Deep Learning Europython 2016 - Bilbao G. French University of - PowerPoint PPT Presentation

Aug 06, 2023 •447 likes •2.37k views

Deep Learning Europython 2016 - Bilbao G. French University of East Anglia Image montages from http://www.image-net.org Focus: Mainly image processing This talk is more about the principles and the maths than code Got to fit this into 1

after 300 iterations over training set: 99.21% validation accuracy Model Error FC64 2.85% FC256--FC256 1.83% 20C5--MP2--50C5--MP2--FC256 0.79%
What about the learned kernels? Image taken from paper [Krizhevsky12] Gabor filters (ImageNet dataset, not MNIST)
Image taken from [Zeiler14]
Image taken from [Zeiler14]
Lasagne
Specifying your network as mathematical expressions is powerful but low-level
Lasagne is a neural network library built on Theano Makes building networks with Theano much easier
Provides API for: constructing layers of a network getting Theano expressions representing output, loss, etc.
Lasagne is quite a thin layer on top of Theano, so understanding Theano is helpful On the plus side, implementing custom layers, loss functions, etc is quite doable.
Intro to Theano and Lasagne slides: https://speakerdeck.com/britefury https://speakerdeck.com/britefury/intro-to-theano-and-lasagne-for-deep-learning
Notes for building and training neural networks
Neural network architecture (OxfordNet / VGG style)
Early part # Layer Input: 3 x 224 x 224 (RGB image, zero-mean) Blocks consisting of: 1 64C3 2 64C3 MP2 A few convolutional layers, often 3x3 3 128C3 kernels 4 128C3 - followed by - MP2 Down-sampling; max-pooling or 64C3 = 3x3 conv, 64 filters MP2 = max-pooling, 2x2 striding
# Layer Notation: Input: 3 x 224 x 224 (RGB image, zero-mean) 64C3 1 64C3 convolutional 2 64C3 layer with 64 3x3 MP2 filters 3 128C3 4 128C3 MP2 MP2 max-pooling, 2x2
# Layer Input: 3 x 224 x 224 (RGB image, zero-mean) Note 1 64 C3 2 64 C3 after down- MP2 sampling, double the number of 3 128 C3 4 128 C3 convolutional MP2 filters
# Layer Later part: Input: 3 x 224 x 224 (RGB image, zero-mean) After blocks of 1 64C3 convolutional and 2 64C3 down-sampling MP2 layers: 3 128C3 4 128C3 Fully-connected MP2 (a.k.a. dense) FC256 layers FC10
# Layer Input: 3 x 224 x 224 (RGB image, zero-mean) Notation: 1 64C3 2 64C3 FC256 MP2 fully-connected 3 128C3 layer with 256 4 128C3 channels MP2 FC256 FC10
# Layer Input: 3 x 224 x 224 Overall (RGB image, zero-mean) 1 64C3 Convolutional 2 64C3 layers detect MP2 feature in various 3 128C3 positions 4 128C3 throughout the MP2 image FC256 FC10
# Layer Input: 3 x 224 x 224 Overall (RGB image, zero-mean) 1 64C3 Fully-connected / 2 64C3 dense layers use MP2 features detected 3 128C3 by convolutional 4 128C3 layers to produce MP2 output FC256 FC10
Could also look at architectures developed by others, e.g. Inception by Google, or ResNets by Micrsoft for inspiration
Batch normalization
Batch normalization [Ioffe15] is recommended in most cases Necessary for deeper networks (> 8 layers)

Recommend

Hao Su July 6, 2017 Outline Overview of 3D deep learning 3D deep learning algorithms

Deep 3D Representation Learning for Visual Computing Hao Su July 6, 2017 Outline Overview of 3D deep learning 3D deep learning algorithms Conclusion 2 Outline Overview of 3D deep learning Background 3D deep learning tasks 3D deep

1.66k views • 122 slides

ACCELERATE DEEP LEARNING WITH NVIDIA'S DEEP LEARNING PLATFORM | STEPHEN JONES | GTC16 DEEP

ACCELERATE DEEP LEARNING WITH NVIDIA'S DEEP LEARNING PLATFORM | STEPHEN JONES | GTC16 DEEP LEARNING EVERYWHERE INTERNET & CLOUD MEDICINE & BIOLOGY MEDIA & ENTERTAINMENT SECURITY & DEFENSE AUTONOMOUS MACHINES Image

