Deep Learning – COSC2779 – Deep Learning Hardware and software Deep Learning – COSC2779 Deep Learning Hardware and software Dr. Ruwan Tennakoon July 26, 2021 Lecture 2 (Part 2) Deep Learning – COSC2779 July 26, 2021 1 / 19 Why Now? Big Data Larger Data sets. Easier collection and storage. Computation Graphic Processing Units. Massively […]
Deep Learning – COSC2779 – Introduction to Deep Learning Deep Learning – COSC2779 Introduction to Deep Learning Dr. Ruwan Tennakoon July 19, 2021 Lecture 1 (Part 1) Deep Learning – COSC2779 July 19, 2021 1 / 16 Outline 1 Introduction: Teaching Team 2 Course Overview 3 Introduction to Deep Learning 4 Review of Machine Learning
Deep Learning – COSC2779 – Regularization Deep Learning – COSC2779 Regularization Dr. Ruwan Tennakoon August 2, 2021 Reference: Chapter 7: Ian Goodfellow et. al., “Deep Learning”, MIT Press, 2016. Lecture 3 (Part 2) Deep Learning – COSC2779 August 2, 2021 1 / 13 Generalization Gap Image: Goodfellow, 2016. Simpler functions are more likely to generalize,
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Tutorial Questions | Week 5 COSC2779 – Deep Learning This tutorial is aimed at reviewing convolutions in deep learning. Please try the questions before you join the session. 1. What is the output of the following convolution operation? 5 5 5 5 5 5 5 5 5 5 5 5 5 5 1 1 5
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Tutorial Questions | Week 5 COSC2779 – Deep Learning This tutorial is aimed at reviewing convolutions in deep learning. Please try the questions before you join the session. 1. What is the output of the following convolution operation? 5 5 5 5 5 5 5 5 5 5 5 5 5 5 1 1 5
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Deep Learning – COSC2779 – Neural Network Optimization Deep Learning – COSC2779 Neural Network Optimization Dr. Ruwan Tennakoon August 2, 2021 Reference: Chapter 7,8: Ian Goodfellow et. al., “Deep Learning”, MIT Press, 2016. Lecture 3 (Part 1) Deep Learning – COSC2779 August 2, 2021 1 / 56 Outline Part 1: Optimization Techniques 1 Loss Function
absl-py==0.7.1 aiohttp==3.4.4 alabaster==0.7.12 allennlp==0.7.1 appnope==0.1.0 argcomplete==1.9.4 argh==0.26.2 asn1crypto==0.24.0 astor==0.8.0 astroid==1.6.5 async-timeout==3.0.1 atomicwrites==1.2.1 attrs==18.2.0 aws-xray-sdk==0.95 awscli==1.16.43 Babel==2.6.0 backcall==0.1.0 backports.csv==1.0.7 beautifulsoup4==4.6.3 bleach==3.0.2 boto==2.49.0 boto3==1.9.33 botocore==1.12.33 bottle==0.12.9 bs4==0.0.1 certifi==2020.4.5.1 cffi==1.11.5 chardet==3.0.4 cheroot==8.3.0 CherryPy==18.5.0 click==7.1.2 codalab==0.5.13 codecov==2.0.15 colorama==0.3.9 conllu==0.11 cookies==2.2.1 coverage==4.5.1 cryptography==2.3.1 cycler==0.10.0 cymem==2.0.2 cytoolz==0.9.0.1 dataclasses==0.7 decorator==4.3.0 defusedxml==0.5.0 Deprecated==1.2.7 diffimg==0.2.3 dill==0.2.8.2 docker==3.7.0 docker-pycreds==0.4.0 docopt==0.6.2 docutils==0.14 docx==0.2.4 ecdsa==0.13 editdistance==0.5.2 eli5==0.8
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Deep Learning – COSC2779 – Convolutional Neural Networks Deep Learning – COSC2779 Convolutional Neural Networks Dr. Ruwan Tennakoon August 9, 2021 Reference: Chapter 9: Ian Goodfellow et. al., “Deep Learning”, MIT Press, 2016. Lecture 4 (Part 1) Deep Learning – COSC2779 August 9, 2021 1 / 42 Outline 1 Motivation 2 2D Convolution in Traditional
COSC2779LabExercises_W2_3_solutions ¶ COSC 2779 | Deep Learning ¶ Week 1-2 Lab Exercises: **Introduction to Tensorflow** ¶ introduction¶ This lab is aimed at introducing the fundamentals of TensorFlow 2.0. The Lab is organized into six sub modules: Minimal example: 2D Linear regression. Exercise: Classifying hand written text MNIST. Advanced TensorFlow example: Understanding gradient tape and writing
COSC2779LabExercises_W09_BERT ¶ COSC 2779 | Deep Learning ¶ Week 9 Lab Exercises: **Classify text – BERT** ¶ Introduction¶ In this tutorial, you will learn how to classify text by using transfer learning from a BERT model. A pre-trained model is a saved network that was previously trained on a large dataset, typically on a large-scale
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