COSC2779LabExercises_W6_2 ¶ COSC 2779 | Deep Learning ¶ Week 6 Lab Exercises: **Transfer Learning** ¶ Introduction¶ In this tutorial, you will learn how to classify images of cats and dogs by using transfer learning from a pre-trained network. A pre-trained model is a saved network that was previously trained on a large dataset, typically on […]
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13-cnn Qiuhong Ke Convolutional Neural Networks COMP90051 Statistical Machine Learning Copyright: University of Melbourne vs 2 Multi-layer perceptron: A fully connected network 9×9 81×1 !! !” !#! “! #! “$!… … … Input layer Hidden layer Output layer Consists of only fully connected (FC) layers 3 Disadvantage: Not spatial invariant ≠ … … Multi-layer perceptron:
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worksheet08_solutions COMP90051 Workshop 8¶ Convolutional Neural Networks¶ In this worksheet, we’ll implement a convolutional neural network (CNN) in Keras—a high-level API for deep learning. Since this is our first time using Keras, we’ll start by implementing logistic regression—a familiar model from workshop 4. We’ll then extend logistic regression to build a CNN by adding 2D
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15_generative-model Qiuhong Ke Generative models COMP90051 Statistical Machine Learning “What I Cannot Create, I Do Not Understand” ——Richard Feynman Copyright: University of Melbourne So far.. Classifier: • SVM • Perceptron • Multi-layer perceptron • CNN Feature extraction: • Pretrained CNN model 2 Using pretrained model in Keras Feature extraction before the last classifier 3 Using
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worksheet07_solutions COMP90051 Workshop 7¶ Recurrent neural networks (RNNs)¶ In this worksheet, we’ll implement a recurrent neural network (RNN) for sentiment analysis of movie reviews. The input to the network will be a movie review, represented as a string, and the output will be a binary label which is “1” if the sentiment is positive and
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worksheet09_solutions COMP90051 Workshop 9¶ Variational autoencoders (VAEs)¶ In this worksheet, we’ll implement a variational autoencoder (VAE) as introduced by Kingma & Welling (2013). We’ll use an independent Bernoulli likelihood for the data $\mathbf{x}$, with a spherical Gaussian prior on the latent variable $\mathbf{z}$. We’ll adopt a convolutional architecture for the encoder/decoder neural nets, which is
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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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l1-intro-v2 COPYRIGHT 2021, THE UNIVERSITY OF MELBOURNE 1 Course Overview & Introduction COMP90042 Natural Language Processing Lecture 1 Semester 1 2021 Week 1 Jey Han Lau COMP90042 L1 2 Prerequisites • COMP90049 “Introduction to Machine Learning” or COMP30027 “Machine Learning” ‣ Modules → Welcome → Machine Learning Readings • Python programming experience • No
07-deep-learning Deep Learning with keras¶ In this workshop, we will try to build some feedforward models to do sentiment analysis, using keras, a deep learning library: https://keras.io/ You will need pandas, keras (2.3.1) and tensorflow (2.1.0; and their dependencies) to run this code (pip install pandas keras==2.3.1 tensorflow-cpu==2.1.0). First let’s prepare the data. We are
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11-machine-translation Neural Machine Translation¶ In this workshop, we are going to build a seq2seq machine translation model and train it on a parallel corpus of English and French. We will frame the translation problem in a slightly different way. Instead of translating the sentence word by word, we are going to work on character-level. This
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