SlideShare ist ein Scribd-Unternehmen logo

Introduction to Deep Learning with TensorFlow

•Als PPTX, PDF herunterladen•
•
Terry Taewoong Um
Terry Taewoong Um

A practical guide for using TensorFlow with examples

Ingenieurwesen
1 von 37
Terry Taewoong Um (terry.t.um@gmail.com) University of Waterloo Department of Electrical & Computer Engineering Terry Taewoong Um INTRODUCTION TO DEEP NEURAL NETWORK WITH TENSORFLOW 1
Terry Taewoong Um (terry.t.um@gmail.com) CONTENTS 2 1. Why Deep Neural Network
Terry Taewoong Um (terry.t.um@gmail.com) 3 EXAMPLE CASE - Imagine you have extracted features from sensors - The dimension of each sample (which represents one of gestures) is around 800 - You have 70,000 samples (trial) - What method would you apply?
Terry Taewoong Um (terry.t.um@gmail.com) 4 EXAMPLE CASE - Reduce the dimension from 800 to 40 by using a feature selection or dim. reduction technique ☞ What you did here is “Finding a good representation” - Then, you may apply a classification methods to classify 10 classes • You may have several ways to do it • But, what if - You have no idea for feature selection? - The dimension is much higher than 800 and you have more classes. ?
Terry Taewoong Um (terry.t.um@gmail.com) 5 EXAMPLE CASE - Reduce the dimension from 800 to 40 by using a feature selection or dim. reduction technique ☞ What you did here is “Finding a good representation” - Then, you may apply a classification methods to classify 10 classes • You may have several ways to do it • But, what if - You have no idea for feature selection? - The dimension is much higher than 800 and you have more classes. MNIST dataset (65000spls * 784dim) MNIST dataset (60000spls * 1024dim)
Terry Taewoong Um (terry.t.um@gmail.com) 6 CLASSIFICATION RESULTS error rate : 28% → 15% → 8% (2010) (2014)(2012) http://rodrigob.github.io/are_we_there_yet/bu ild/classification_datasets_results.html
Terry Taewoong Um (terry.t.um@gmail.com) 7 PARADIGM CHANGE Knowledge PRESENT Representation (Features) How can we find a good representation? IMAGE SPEECH Hand-Crafted Features
Terry Taewoong Um (terry.t.um@gmail.com) 8 PARADIGM CHANGE IMAGE SPEECH Hand-Crafted Features Knowledge PRESENT Representation (Features) Can we learn a good representation (feature) for the target task as well?
Terry Taewoong Um (terry.t.um@gmail.com) 9 UNSUPERVISED LEARNING “Convolutional deep belief networks for scalable unsupervised learning of hierarchical representation”, Lee et al., 2012
Terry Taewoong Um (terry.t.um@gmail.com) 10 THREE TYPES OF DEEP LEARNING • Unsupervised learning method Autoencoder http://goo.gl/s6kmqY - Restricted Boltzmann Machine(RBM), Autoencoder, etc. - It helps to avoid local minima problem (It regularizes the training data) - But it is not necessary when we have large amount of data. (Drop-out is enough for regularization) • Convolutional Neural Network (ConvNet) • Recurrent Neural Network (RNN) + Long-Short Term Memory (LSTM) - ConvNet has shown outstanding performance in recognition tasks (image, speech) - ConvNet contains hierarchical abstraction process called pooling. - RNN+LSTM makes use of long-term memory → Good for time-series data - RNN is a generative model: It can generate new data
Terry Taewoong Um (terry.t.um@gmail.com) CONTENTS 11 2. DNN with TensorFlow Thanks to Sungjoon Choi https://github.com/sjchoi86/
Terry Taewoong Um (terry.t.um@gmail.com) 12 DEEP LEARNING LIBRARIES
Terry Taewoong Um (terry.t.um@gmail.com) 13 DEEP LEARNING LIBRARY
Terry Taewoong Um (terry.t.um@gmail.com) 14 DEEP LEARNING LIBRARY • Karpathy’s Recommendation
Terry Taewoong Um (terry.t.um@gmail.com) 15 BASIC WORKFLOW OF TF 1. Load data 2. Define the NN structure 3. Set optimization parameters 4. Run! https://github.com/terryum/TensorFlow_Exercises
Terry Taewoong Um (terry.t.um@gmail.com) 16 EXAMPLE 1 https://github.com/terryum/TensorFlow_Exercises
Terry Taewoong Um (terry.t.um@gmail.com) 17 1. LOAD DATA https://github.com/terryum/TensorFlow_Exercises/blob/ master/2_LogisticRegression_MNIST_160516.ipynb
Terry Taewoong Um (terry.t.um@gmail.com) 18 1. LOAD DATA
Terry Taewoong Um (terry.t.um@gmail.com) 19 2. DEFINE THE NN STRUCTURE 3. SET OPTIMIZATION PARAMETERS
Terry Taewoong Um (terry.t.um@gmail.com) 20 4. RUN
Terry Taewoong Um (terry.t.um@gmail.com) 21 4. RUN (C.F.)
Terry Taewoong Um (terry.t.um@gmail.com) 22 EXAMPLE 2 https://github.com/terryum/TensorFlow_Exercises
Terry Taewoong Um (terry.t.um@gmail.com) 23 NEURAL NETWORK Hugo Larochelle, http://www.dmi.usherb.ca/~larocheh/index_en.html • Activation functions http://goo.gl/qMQk5H • Basic NN structure
Terry Taewoong Um (terry.t.um@gmail.com) 24 1. LOAD DATA https://github.com/terryum/TensorFlow_Exercises/blob/ master/3a_MLP_MNIST_160516.ipynb
Terry Taewoong Um (terry.t.um@gmail.com) 25 2. DEFINE THE NN STRUCTURE
Terry Taewoong Um (terry.t.um@gmail.com) 26 3. SET OPTIMIZATION PARAMETERS
Terry Taewoong Um (terry.t.um@gmail.com) 27 4. RUN
Terry Taewoong Um (terry.t.um@gmail.com) 28 EXAMPLE 3 https://github.com/terryum/TensorFlow_Exercises
Terry Taewoong Um (terry.t.um@gmail.com) 29 CONVOLUTION http://colah.github.io/posts/2014-07- Understanding-Convolutions/ http://www.wildml.com/2015/11/understanding-convolutional-neural-networks-for-nlp/
Terry Taewoong Um (terry.t.um@gmail.com) 30 CONVOLUTIONAL NN • How can we deal with real images which is much bigger than MNIST digit images? - Use not fully-connected, but locally-connected NN - Use convolutions to get various feature maps - Abstract the results into higher layer by using pooling - Fine tune with fully-connected NN https://goo.gl/G7kBjI https://goo.gl/Xswsbd http://goo.gl/5OR5oH
Terry Taewoong Um (terry.t.um@gmail.com) 31 1. LOAD DATA https://github.com/terryum/TensorFlow_Exercises/blob/ master/4a_CNN_MNIST_160517.ipynb
Terry Taewoong Um (terry.t.um@gmail.com) 32 2. DEFINE THE NN STRUCTURE
Terry Taewoong Um (terry.t.um@gmail.com) 33 2. DEFINE THE NN STRUCTURE
Terry Taewoong Um (terry.t.um@gmail.com) 34 3. SET OPTIMIZATION PARAMETERS
Terry Taewoong Um (terry.t.um@gmail.com) 35 4. RUN
Terry Taewoong Um (terry.t.um@gmail.com) 36 4. RUN (C.F.)
Terry Taewoong Um (terry.t.um@gmail.com) 37 Thank you https://www.facebook.com/terryum http://terryum.io/ http://t-robotics.blogspot.kr/

