COMP90042 Project 2021: Rumour Detection and Analysis on Twitter Copyright the University of Melbourne, 2021 Project type: Individual (non-group) Report and code submission due date: 9pm Thu, 13th May 2021 Codalab submission due date: 1pm Thu, 13th May 2021 (no extensions possible for this component) The concept of rumour has a long history, and it […]
Machine Learning 10-601/301 Tom M. Mitchell Machine Learning Department Carnegie Mellon University March 115, 2021 This section: • Convolutional neural nets • Recurrent neural nets • LSTMs • Sequence to sequence models Reading: • optional: Mitchell: Chapter 4 • Note Mitchell book now downloadable Convolutional Neural Nets A Convolutional Neural Net for Handwritten Digit recognition:
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Machine Learning 10-601/301 Tom M. Mitchell Machine Learning Department Carnegie Mellon University March 17, 2021 This section: • LSTMs • Sequence to sequence models • Transformer models • Attention Readings: optional but recommended • “Dive into Deep Learning” chapters 6.6, 8-8.4, 10.3-10.7 • This book is a free download on the web, and contains running
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Practical Week 05 An End-to-End Text Classification System¶ In this workshop you will implement a text classification system from scratch. This means that we will not rely on Keras’ convenient data sets. These data sets are pre-processed and it will be useful if you know how to tokenise and find the word indices of text
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Practical Week 04 Workshop Week 4¶ Deep Learning for Name Gender Classification¶ We have already seen the following code for partitioning the data of name gender classification and feature extraction. The code is changed slightly so that the labels are numerical (0 for male, 1 for female). This is the format required for Keras: In [1]:
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W06L1-2-Transformers 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,
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An End-to-End Text Classification System¶ In this workshop you will implement a text classification system from scratch. This means that we will not rely on Keras’ convenient data sets. These data sets are pre-processed and it will be useful if you know how to tokenise and find the word indices of text collections not provided
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Classifying movie reviews: a binary classification example¶ This notebook is based on the code samples found in Chapter 3, Section 5 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′,
W06L1-1-Generation Text generation with LSTM¶ This notebook is based on the code samples found in Chapter 8, 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:XLA_GPU:0′,
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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’), PhysicalDevice(name=’/physical_device:XLA_GPU:0′,
CS计算机代考程序代写 python GPU deep learning Keras Understanding recurrent neural networks¶ Read More »