308 views • 26 slides

Deep Learning on GPUs March 2016 What is Deep Learning? GPUs and DL AGENDA DL in practice

Deep Learning on GPUs March 2016 What is Deep Learning? GPUs and DL AGENDA DL in practice Scaling up DL 2 What is Deep Learning? 3 DEEP LEARNING EVERYWHERE INTERNET & CLOUD MEDICINE & BIOLOGY SECURITY & DEFENSE MEDIA &

1.45k views • 39 slides

Reproducibility and Replicability in Deep Reinforcement Learning (and Other Deep Learning

Reproducibility and Replicability in Deep Reinforcement Learning (and Other Deep Learning Methods) Peter Henderson Statistical Society of Canada Annual Meeting 2018 Contributors Riashat Islam Phil Bachman Doina Precup David Meger Joelle

850 views • 37 slides

Image Classification with DIGITS NVIDIA Deep Learning Institute 1 DEEP LEARNING INSTITUTE DLI

Image Classification with DIGITS NVIDIA Deep Learning Institute 1 DEEP LEARNING INSTITUTE DLI Mission Helping people solve challenging problems using AI and deep learning. Developers, data scientists and engineers Self-driving cars,

1.44k views • 72 slides

Deep Neural Networks and Deep Reinforcement Learning Deep Learning, Goodfellow, Bengio and

Deep Neural Networks and Deep Reinforcement Learning Deep Neural Networks and Deep Reinforcement Learning Deep Learning, Goodfellow, Bengio and Courville [chapt. 6,7,8]; AIMA [sect. 21.1-21.3]; Sutton and Barto, Reinforcement Learning: an

527 views • 35 slides

Natural Language Processing with Deep Learning CS224N The Future of Deep Learning + NLP Kevin

Natural Language Processing with Deep Learning CS224N The Future of Deep Learning + NLP Kevin Clark Deep Learning for NLP 5 years ago No Seq2Seq No Attention No large-scale QA/reading comprehension datasets No TensorFlow or

1.41k views • 92 slides

An Overview of Deep Residual Learning Semih Yagcioglu 01.03.2016 Deep Residual Learning

An Overview of Deep Residual Learning Semih Yagcioglu 01.03.2016 Deep Residual Learning Microsoft Research Asia (MSRA) Kaiming He, Xiangyu Zhang, Shaoqing Ren, & Jian Sun. Deep Residual Learning for Image Recognition. arXiv

1.32k views • 54 slides

Deep Reinforcement Learning [Mastering the Game of Go with Deep Reinforcement Learning and Tree

Deep Reinforcement Learning [Mastering the Game of Go with Deep Reinforcement Learning and Tree Search, Nature 2016] CS 486/686 University of Waterloo Lecture 21: July 12, 2017 Outline AlphaGo Supervised Learning of Policy Networks

541 views • 15 slides

Mocha.jl Deep Learning in Julia Chiyuan Zhang (@pluskid) CSAIL, MIT Deep Learning Learning

Mocha.jl Deep Learning in Julia Chiyuan Zhang (@pluskid) CSAIL, MIT Deep Learning Learning with multi-layer (3~30) neural networks, on a huge training set. State-of-the-art on many AI tasks Computer Vision : Image Classification,

1.53k views • 23 slides

Minjie Wang Deep Learning Deep Learning trend in the past 10 years Caffe State-of-art DL

Tofu: Parallelizing Deep Learning Systems with Automatic Tiling Minjie Wang Deep Learning Deep Learning trend in the past 10 years Caffe State-of-art DL system is based on dataflow GPU#0 w1 w2 data g1 g2 Forward propagation

405 views • 38 slides

Deep Learning: Theory and Practice Deep Learning - Practical 02-04-2020 Considerations

Deep Learning: Theory and Practice Deep Learning - Practical 02-04-2020 Considerations deeplearning.cce2020@gmail.com Deep Networks Intuition Neural networks with multiple hidden layers - Deep networks [Hinton, 2006] Deep Networks Intuition

1.33k views • 28 slides

Deep Learning: State of the Art (2020) Deep Learning Lecture Series https://deeplearning.mit.edu

