COSC2779LabExercises_W5_solutions-checkpoint ¶ COSC 2779 | Deep Learning ¶ Week 5 Lab Exercises: **Data loader and Augmentation** ¶ Introduction¶ This lab is aimed at understanding how to write your own data loader and learn the importance of data augmentation. During this lab you will: Learn to do data augmentation Explore different data loading mechanisms in TensorFlow […]
COSC2779LabExercises_W6_1 ¶ COSC 2779 | Deep Learning ¶ Week 6 Lab Exercises: **Practical methodology** ¶ Introduction¶ This lab is aimed at understanding how to develop a CNN from scratch. Will be based on solveing CIFAR 10 task. During this lab you will: Learn how to establish a base model. Debug the base model and setup
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Week03_Lecture ¶ COSC 2779 | Deep Learning ¶ Week 3 Lecture: **Neural Network Optimization** ¶ This script provides examples that show the inportance of selecting the correct: Activation Learning Rate Optimizer in your project. However, the results should not be used to make conclusuins about the elements involved. The most suitable element will depend n
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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
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_W4 ¶ COSC 2779 | Deep Learning ¶ Week 4 Lab Exercises: **Feed-forward Neural Networks** ¶ Introduction¶ This lab is aimed at understanding different elements and, debugging simple feed-forward neural networks. During this lab you will: Try different activations Try different models with varying capacities Try different optimisation techniques Experiment with regularisation This notebook is
COSC2779LabExercises_W5 ¶ COSC 2779 | Deep Learning ¶ Week 5 Lab Exercises: **Data loader and Augmentation** ¶ Introduction¶ This lab is aimed at understanding how to write your own data loader and learn the importance of data augmentation. During this lab you will: Learn to do data augmentation Explore different data loading mechanisms in TensorFlow
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COSC2779LabExercises_W4_solutions ¶ COSC 2779 | Deep Learning ¶ Week 4 Lab Exercises: **Feed-forward Neural Networks** ¶ Introduction¶ This lab is aimed at understanding different elements and, debugging simple feed-forward neural networks. During this lab you will: Try different activations Try different models with varying capacities Try different optimisation techniques Experiment with regularisation This notebook is
COSC2779LabExercises_W8 ¶ COSC 2779 | Deep Learning ¶ Week 8 Lab Exercises: **Classify text by using transfer learning from a pre-trained embedding** ¶ Introduction¶ In this tutorial, you will learn how to classify text by using transfer learning from a pre-trained embedding. A pre-trained model is a saved network that was previously trained on a
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COSC2779LabExercises_W7_solution Lab Exercises – Week: 7¶ COSC2779 – Deep Learning – 2020 This lab is aimed at understanding how to develop a simple RNN for sentiment classification. During this lab you will: Encode/Decode text data Develop a many-to-one RNN model for sentiment classification This notebook is designed to run on Google Colab. If you like
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