Empfohlen

Human Motion Forecasting (Generation) with RNNs
Human Motion Forecasting (Generation) with RNNsHuman Motion Forecasting (Generation) with RNNs
Human Motion Forecasting (Generation) with RNNsTerry Taewoong Um
 
기계학습(Machine learning) 입문하기
기계학습(Machine learning) 입문하기기계학습(Machine learning) 입문하기
기계학습(Machine learning) 입문하기Terry Taewoong Um
 
Deep learning (Machine learning) tutorial for beginners
Deep learning (Machine learning) tutorial for beginnersDeep learning (Machine learning) tutorial for beginners
Deep learning (Machine learning) tutorial for beginnersTerry Taewoong Um
 
Lie Group Formulation for Robot Mechanics
Lie Group Formulation for Robot MechanicsLie Group Formulation for Robot Mechanics
Lie Group Formulation for Robot MechanicsTerry Taewoong Um
 
Introduction to Machine Learning and Deep Learning
Introduction to Machine Learning and Deep LearningIntroduction to Machine Learning and Deep Learning
Introduction to Machine Learning and Deep LearningTerry Taewoong Um
 
Understanding Black-box Predictions via Influence Functions (2017)
Understanding Black-box Predictions via Influence Functions (2017)Understanding Black-box Predictions via Influence Functions (2017)
Understanding Black-box Predictions via Influence Functions (2017)Terry Taewoong Um
 
Introducing TensorFlow: The game changer in building "intelligent" applications
Introducing TensorFlow: The game changer in building "intelligent" applicationsIntroducing TensorFlow: The game changer in building "intelligent" applications
Introducing TensorFlow: The game changer in building "intelligent" applicationsRokesh Jankie
 
Deep learning with TensorFlow
Deep learning with TensorFlowDeep learning with TensorFlow
Deep learning with TensorFlowBarbara Fusinska
 

