Approximate Computing for Deep Learning in TensorFlow Abstract Nowadays, many machine learning techniques are applied on the smart phone to do things like image classificatin, audio recognization and object detection to make smart phone even smarter. Since deep learning has achieved the best result in many fields. More and more people want to use deep […]
3_regularization Deep Learning¶ Assignment 3¶ Previously in 2_fullyconnected.ipynb, you trained a logistic regression and a neural network model. The goal of this assignment is to explore regularization techniques. 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 numpy
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Word2Vec_Demo COMP6714 Word Embeddings Demonstration using Tensorflow¶ In this notebook, we demonstrate a basic implementation of Word Embbeddings by training the skip-gram model using a small test corpus: Text8 (based on Tensorflow’s word2vec tutorial). It will provide you with hands on experience prior to COMP6714 Project2. The key part of word embeddings is the training
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MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications Andrew G. Howard Menglong Zhu Bo Chen Dmitry Kalenichenko Weijun Wang Tobias Weyand Marco Andreetto Hartwig Adam Google Inc. {howarda,menglong,bochen,dkalenichenko,weijunw,weyand,anm,hadam}@google.com Abstract We present a class of efficient models called MobileNets for mobile and embedded vision applications. MobileNets are based on a streamlined architecture that uses depth-
Approximate Computing for Deep Learning in TensorFlow Chiang Chi-An T H E U N I V E R S I T Y O F E D I N B U R G H Master of Science School of Informatics University of Edinburgh 2017 Abstract Nowadays, many machine learning techniques are applied on the smart phone
1_notmnist Deep Learning¶ Assignment 1¶ The objective of this assignment is to learn about simple data curation practices, and familiarize you with some of the data we’ll be reusing later. This notebook uses the notMNIST dataset to be used with python experiments. This dataset is designed to look like the classic MNIST dataset, while looking
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January 4, 2017 January 4, 2017 1 / 77 January 4, 2017 January 4, 2017 2 / 77 Today’s Class Part I Announcements Course Admin Course Overview motivation topics timelines Part II Understanding and Preparing Data for Analysis Basic definitions of data and how to manage, clean, analyse data at a high level. January 4,
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
Rethinking the Inception Architecture for Computer Vision Christian Szegedy Google Inc. szegedy@google.com Vincent Vanhoucke vanhoucke@google.com Sergey Ioffe sioffe@google.com Jonathon Shlens shlens@google.com Zbigniew Wojna University College London zbigniewwojna@gmail.com Abstract Convolutional networks are at the core of most state- of-the-art computer vision solutions for a wide variety of tasks. Since 2014 very deep convolutional networks started to
Deep Convolutional Neural Networks as Generic Feature Extractors Lars Hertel∗†, Erhardt Barth†, Thomas Käster†‡ and Thomas Martinetz† ∗Institute for Signal Processing, University of Luebeck, Germany Email: hertel@isip.uni-luebeck.de †Institute for Neuro- and Bioinformatics, University of Luebeck, Germany Email: {barth, kaester, martinetz}@inb.uni-luebeck.de ‡Pattern Recognition Company GmbH, Luebeck, Germany Abstract—Recognizing objects in natural images is an intricate problem