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 Tuesday 21st March 2017 (deadline extended) 1 Introduction Courseworks 3 & 4 […]
ShuffleNet: An Extremely Efficient Convolutional Neural Network for Mobile Devices Xiangyu Zhang∗ Xinyu Zhou∗ Mengxiao Lin Jian Sun Megvii Inc (Face++) {zhangxiangyu,zxy,linmengxiao,sunjian}@megvii.com Abstract We introduce an extremely computation efficient CNN architecture named Shuf- fleNet, designed specially for mobile devices with very limited computing power (e.g., 10-150 MFLOPs). The new architecture utilizes two proposed operations, pointwise
2_fullyconnected Deep Learning¶ Assignment 2¶ Previously in 1_notmnist.ipynb, we created a pickle with formatted datasets for training, development and testing on the notMNIST dataset. The goal of this assignment is to progressively train deeper and more accurate models using TensorFlow. In [0]: # These are all the modules we’ll be using later. Make sure you can
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ECE 657A: Classification – Lecture 8: Neural Networks and Deep Learning ECE 657A: Classification Lecture 8: Neural Networks and Deep Learning Mark Crowley March 1, 2017 Mark Crowley ECE 657A : Lecture 8 March 1, 2017 1 / 85 Class Admin Announcements Today’s Class Announcements Linear and Logistic Regression Multilayer Perceptrons Deep Learning Decision Trees,
Chapter 1 Introduction Comment by B: Thank you for the opportunity to assist you with this project. Overall, I found this extremely well written (i.e., in the PDF). However, I worked on improving the writing by eliminating any errors in grammar, spelling, and punctuation and by refining word choice and sentence structure. As ever, please
6_lstm Deep Learning¶ Assignment 6¶ After training a skip-gram model in 5_word2vec.ipynb, the goal of this notebook is to train a LSTM character model over Text8 data. In [0]: # These are all the modules we’ll be using later. Make sure you can import them # before proceeding further. from __future__ import print_function import os import
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Dynamical Systems and Deep Learning Coursework II You can work in groups of two or individually on your own. This is a practical investigation into training restricted Boltzmann ma- chines (RBM) and performing a simple classification task. You should sub- mit your work electronically as specified below in the problem. Description of the dataset The
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Preliminaries What we will discuss: • Programming languages and the process of programming. • Criteria for the design and evaluation of programming languages • Basic ideas of programming language implementations. S. Spakowicz, N. Japkowicz, R. Falcon CSI 3120, Preliminaries, page 1 Programming languages and the process of programming Points to discuss: – Programming means more
Dynamical Systems and Deep Learning Coursework II You can work in groups of two or individually on your own. This is a practical investigation into training restricted Boltzmann ma- chines (RBM) and performing a simple classification task. You should sub- mit your work electronically as specified below in the problem. Description of the dataset The
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ECBM E4040 Neural Networks and Deep Learning – 2016 Fall HOMEWORK #3 INSTRUCTIONS: This homework has only the programming component. Please submit your homework via your bitbucket repository. Your submission should consist of 1. Completed code in hw3.py file, 2. and a Jupyter Notebook file ecbm_e4040_hw3.ipynb. Important Note: This homework is to be completed individually.