Empfohlen

Human Motion Forecasting (Generation) with RNNs
Human Motion Forecasting (Generation) with RNNsHuman Motion Forecasting (Generation) with RNNs
Human Motion Forecasting (Generation) with RNNsTerry Taewoong Um
 
기계학습(Machine learning) 입문하기
기계학습(Machine learning) 입문하기기계학습(Machine learning) 입문하기
기계학습(Machine learning) 입문하기Terry Taewoong Um
 
Deep learning (Machine learning) tutorial for beginners
Deep learning (Machine learning) tutorial for beginnersDeep learning (Machine learning) tutorial for beginners
Deep learning (Machine learning) tutorial for beginnersTerry Taewoong Um
 
Lie Group Formulation for Robot Mechanics
Lie Group Formulation for Robot MechanicsLie Group Formulation for Robot Mechanics
Lie Group Formulation for Robot MechanicsTerry Taewoong Um
 
Introduction to Machine Learning and Deep Learning
Introduction to Machine Learning and Deep LearningIntroduction to Machine Learning and Deep Learning
Introduction to Machine Learning and Deep LearningTerry Taewoong Um
 
Understanding Black-box Predictions via Influence Functions (2017)
Understanding Black-box Predictions via Influence Functions (2017)Understanding Black-box Predictions via Influence Functions (2017)
Understanding Black-box Predictions via Influence Functions (2017)Terry Taewoong Um
 
Introducing TensorFlow: The game changer in building "intelligent" applications
Introducing TensorFlow: The game changer in building "intelligent" applicationsIntroducing TensorFlow: The game changer in building "intelligent" applications
Introducing TensorFlow: The game changer in building "intelligent" applicationsRokesh Jankie
 
Deep learning with TensorFlow
Deep learning with TensorFlowDeep learning with TensorFlow
Deep learning with TensorFlowBarbara Fusinska
 
Neural Networks with Google TensorFlow
Neural Networks with Google TensorFlowNeural Networks with Google TensorFlow
Neural Networks with Google TensorFlowDarshan Patel
 
Introduction to Neural Networks in Tensorflow
Introduction to Neural Networks in TensorflowIntroduction to Neural Networks in Tensorflow
Introduction to Neural Networks in TensorflowNicholas McClure
 
TensorFlow in 3 sentences
TensorFlow in 3 sentencesTensorFlow in 3 sentences
TensorFlow in 3 sentencesBarbara Fusinska
 
Interaction Networks for Learning about Objects, Relations and Physics
Interaction Networks for Learning about Objects, Relations and PhysicsInteraction Networks for Learning about Objects, Relations and Physics
Interaction Networks for Learning about Objects, Relations and PhysicsKen Kuroki
 
Deep learning with tensorflow
Deep learning with tensorflowDeep learning with tensorflow
Deep learning with tensorflowCharmi Chokshi
 
Overview of TensorFlow For Natural Language Processing
Overview of TensorFlow For Natural Language ProcessingOverview of TensorFlow For Natural Language Processing
Overview of TensorFlow For Natural Language Processingananth
 
Neural networks and google tensor flow
Neural networks and google tensor flowNeural networks and google tensor flow
Neural networks and google tensor flowShannon McCormick
 
Deep Learning: Chapter 11 Practical Methodology
Deep Learning: Chapter 11 Practical MethodologyDeep Learning: Chapter 11 Practical Methodology
Deep Learning: Chapter 11 Practical MethodologyJason Tsai
 
Teaching Recurrent Neural Networks using Tensorflow (May 2016)
Teaching Recurrent Neural Networks using Tensorflow (May 2016)Teaching Recurrent Neural Networks using Tensorflow (May 2016)
Teaching Recurrent Neural Networks using Tensorflow (May 2016)Rajiv Shah
 
TensorFlow
TensorFlowTensorFlow
TensorFlowSang-Houn Choi
 
Machine Learning, Deep Learning and Data Analysis Introduction
Machine Learning, Deep Learning and Data Analysis IntroductionMachine Learning, Deep Learning and Data Analysis Introduction
Machine Learning, Deep Learning and Data Analysis IntroductionTe-Yen Liu
 
Introduction To TensorFlow
Introduction To TensorFlowIntroduction To TensorFlow
Introduction To TensorFlowSpotle.ai
 
Le Song, Assistant Professor, College of Computing, Georgia Institute of Tech...
Le Song, Assistant Professor, College of Computing, Georgia Institute of Tech...Le Song, Assistant Professor, College of Computing, Georgia Institute of Tech...
Le Song, Assistant Professor, College of Computing, Georgia Institute of Tech...MLconf
 
Few shot learning/ one shot learning/ machine learning
Few shot learning/ one shot learning/ machine learningFew shot learning/ one shot learning/ machine learning
Few shot learning/ one shot learning/ machine learningﺁﺻﻒ ﻋﻠﯽ ﻣﯿﺮ
 
