Practical Week 06 Practical Week 6: Multi-label Classification¶ In this practical you will practice with multi-label classification, in preparation for assignment 2. In a multi-label classification task, the documents may have one or more labels (or sometimes none). An example of multi-label classification of images is to assign keywords to images. This blog post shows […]
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COMP3220 — Document Processing and the Semantic Web Week 06 L1: Advanced Topics in Deep Learning Diego Moll ́a COMP3220 2021H1 Abstract This is the final lecture on deep learning where we will introduce several advanced topics on deep learning for text processing. The emphasis here is on aspects related to the generation of text.
Text Generation Encoder-Decoder Architecture Pre-training and Fine-tuning COMP3220 — Document Processing and the Semantic Web Week 06 L1: Advanced Topics in Deep Learning Diego Moll ́a Department of Computer Science Macquarie University COMP3220 2021H1 Diego Moll ́a W06L1: Advanced Deep Learning 1/25 Text Generation Encoder-Decoder Architecture Pre-training and Fine-tuning Programme 1 Text Generation 2 Encoder-Decoder
COMP3220 — Document Processing and the Semantic Web Week 04 Lecture 1: Deep Learning for Text Classification Diego Moll ́a COMP3220 2021H1 Abstract Deep learning has recently achieved spectacular results in several text processing applications. In this lecture we will introduce the basics of deep learning and how it can be applied to text classification.
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′, device_type=’XLA_GPU’),
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Classifying newswires: a multi-class 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′, device_type=’XLA_CPU’),
COMP3220 — Document Processing and the Semantic Web Week 05 Lecture 1: Processing Text Sequences Diego Moll ́a COMP3220 2021H1 Abstract This lecture focuses on some of the key aspects of text that make it different, and difficult, from other unstructured data from the point of machine learning, and how deep learning handles them. The
COMP5329 – Deep Learning Assignment-1 1. Task description Based on the codes given in Tutorial: Multilayer Neural Network, you are required to accomplish a multi-class classification task on the provided dataset. In this assignment, you are expected to implement the modules specified in the marking table. You must guarantee that the submitted codes are self-complete,
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DAML Week 9, CP17: Lepton energy reconstruction in water Cˇerenkov detectors: NN Regression and Gradient Boosted Regression Trees 1 Introduction σ(p+ν ̄e →n+e+)≃5×10−44 * Christos.Leonidopoulos@ed.ac.uk cm2 Christos Leonidopoulos* University of Edinburgh March 14, 2021 In today’s lecture we are returning to regression problems. For the last CP of the course we will try to model