1b: Multi Layer Networks and Backpropagation Multi Layer Perceptrons Limitations of Perceptrons The main problem with Perceptrons is that many useful functions are not linearly separable. The simplest example of a logical function that is not linearly separable is the Exclusive OR or XOR function. Some languages have distinct words for inclusive and exclusive OR […]
2021/8/24 COMP9444 Project 1 www.cse.unsw.edu.au/~cs9444/21T2/hw1/ 1/5 COMP9444 Neural Networks and Deep Learning Term 2, 2021 Project 1 – Characters, Spirals and Hidden Unit Dynamics Due: Friday 16 July, 23:59 pm Marks: 30% of final assessment In this assignment, you will be implementing and training various neural network models for four different tasks, and analysing the
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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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2b: Cross Entropy, Softmax, Weight Decay and Momentum Cross Entropy and Softmax Loss Functions In Week 1 we introduced the sum squared error (SSE) loss function, which is suitable for function approximation tasks. E = (t − 2 1 i ∑ i z )i 2 However, for binary classi�cation tasks, where the target output is
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2a: Probability, Generalization and Over�tting Week 2: Overview In this module, we will brie�y review certain topics from probability which are essential for deep learning, and we will introduce the issue of generalization and over�tting in supervised learning. We will then discuss cross entropy and softmax, which are used for classi�cation tasks as alternatives to
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 .
3b: Hidden Unit Dynamics Hidden Unit Dynamics Encoder Networks The Encoder task is a simple supervised learning task which has been designed to help us understand the hidden unit dynamics of neural networks, and also serves as a simpli�ed version of the Autoencoders we will meet in Week 6. N −K−N For this task, the
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Machine Learning and Data Mining in Business Lecture 12: Recurrent Neural Networks Discipline of Business Analytics Copyright By PowCoder代写 加微信 powcoder Lecture 12: Recurrent Neural Networks Learning objectives • Recurrent neural networks • Gated recurrent units (GRU) • Long short-term memory (LSTM) Lecture 12: Recurrent Neural Networks 1. Sequence models 2. Text data 3. Recurrent
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COMP3620 / 6320 – S1 2022 This lecture will be recorded Turn off your camera if you do not want to be in the recording If you cannot see my whole slide, go to View Options -> Zoom Ratio and select Fit to Window Copyright By PowCoder代写 加微信 powcoder If you want to ask a
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