Networks are like onions: Practical Deep Learning with TensorFlow
Networks are like onions: Practical Deep Learning with TensorFlowNetworks are like onions: Practical Deep Learning with TensorFlow
Networks are like onions: Practical Deep Learning with TensorFlowBarbara Fusinska
 
Deep Learning in Recommender Systems - RecSys Summer School 2017
Deep Learning in Recommender Systems - RecSys Summer School 2017Deep Learning in Recommender Systems - RecSys Summer School 2017
Deep Learning in Recommender Systems - RecSys Summer School 2017Balázs Hidasi
 
Daniel Shank, Data Scientist, Talla at MLconf SF 2016
Daniel Shank, Data Scientist, Talla at MLconf SF 2016Daniel Shank, Data Scientist, Talla at MLconf SF 2016
Daniel Shank, Data Scientist, Talla at MLconf SF 2016MLconf
 
TensorFlow in Context
TensorFlow in ContextTensorFlow in Context
TensorFlow in ContextAltoros
 
Language translation with Deep Learning (RNN) with TensorFlow
Language translation with Deep Learning (RNN) with TensorFlowLanguage translation with Deep Learning (RNN) with TensorFlow
Language translation with Deep Learning (RNN) with TensorFlowS N
 
Tensor flow
Tensor flowTensor flow
Tensor flowNikhil Krishna Nair
 
Intro to Python
Intro to PythonIntro to Python
Intro to PythonTarek Abdul-Kader , Android Developer
 
20150306 파이썬기초 IPython을이용한프로그래밍_이태영
20150306 파이썬기초 IPython을이용한프로그래밍_이태영20150306 파이썬기초 IPython을이용한프로그래밍_이태영
20150306 파이썬기초 IPython을이용한프로그래밍_이태영Tae Young Lee
 

Weitere ähnliche Inhalte

Was ist angesagt?

Neural Networks with Google TensorFlow
Neural Networks with Google TensorFlowNeural Networks with Google TensorFlow
Neural Networks with Google TensorFlowDarshan Patel
 
Introduction to Neural Networks in Tensorflow
Introduction to Neural Networks in TensorflowIntroduction to Neural Networks in Tensorflow
Introduction to Neural Networks in TensorflowNicholas McClure
 
Interaction Networks for Learning about Objects, Relations and Physics
Interaction Networks for Learning about Objects, Relations and PhysicsInteraction Networks for Learning about Objects, Relations and Physics
Interaction Networks for Learning about Objects, Relations and PhysicsKen Kuroki
 
Deep learning with tensorflow
Deep learning with tensorflowDeep learning with tensorflow
Deep learning with tensorflowCharmi Chokshi
 
Overview of TensorFlow For Natural Language Processing
Overview of TensorFlow For Natural Language ProcessingOverview of TensorFlow For Natural Language Processing
Overview of TensorFlow For Natural Language Processingananth
 
Neural networks and google tensor flow
Neural networks and google tensor flowNeural networks and google tensor flow
Neural networks and google tensor flowShannon McCormick
 
Deep Learning: Chapter 11 Practical Methodology
Deep Learning: Chapter 11 Practical MethodologyDeep Learning: Chapter 11 Practical Methodology
Deep Learning: Chapter 11 Practical MethodologyJason Tsai
 
Teaching Recurrent Neural Networks using Tensorflow (May 2016)
Teaching Recurrent Neural Networks using Tensorflow (May 2016)Teaching Recurrent Neural Networks using Tensorflow (May 2016)
Teaching Recurrent Neural Networks using Tensorflow (May 2016)Rajiv Shah
 
Machine Learning, Deep Learning and Data Analysis Introduction
Machine Learning, Deep Learning and Data Analysis IntroductionMachine Learning, Deep Learning and Data Analysis Introduction
Machine Learning, Deep Learning and Data Analysis IntroductionTe-Yen Liu
 
Introduction To TensorFlow
Introduction To TensorFlowIntroduction To TensorFlow
Introduction To TensorFlowSpotle.ai
 
Le Song, Assistant Professor, College of Computing, Georgia Institute of Tech...
Le Song, Assistant Professor, College of Computing, Georgia Institute of Tech...Le Song, Assistant Professor, College of Computing, Georgia Institute of Tech...
Le Song, Assistant Professor, College of Computing, Georgia Institute of Tech...MLconf
 
Few shot learning/ one shot learning/ machine learning
Few shot learning/ one shot learning/ machine learningFew shot learning/ one shot learning/ machine learning
Few shot learning/ one shot learning/ machine learningﺁﺻﻒ ﻋﻠﯽ ﻣﯿﺮ
 
Networks are like onions: Practical Deep Learning with TensorFlow
Networks are like onions: Practical Deep Learning with TensorFlowNetworks are like onions: Practical Deep Learning with TensorFlow
Networks are like onions: Practical Deep Learning with TensorFlowBarbara Fusinska
 
