readme.md Deep Learning Due: 12. October (23:55) Overview For gaining a better understanding of deep learning, we’re going to be looking at deep averaging networks. These are a very simple framework, but they work well for a variety of tasks and will help introduce some of the core concepts of using deep learning in practice. […]
COMP9444 Neural Networks and Deep Learning Session 2, 2018 Project 2 – Recurrent Networks and Sentiment Classification Due: Sunday 23 September, 23:59 pm Marks: 15% of final assessment Introduction You should now have a good understanding of the internal dynamics of TensorFlow and how to implement, train and test various network architectures. In this assignment
Food Classification Challenge All assignment work must use the Python/Keras CIPA1 development environment. Deliverables The main purpose of this assignment is to introduce the student to the practical aspects of developing advanced deep learning based computer vision systems within the Python/Keras environment. The assignment is worth 40% of your overall module mark2.
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Introduction COMP9444 Neural Networks and Deep Learning Session 2, 2017 Project 2 – Recurrent Networks and Sentiment Classification Due Dates: Stage 1: Sunday 8 October, 23:59 pm Stage 2: Sunday 15 October, 23:59 pm Marks: 15% of final assessment You should now have a good understanding of the internal dynamics of TensorFlow and how to
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MLP Courseworks 3 & 4 Due: 2017-02-16 (cw3); 2017-03-16 (cw4) Machine Learning Practical: Courseworks 3 & 4 Release date Friday 27 January 2017 Due dates 1. Baseline experiments (Coursework 3) – 16:00 Thursday 16th February 2017 2. Advanced experiments (Coursework 4) – 16:00 Thursday 16th March 2017 1 Introduction Courseworks 3 & 4 in MLP
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Introduction COMP9444 Neural Networks and Deep Learning Session 2, 2017 Project 3 – Deep Reinforcement Learning Due: Sunday 29 October, 23:59 pm Marks: 15% of final assessment In this assignment we will implement a Deep Reinforcement Learning algorithm on some classic control tasks in the OpenAI AI-Gym Environment. Specifically, we will implement Q-Learning using a
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COMP9444 Neural Networks and Deep Learning Session 2, 2017 Project 2 – Recurrent Networks and Sentiment Classification Due: Sunday 8 October, 23:59 pm Marks: 15% of final assessment Introduction You should now have a good understanding of the internal dynamics of TensorFlow and how to implement, train and test various network architectures. In this assignment
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MLP Coursework 2 Due: 24 November 2016 Machine Learning Practical: Coursework 2 Release date: Wednesday 2nd November 2016 Due date: 16:00 Thursday 24th November 2016 Introduction The aim of this coursework is to use a selection of the techniques covered in the course so far to train accurate multi-layer networks for MNIST classification. It is
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#| ;;;;;; Assignment 4 ; Write a two-layer backpropagation neural network. ; ; Due THURSDAY, October 20, at MIDNIGHT. ; Note that the 580 students must implement one additional function (K-fold validation). I have decided to provide the list version of this code rather than the array version (the array version is tougher to write
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1) Boosting (3 points) The AdaBoost algorithm is described in the paper “A Brief Introduction to Boosting” by Robert Schapire. You have been provided this paper on the course website. This algorithm learns from a training set of input/output instances{(x1, y1),…,(xm , ym )}where X is an arbitrary set of inputs, xi ∈ X and
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