machine learning.  Sequence processing: Provides functions to generate time based data from the given input data. We can use >>> model = Sequential() # apply a unshared weight convolution 1-dimension of length 3 to a sequence with # 10 timesteps, with 16 output filters >>> model.add(LocallyConnected1D(16, 3, input_shape=(10

'Gumbel', 'HalfCauchy', 'HalfNormal', (continues on next page) 43 https://en.wikipedia.org/wiki/Venn_diagram 44 https://en.wikipedia.org/wiki/Markov_chain 45 https://discuss.d2l.ai/t/1762 2.7. 查阅文档 81 (continued scalar value 1, with the shape defined by the variable argument size. Args: size (int...): a sequence of integers defining the shape of the output tensor. Can be a variable number of arguments or a Discussions112 9.5 机器翻译与数据集 语言模型是自然语言处理的关键，而机器翻译是语言模型最成功的基准测试。因为机器翻译正是将输入序列 转换成输出序列的 序列转换模型（sequence transduction）的核心问题。序列转换模型在各类现代人工智能 应用中发挥着至关重要的作用，因此我们将其做为本章剩余部分和 10节的重点。为此，本节将介绍机器翻译 问题及其后文需要使用的数据集。

accuracy. Hence, this is a trade-off. We also ensure that the tokenized input results in an integer sequence with exactly 250 tokens. This might mean padding short texts with padding tokens and truncating tokenize, by truncating # the rest of the sequence. max_seq_len = 100 vectorization_layer = tf.keras.layers.TextVectorization( max_tokens=vocab_size, output_sequence_length=max_seq_len) Once we have initialized are confident will not be in the vocabulary. edl_sequence_output = vectorization_layer( [['efficient deep learning x123!']]).numpy()[0, :4] edl_sequence_output array([ 1, 1379, 1585, 1]) The code snippet

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 123 6.2.4 text_to_word_sequence . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 123 6.3 图像预处理 . . . . . . . . . . . HDF5Matrix [source] . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 236 20.3 Sequence [source] . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 236 20.4 to_categorical 这是基于之前定义的视觉模型（权重被重用）构建的视频编码 encoded_frame_sequence = TimeDistributed(vision_model)(video_input) # 输出为向量的序列 encoded_video = LSTM(256)(encoded_frame_sequence) # 输出为一个向量 # 这是问题编码器的模型级表示，重复使用与之前相同的权重：

Language Toolkit (NLTK) and creates a text sequence from a sentence. from random import choice, randint from keras.preprocessing.text import text_to_word_sequence # NLTK Import try: from nltk.corpus import choice(synonyms(word) or [word]) original = 'We enjoyed our short vacation in Mexico' words = text_to_word_sequence(original) # Tokenize the sentence. Now, let’s go through the different text transformations with inserted. """ It inserts a synonym for every candidate word at a random position in the word sequence. """ def ins_transformation(words, candidates): for candidate in candidates: pos = randint(0

natural language inputs. Then by definition the model should be able to encode the given text in a sequence of embeddings such that there is some semantic relationship preserved between pieces of text that intractable. See figure 6-2 for a general theme that these tasks follow. If you consider to be a sequence that you can create from your unlabeled dataset, a few simple pretext tasks can be to predict the of the pretext task. This works well for domains like natural language where your data will be a sequence of tokens. You can extend the analogy to being a tensor of rank , and hide part of the input and

which eliminates ambiguity when decoding), we can easily construct the original sequence of symbols from the encoded sequence and the lookup table. Refer the wikipedia article on arithmetic coding to learn to a low precision domain. The following exercise will apply them to quantize an arbitrary data sequence. Exercise: Data Quantization Let's put our learnings from the previous exercise into practice.

时间序列表示 主讲人：龙良曲 Spatial Signals Temporal Signals? Sequence http://slazebni.cs.illinois.edu/spring17/lec02_rnn.pdf Sequence representation ▪ [seq_len, feature_len] [100, 1] [28, 28] [words, word_vec]

Classification Matching Wordpiece Keras tokenizer Jieba Hanlp Model Saving Deployment Testing Vocab Sequence labeling Huawei tokenizer word2vec Elmo pb ckpt H5 (Keras) RESTful API RPC API Function --->simple Char replacement Synonym replacement Char filter Featurizer Classification/ Matching/ Sequence labeling TF model Sklearn model feature Countvectorizer Sentence encoder char ... Stop word

value of weight decay. • --adam_beta2: the value of β2 in Adam. • --model_max_length: the maximum sequence length. • --use_lora: whether to use LoRA. Adding --q_lora can enable Q-LoRA. • --gradient_checkpointing: field(default="adamw_torch") model_max_length: int = field( default=8192, metadata={ "help": "Maximum sequence length. Sequences will be right padded (and␣ �→possibly truncated)." }, ) use_lora: bool = False