Deep Learning in Recommender Systems - RecSys Summer School 2017
Deep Learning in Recommender Systems - RecSys Summer School 2017Deep Learning in Recommender Systems - RecSys Summer School 2017
Deep Learning in Recommender Systems - RecSys Summer School 2017Balázs Hidasi
 
Daniel Shank, Data Scientist, Talla at MLconf SF 2016
Daniel Shank, Data Scientist, Talla at MLconf SF 2016Daniel Shank, Data Scientist, Talla at MLconf SF 2016
Daniel Shank, Data Scientist, Talla at MLconf SF 2016MLconf
 
TensorFlow in Context
TensorFlow in ContextTensorFlow in Context
TensorFlow in ContextAltoros
 
Language translation with Deep Learning (RNN) with TensorFlow
Language translation with Deep Learning (RNN) with TensorFlowLanguage translation with Deep Learning (RNN) with TensorFlow
Language translation with Deep Learning (RNN) with TensorFlowS N
 

Was ist angesagt? (20)

Neural Networks with Google TensorFlow
Neural Networks with Google TensorFlowNeural Networks with Google TensorFlow
Neural Networks with Google TensorFlow
 
Introduction to Neural Networks in Tensorflow
Introduction to Neural Networks in TensorflowIntroduction to Neural Networks in Tensorflow
Introduction to Neural Networks in Tensorflow
 
TensorFlow in 3 sentences
TensorFlow in 3 sentencesTensorFlow in 3 sentences
TensorFlow in 3 sentences
 
Interaction Networks for Learning about Objects, Relations and Physics
Interaction Networks for Learning about Objects, Relations and PhysicsInteraction Networks for Learning about Objects, Relations and Physics
Interaction Networks for Learning about Objects, Relations and Physics
 
Deep learning with tensorflow
Deep learning with tensorflowDeep learning with tensorflow
Deep learning with tensorflow
 
Overview of TensorFlow For Natural Language Processing
Overview of TensorFlow For Natural Language ProcessingOverview of TensorFlow For Natural Language Processing
Overview of TensorFlow For Natural Language Processing
 
Neural networks and google tensor flow
Neural networks and google tensor flowNeural networks and google tensor flow
Neural networks and google tensor flow
 
Deep Learning: Chapter 11 Practical Methodology
Deep Learning: Chapter 11 Practical MethodologyDeep Learning: Chapter 11 Practical Methodology
Deep Learning: Chapter 11 Practical Methodology
 
Teaching Recurrent Neural Networks using Tensorflow (May 2016)
Teaching Recurrent Neural Networks using Tensorflow (May 2016)Teaching Recurrent Neural Networks using Tensorflow (May 2016)
Teaching Recurrent Neural Networks using Tensorflow (May 2016)
 
TensorFlow
TensorFlowTensorFlow
TensorFlow
 
Machine Learning, Deep Learning and Data Analysis Introduction
Machine Learning, Deep Learning and Data Analysis IntroductionMachine Learning, Deep Learning and Data Analysis Introduction
Machine Learning, Deep Learning and Data Analysis Introduction
 
Introduction To TensorFlow
Introduction To TensorFlowIntroduction To TensorFlow
Introduction To TensorFlow
 
Le Song, Assistant Professor, College of Computing, Georgia Institute of Tech...
Le Song, Assistant Professor, College of Computing, Georgia Institute of Tech...Le Song, Assistant Professor, College of Computing, Georgia Institute of Tech...
Le Song, Assistant Professor, College of Computing, Georgia Institute of Tech...
 
Few shot learning/ one shot learning/ machine learning
Few shot learning/ one shot learning/ machine learningFew shot learning/ one shot learning/ machine learning
Few shot learning/ one shot learning/ machine learning
 
Networks are like onions: Practical Deep Learning with TensorFlow
Networks are like onions: Practical Deep Learning with TensorFlowNetworks are like onions: Practical Deep Learning with TensorFlow
Networks are like onions: Practical Deep Learning with TensorFlow
 
Deep Learning in Recommender Systems - RecSys Summer School 2017
Deep Learning in Recommender Systems - RecSys Summer School 2017Deep Learning in Recommender Systems - RecSys Summer School 2017
Deep Learning in Recommender Systems - RecSys Summer School 2017
 
Daniel Shank, Data Scientist, Talla at MLconf SF 2016
Daniel Shank, Data Scientist, Talla at MLconf SF 2016Daniel Shank, Data Scientist, Talla at MLconf SF 2016
Daniel Shank, Data Scientist, Talla at MLconf SF 2016
 
TensorFlow in Context
TensorFlow in ContextTensorFlow in Context
TensorFlow in Context
 
Language translation with Deep Learning (RNN) with TensorFlow
Language translation with Deep Learning (RNN) with TensorFlowLanguage translation with Deep Learning (RNN) with TensorFlow
Language translation with Deep Learning (RNN) with TensorFlow
 
