Final Project In this assignment you will synthesize your knowledge of web design into a new, professionally-oriented website. Potential topics include a portfolio, a social cause, a small business, and an expanded résumé. Research & Wireframing Once you’ve decided what your professionally-oriented website will be about, write a paragraph of at least 100 words describing […]
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COMP3331/9331 Practice Final Exam 1/37 Exam Information Cover Sheet COMP3331/9331— Computer Networks and Applications Term 2, 2021 Practice Final Examination Instructions: 1. TIME ALLOWED: 2 hours and 10 minutes (Reading Time). 2. TOTAL MARKS AVAILABLE: 40 marks worth 40% of the total marks for the course. You must score at least 16 marks on the
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Assignment 1 2021 FIT2102 Programming Paradigms 2021 Assignment 1: Functional Reactive Programming Due Date: 10/09/2021 Weighting: 30% of your final mark for the unit Overview. Students will work independently to create a classic arcade game using Functional Reactive Programming (FRP) techniques. Programs will be implemented in TypeScript and will use RxJS Observable streams to handle
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Levels of Abstraction Learning Outcomes · Understand the motivation for different programming paradigms: to abstract machine operation into human understandable and composable programs · Understand the difference between syntax the textual symbols and grammatical rules of a program, and semantics the meaning of what is computed · Understand that there are different models of computation upon which different programming
Assignment 1 2021 FIT2102 Programming Paradigms 2021 Assignment 1: Functional Reactive Programming Due Date: 10/09/2021 Weighting: 30% of your final mark for the unit Overview. Students will work independently to create a classic arcade game using Functional Reactive Programming (FRP) techniques. Programs will be implemented in TypeScript and will use RxJS Observable streams to handle
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5a: Recurrent Networks Week 5: Overview This week, we will explore the use of neural networks for sequence and language processing. Simple Recurrent Networks (SRN) can be trained to recognize or predict formal languages, and we can analyse their hidden unit dynamics. By the use of a gating mechanism, Long Short Term Memory (LSTM) and
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1a: Neuroanatomy and Perceptrons Week 1: Overview In this �rst week, we will look at the historical background of arti�cial intelligence and deep learning, biological and arti�cial neurons, the perceptron learning algorithm, and the training of multi-layer neural networks by gradient descent. Weekly learning outcomes By the end of this module, you will be able
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7b: Language Processing Discussion: Translation, Transformers and ChatBots Attention Mechanism and Neural Machine Translation These articles explain how an attention mechanism can be used for sequence-to-sequence prediction, and how stacked LSTMs, combined with attention and word vectors, can be used for multi-lingual neural machine translation: Cho, K., 2015. Introduction to Neural Machine Translation with GPUs
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9b: Autoencoders and Adversarial Training Autoencoders The encoder networks we met in Week 2 can be seen as a simple example of a much wider class of Autoencoder Networks, consisting of an Encoder which converts each input to a vector of latent variables , and a Decoder which converts the latent variables to output .
COMP 302: Programming Languages and Paradigms Week 1: Basic Intro to OCaml Prof. Xujie Si A brief history of OCaml Copyright By PowCoder代写 加微信 powcoder A brief history of OCaml • Meta-language (ML), 1970s • Designed for developing theorem provers • Logic for Computable Functions (LCF) theorem-proving project • Standard ML, 1980s • Caml, 1980s
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