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
CS计算机代考程序代写 python deep learning Keras cache algorithm Hive COSC2779LabExercises_W6_1 Read More »
Tutorial Questions | Week 7 COSC2779 – Deep Learning This tutorial is aimed at reviewing practical methodology in developing CNN. Please try the questions before you join the session. The question below has similar structure to the case study questions that you can expect in the end semester test. 1. You have been hired by
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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 – Representation Learning Problems in Applying Deep Learning When applying deep learning in practice: Usually, deep network architectures are required for complex tasks. They have many parameters and need large labeled datasets to obtain good generalization. Generating large ”labeled” dataset for your task is di�cult. Time consuming: You may want to
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Deep Learning – COSC2779 – Practical methodology & Hyperparameter tuning Deep Learning – COSC2779 Practical methodology & Hyperparameter tuning Dr. Ruwan Tennakoon Aug 23, 2021 Reference: Chapter 11: Ian Goodfellow et. al., “Deep Learning”, MIT Press, 2016. Lecture 6 (Part 1) Deep Learning – COSC2779 Aug 23, 2021 1 / 28 Outline 1 Practical Methodology
Deep Learning – COSC2779 – Modelling Sequential (Time Series) Data Deep Learning – COSC2779 Modelling Sequential (Time Series) Data Dr. Ruwan Tennakoon Sep 6, 2021 Reference: Chapter 10: Ian Goodfellow et. al., “Deep Learning”, MIT Press, 2016. Lecture 7 (Part 1) Deep Learning – COSC2779 Sep 6, 2021 1 / 39 MAIA – AI for
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