Tensor flow
Tensor flowTensor flow
Tensor flow
 

Andere mochten auch

20150306 파이썬기초 IPython을이용한프로그래밍_이태영
20150306 파이썬기초 IPython을이용한프로그래밍_이태영20150306 파이썬기초 IPython을이용한프로그래밍_이태영
20150306 파이썬기초 IPython을이용한프로그래밍_이태영Tae Young Lee
 
High Performance Distributed TensorFlow with GPUs - NYC Workshop - July 9 2017
High Performance Distributed TensorFlow with GPUs - NYC Workshop - July 9 2017High Performance Distributed TensorFlow with GPUs - NYC Workshop - July 9 2017
High Performance Distributed TensorFlow with GPUs - NYC Workshop - July 9 2017Chris Fregly
 
인공 신경망 구현에 관한 간단한 설명
인공 신경망 구현에 관한 간단한 설명인공 신경망 구현에 관한 간단한 설명
인공 신경망 구현에 관한 간단한 설명Woonghee Lee
 
[모두의연구소] 쫄지말자딥러닝
[모두의연구소] 쫄지말자딥러닝[모두의연구소] 쫄지말자딥러닝
[모두의연구소] 쫄지말자딥러닝Modulabs
 
Lie Group Formulation for Robot Mechanics
Lie Group Formulation for Robot MechanicsLie Group Formulation for Robot Mechanics
Lie Group Formulation for Robot MechanicsTerry Taewoong Um
 
R 프로그래밍 기본 문법
R 프로그래밍 기본 문법R 프로그래밍 기본 문법
R 프로그래밍 기본 문법Terry Cho
 
머신 러닝 입문 #1-머신러닝 소개와 kNN 소개
머신 러닝 입문 #1-머신러닝 소개와 kNN 소개머신 러닝 입문 #1-머신러닝 소개와 kNN 소개
머신 러닝 입문 #1-머신러닝 소개와 kNN 소개Terry Cho
 
TensorFlow Tutorial
TensorFlow TutorialTensorFlow Tutorial
TensorFlow TutorialNamHyuk Ahn
 
쫄지말자딥러닝2 - CNN RNN 포함버전
쫄지말자딥러닝2 - CNN RNN 포함버전쫄지말자딥러닝2 - CNN RNN 포함버전
쫄지말자딥러닝2 - CNN RNN 포함버전Modulabs
 
인공지능, 기계학습 그리고 딥러닝
인공지능, 기계학습 그리고 딥러닝인공지능, 기계학습 그리고 딥러닝
인공지능, 기계학습 그리고 딥러닝Jinwon Lee
 
Large Scale Deep Learning with TensorFlow
Large Scale Deep Learning with TensorFlow Large Scale Deep Learning with TensorFlow
Large Scale Deep Learning with TensorFlow Jen Aman
 
알파고 (바둑 인공지능)의 작동 원리
알파고 (바둑 인공지능)의 작동 원리알파고 (바둑 인공지능)의 작동 원리
알파고 (바둑 인공지능)의 작동 원리Shane (Seungwhan) Moon
 
Intro to TensorFlow and PyTorch Workshop at Tubular Labs
Intro to TensorFlow and PyTorch Workshop at Tubular LabsIntro to TensorFlow and PyTorch Workshop at Tubular Labs
Intro to TensorFlow and PyTorch Workshop at Tubular LabsKendall
 
자습해도 모르겠던 딥러닝, 머리속에 인스톨 시켜드립니다.
자습해도 모르겠던 딥러닝, 머리속에 인스톨 시켜드립니다.자습해도 모르겠던 딥러닝, 머리속에 인스톨 시켜드립니다.
자습해도 모르겠던 딥러닝, 머리속에 인스톨 시켜드립니다.Yongho Ha
 
Ai 그까이거
Ai 그까이거Ai 그까이거
Ai 그까이거도형 임
 
Learning with side information through modality hallucination (2016)
Learning with side information through modality hallucination (2016)Learning with side information through modality hallucination (2016)
Learning with side information through modality hallucination (2016)Terry Taewoong Um
 
Deformable Convolutional Network (2017)
Deformable Convolutional Network (2017)Deformable Convolutional Network (2017)
Deformable Convolutional Network (2017)Terry Taewoong Um
 
기계학습 / 딥러닝이란 무엇인가
기계학습 / 딥러닝이란 무엇인가기계학습 / 딥러닝이란 무엇인가
기계학습 / 딥러닝이란 무엇인가Yongha Kim
 

Andere mochten auch (19)

Intro to Python
Intro to PythonIntro to Python
Intro to Python
 
20150306 파이썬기초 IPython을이용한프로그래밍_이태영
20150306 파이썬기초 IPython을이용한프로그래밍_이태영20150306 파이썬기초 IPython을이용한프로그래밍_이태영
20150306 파이썬기초 IPython을이용한프로그래밍_이태영
 
