Python广泛应用于机器学习, 人工智能和统计数据分析等课程. 它也被很多大学作为入门语言来教授. 目前是我们代写最多的编程语言.
Practical Week 02 Demonstration¶ The following demonstration will use the training set of the OHSUMED corpus. This training set was used in the Filtering Track of the 9th edition of the Text REtrieval Conference (TREC-9). We will use it for the information retrieval exercises of this workshop. Download ohsumed.zip into the same folder as this […]
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Stop Words¶ In [1]: import nltk In [2]: nltk.download(‘stopwords’) from nltk.corpus import stopwords [nltk_data] Downloading package stopwords to /home/diego/nltk_data… [nltk_data] Package stopwords is already up-to-date! In [3]: stop = stopwords.words(‘english’) stop[:5] Out[3]: [‘i’, ‘me’, ‘my’, ‘myself’, ‘we’] Zipf’s Law¶ In [4]: %matplotlib inline nltk.download(‘gutenberg’) import nltk import collections import matplotlib.pyplot as plt words = nltk.corpus.gutenberg.words() fd = collections.Counter(words) data
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W05L1-1-WordEmbeddings Using word embeddings¶ This notebook is based on the code samples found in Chapter 6, Section 1 of Deep Learning with Python and hosted on https://github.com/fchollet/deep-learning-with-python-notebooks. Note that the original text features far more content, in particular further explanations and figures. In [1]: import tensorflow as tf tf.config.experimental.list_physical_devices() Out[1]: [PhysicalDevice(name=’/physical_device:CPU:0′, device_type=’CPU’), PhysicalDevice(name=’/physical_device:XLA_CPU:0′, device_type=’XLA_CPU’), PhysicalDevice(name=’/physical_device:GPU:0′, device_type=’GPU’),
Challenges of Text for Machine Learning Word Embeddings Text Sequences COMP3220 — Document Processing and the Semantic Web Week 05 Lecture 1: Processing Text Sequences Diego Moll ́a Department of Computer Science Macquarie University COMP3220 2021H1 Diego Moll ́a W05L1: Text Sequences 1/28 Challenges of Text for Machine Learning Word Embeddings Text Sequences Programme 1
W05L1-2-RNN Understanding recurrent neural networks¶ This notebook is based on code samples found in Chapter 6, Section 2 of Deep Learning with Python and hosted on https://github.com/fchollet/deep-learning-with-python-notebooks. Note that the original text features far more content, in particular further explanations and figures. In [1]: import tensorflow as tf tf.config.experimental.list_physical_devices() Out[1]: [PhysicalDevice(name=’/physical_device:CPU:0′, device_type=’CPU’), PhysicalDevice(name=’/physical_device:XLA_CPU:0′, device_type=’XLA_CPU’), PhysicalDevice(name=’/physical_device:GPU:0′, device_type=’GPU’),
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Information Retrieval Evaluation Indexing and Retrieval COMP3220 — Document Processing and the Semantic Web Week 02 Lecture 1: Searching for Information Diego Moll ́a Department of Computer Science Macquarie University COMP3220 2021H1 Diego Moll ́a W02L1: Search 1/53 Information Retrieval Evaluation Indexing and Retrieval Programme 1 Information Retrieval 2 Evaluation Precision and Recall 3 Indexing
Using word embeddings¶ This notebook is based on the code samples found in Chapter 6, Section 1 of Deep Learning with Python and hosted on https://github.com/fchollet/deep-learning-with-python-notebooks. Note that the original text features far more content, in particular further explanations and figures. In [1]: import tensorflow as tf tf.config.experimental.list_physical_devices() Out[1]: [PhysicalDevice(name=’/physical_device:CPU:0′, device_type=’CPU’), PhysicalDevice(name=’/physical_device:XLA_CPU:0′, device_type=’XLA_CPU’), PhysicalDevice(name=’/physical_device:GPU:0′, device_type=’GPU’), PhysicalDevice(name=’/physical_device:XLA_GPU:0′,
Huggingface’s transformers library¶ Parts of this code based on https://huggingface.co/transformers/quickstart.html and https://github.com/strongio/keras-bert/blob/master/keras-bert.ipynb Huggingface’s transformers library is a very popular library that contains some of the latest complex architectures based on the Transformer. This library is used by an increasing number of developers and researchers to produce state-of-the-art results in multiple tasks. In this notebook, we
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W03L1TextClassification Gender Classification¶ The following example shows how NLTK uses features for gender classification of names. In [1]: import nltk nltk.download(“names”) from nltk.corpus import names [nltk_data] Downloading package names to /home/diego/nltk_data… [nltk_data] Package names is already up-to-date! In [2]: m = names.words(‘male.txt’) len(m) Out[2]: 2943 In [3]: f = names.words(‘female.txt’) len(f) Out[3]: 5001 Creation of a training set
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Basic Python¶ Beginning Python¶ In [1]: def hello (who): # 1 “””Greet somebody””” # 2 print(“Hello ” + who + “!”) # 3 1 Defines a new function/procedure called hello which takes a single argument. Note that python variables are not typed, who could be a string, integer, array … The line ends with a colon
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