Deep Learning: State of the Art (2020) Deep Learning Lecture Series https://deeplearning.mit.edu 2020 For the full list of references visit: http://bit.ly/deeplearn-sota-2020 Outline Deep Learning Growth, Celebrations, and Limitations

1.1k views • 87 slides

12. Unsupervised Deep Learning CS 535 Deep Learning, Winter 2018 Fuxin Li With materials from

12. Unsupervised Deep Learning CS 535 Deep Learning, Winter 2018 Fuxin Li With materials from Wanli Ouyang, Zsolt Kira, Lawrence Neal, Raymond Yeh, Junting Lou and Teck-Yian Lim Unsupervised Learning in General Unsupervised learning is

1.48k views • 115 slides

Regularization for Deep Learning Lecture slides for Chapter 7 of Deep Learning

Regularization for Deep Learning Lecture slides for Chapter 7 of Deep Learning www.deeplearningbook.org Ian Goodfellow 2016-09-27 Definition Regularization is any modification we make to a learning algorithm that is intended to reduce

179 views • 13 slides

Introduction to Deep Learning Outline Deep Learning RNN CNN Attention

Introduction to Deep Learning Outline Deep Learning RNN CNN Attention Transformer Pytorch Introduction Basics Examples RNNs Some slides borrowed from Fei-Fei Li & Justin Johnson &

1.1k views • 55 slides

Deep learning for NLP: Introduction CS 6956: Deep Learning for NLP Words are a very fantastical

Deep learning for NLP: Introduction CS 6956: Deep Learning for NLP Words are a very fantastical banquet, just so many strange dishes And yet, we seem to do fine We can understand and generate language effortlessly. Almost 1 Wouldnt it be

1.62k views • 52 slides

Deep Reinforcement Learning and Complex Environments Raia Hadsell End-to-end Deep Learning

Deep Reinforcement Learning and Complex Environments Raia Hadsell End-to-end Deep Learning for robots? slide from V. Vanhoucke End-to-end Deep Learning for robots? 2010 : Speech Recognition Audio Acoustic Model Phonetic Model

1.05k views • 65 slides

Bayesian Deep Learning Mohd Adnan Problems With Deep Learning What does a model not know?

Bayesian Deep Learning Mohd Adnan Problems With Deep Learning What does a model not know? Uninterpretable black-boxes Easily fooled (AI safety) Lacks solid mathematical foundation Crucially relies on big dat Why

297 views • 15 slides

Deep Learning on Massively Parallel Processing Databases Frank McQuillan Feb 2019 2 A Brief

Deep Learning on Massively Parallel Processing Databases Frank McQuillan Feb 2019 2 A Brief Introduction to Deep Learning Artificial Intelligence Landscape Deep Learning 4 Example Deep Learning Algorithms Multilayer Recurrent

651 views • 44 slides

Deep Learning with Neural Networks The Structure and Optimization of Deep Neural Networks Allan

Deep Learning with Neural Networks The Structure and Optimization of Deep Neural Networks Allan Zelener Machine Learning Reading Group January 7 th 2016 The Graduate Center, CUNY Objectives Explain some of the trends of deep learning and

680 views • 44 slides

DSSTNE: Deep Learning At Scale For Large Sparse Datasets

DSSTNE: Deep Learning At Scale For Large Sparse Datasets https://github.com/amznlabs/amazon-dsstne Scott Le Grand Senior Scientist Teza Technologies varelse2005@gmail.com Outline What's Deep Learning? Why GPUs? Deep Learning for

1.55k views • 76 slides

Deep Learning With Differential Privacy Presenter: Xiaojun Xu Deep Learning Framework

Deep Learning With Differential Privacy Presenter: Xiaojun Xu Deep Learning Framework Autonomous Driving Gaming Face Recognition Healthcare Deep Learning Framework Dataset Server Model Privacy Issues of Training Data Dataset Server

367 views • 33 slides

Presentation about Deep Learning --- Zhongwu xie Contents 1.Brief introduction of Deep learning.

Presentation about Deep Learning --- Zhongwu xie Contents 1.Brief introduction of Deep learning. 2.Brief introduction of Backpropagation. 3.Brief introduction of Convolutional Neural Networks. Deep learning I . Introduction to Deep Learning

609 views • 22 slides