High Performance Distributed TensorFlow with GPUs - NYC Workshop - July 9 2017
High Performance Distributed TensorFlow with GPUs - NYC Workshop - July 9 2017High Performance Distributed TensorFlow with GPUs - NYC Workshop - July 9 2017
High Performance Distributed TensorFlow with GPUs - NYC Workshop - July 9 2017
 
인공 신경망 구현에 관한 간단한 설명
인공 신경망 구현에 관한 간단한 설명인공 신경망 구현에 관한 간단한 설명
인공 신경망 구현에 관한 간단한 설명
 
[모두의연구소] 쫄지말자딥러닝
[모두의연구소] 쫄지말자딥러닝[모두의연구소] 쫄지말자딥러닝
[모두의연구소] 쫄지말자딥러닝
 
Lie Group Formulation for Robot Mechanics
Lie Group Formulation for Robot MechanicsLie Group Formulation for Robot Mechanics
Lie Group Formulation for Robot Mechanics
 
R 프로그래밍 기본 문법
R 프로그래밍 기본 문법R 프로그래밍 기본 문법
R 프로그래밍 기본 문법
 
머신 러닝 입문 #1-머신러닝 소개와 kNN 소개
머신 러닝 입문 #1-머신러닝 소개와 kNN 소개머신 러닝 입문 #1-머신러닝 소개와 kNN 소개
머신 러닝 입문 #1-머신러닝 소개와 kNN 소개
 
TensorFlow Tutorial
TensorFlow TutorialTensorFlow Tutorial
TensorFlow Tutorial
 
쫄지말자딥러닝2 - CNN RNN 포함버전
쫄지말자딥러닝2 - CNN RNN 포함버전쫄지말자딥러닝2 - CNN RNN 포함버전
쫄지말자딥러닝2 - CNN RNN 포함버전
 
인공지능, 기계학습 그리고 딥러닝
인공지능, 기계학습 그리고 딥러닝인공지능, 기계학습 그리고 딥러닝
인공지능, 기계학습 그리고 딥러닝
 
Large Scale Deep Learning with TensorFlow
Large Scale Deep Learning with TensorFlow Large Scale Deep Learning with TensorFlow
Large Scale Deep Learning with TensorFlow
 
알파고 (바둑 인공지능)의 작동 원리
알파고 (바둑 인공지능)의 작동 원리알파고 (바둑 인공지능)의 작동 원리
알파고 (바둑 인공지능)의 작동 원리
 
Intro to TensorFlow and PyTorch Workshop at Tubular Labs
Intro to TensorFlow and PyTorch Workshop at Tubular LabsIntro to TensorFlow and PyTorch Workshop at Tubular Labs
Intro to TensorFlow and PyTorch Workshop at Tubular Labs
 
자습해도 모르겠던 딥러닝, 머리속에 인스톨 시켜드립니다.
자습해도 모르겠던 딥러닝, 머리속에 인스톨 시켜드립니다.자습해도 모르겠던 딥러닝, 머리속에 인스톨 시켜드립니다.
자습해도 모르겠던 딥러닝, 머리속에 인스톨 시켜드립니다.
 
Ai 그까이거
Ai 그까이거Ai 그까이거
Ai 그까이거
 
Learning with side information through modality hallucination (2016)
Learning with side information through modality hallucination (2016)Learning with side information through modality hallucination (2016)
Learning with side information through modality hallucination (2016)
 
Deformable Convolutional Network (2017)
Deformable Convolutional Network (2017)Deformable Convolutional Network (2017)
Deformable Convolutional Network (2017)
 
기계학습 / 딥러닝이란 무엇인가
기계학습 / 딥러닝이란 무엇인가기계학습 / 딥러닝이란 무엇인가
기계학습 / 딥러닝이란 무엇인가
 

Ähnlich wie Introduction to Deep Learning with TensorFlow

Deep Reinforcement Learning in a Handful of Trials using Probabilistic Dynami...
Deep Reinforcement Learning in a Handful of Trials using Probabilistic Dynami...Deep Reinforcement Learning in a Handful of Trials using Probabilistic Dynami...
Deep Reinforcement Learning in a Handful of Trials using Probabilistic Dynami...Terry Taewoong Um
 
04-Data-Analysis-Overview.pptx
04-Data-Analysis-Overview.pptx04-Data-Analysis-Overview.pptx
04-Data-Analysis-Overview.pptxShree Shree
 
Week 1 Lec 1-5 with watermarking.pdf
Week 1 Lec 1-5 with watermarking.pdfWeek 1 Lec 1-5 with watermarking.pdf
Week 1 Lec 1-5 with watermarking.pdfmeghana092
 
Week_1_Lec_1-5_with_watermarking_(1).pdf
Week_1_Lec_1-5_with_watermarking_(1).pdfWeek_1_Lec_1-5_with_watermarking_(1).pdf
Week_1_Lec_1-5_with_watermarking_(1).pdfPrabhaK22
 
00_pytorch_and_deep_learning_fundamentals.pdf
00_pytorch_and_deep_learning_fundamentals.pdf00_pytorch_and_deep_learning_fundamentals.pdf
00_pytorch_and_deep_learning_fundamentals.pdfeanyang7
 
Predict saturated thickness using tensor board visualization
Predict saturated thickness using tensor board visualizationPredict saturated thickness using tensor board visualization
Predict saturated thickness using tensor board visualizationVinh Nguyen
 
TensorfLow_Basic.pptx
TensorfLow_Basic.pptxTensorfLow_Basic.pptx
TensorfLow_Basic.pptxTMUb202109065
 
Data structures and Big O notation
Data structures and Big O notationData structures and Big O notation
Data structures and Big O notationMuthiah Abbhirami
 
Word embeddings as a service - PyData NYC 2015
Word embeddings as a service - PyData NYC 2015Word embeddings as a service - PyData NYC 2015
Word embeddings as a service - PyData NYC 2015François Scharffe
 
Semi-Supervised Insight Generation from Petabyte Scale Text Data
Semi-Supervised Insight Generation from Petabyte Scale Text DataSemi-Supervised Insight Generation from Petabyte Scale Text Data
Semi-Supervised Insight Generation from Petabyte Scale Text DataTech Triveni
 
Rocky Nevin's presentation at eComm 2008
Rocky Nevin's presentation at eComm 2008Rocky Nevin's presentation at eComm 2008
Rocky Nevin's presentation at eComm 2008eComm2008
 
Classification decision tree
Classification decision treeClassification decision tree
Classification decision treeyazad dumasia
 
Computer notes - data structures
Computer notes - data structuresComputer notes - data structures
Computer notes - data structuresecomputernotes
 
Database Research Principles Revealed
Database Research Principles RevealedDatabase Research Principles Revealed
Database Research Principles Revealedinfoblog
 
Basics in algorithms and data structure
Basics in algorithms and data structure Basics in algorithms and data structure
Basics in algorithms and data structure Eman magdy
 

Ähnlich wie Introduction to Deep Learning with TensorFlow (20)

Deep Reinforcement Learning in a Handful of Trials using Probabilistic Dynami...
Deep Reinforcement Learning in a Handful of Trials using Probabilistic Dynami...Deep Reinforcement Learning in a Handful of Trials using Probabilistic Dynami...
Deep Reinforcement Learning in a Handful of Trials using Probabilistic Dynami...
 
04-Data-Analysis-Overview.pptx
04-Data-Analysis-Overview.pptx04-Data-Analysis-Overview.pptx
04-Data-Analysis-Overview.pptx
 
Week 1 Lec 1-5 with watermarking.pdf
Week 1 Lec 1-5 with watermarking.pdfWeek 1 Lec 1-5 with watermarking.pdf
Week 1 Lec 1-5 with watermarking.pdf
 
Week_1_Lec_1-5_with_watermarking_(1).pdf
Week_1_Lec_1-5_with_watermarking_(1).pdfWeek_1_Lec_1-5_with_watermarking_(1).pdf
Week_1_Lec_1-5_with_watermarking_(1).pdf
 
00_pytorch_and_deep_learning_fundamentals.pdf
00_pytorch_and_deep_learning_fundamentals.pdf00_pytorch_and_deep_learning_fundamentals.pdf
00_pytorch_and_deep_learning_fundamentals.pdf
 
Chatbot ppt
Chatbot pptChatbot ppt
Chatbot ppt
 
Predict saturated thickness using tensor board visualization
Predict saturated thickness using tensor board visualizationPredict saturated thickness using tensor board visualization
Predict saturated thickness using tensor board visualization
 
TensorfLow_Basic.pptx
TensorfLow_Basic.pptxTensorfLow_Basic.pptx
TensorfLow_Basic.pptx
 
1645 track 2 pafka
1645 track 2 pafka1645 track 2 pafka
1645 track 2 pafka
 
Data structures and Big O notation
Data structures and Big O notationData structures and Big O notation
Data structures and Big O notation
 
Data Structure and Algorithms
Data Structure and AlgorithmsData Structure and Algorithms
Data Structure and Algorithms
 
Algorithm
AlgorithmAlgorithm
Algorithm
 
Word embeddings as a service - PyData NYC 2015
Word embeddings as a service - PyData NYC 2015Word embeddings as a service - PyData NYC 2015
Word embeddings as a service - PyData NYC 2015
 
Semi-Supervised Insight Generation from Petabyte Scale Text Data
Semi-Supervised Insight Generation from Petabyte Scale Text DataSemi-Supervised Insight Generation from Petabyte Scale Text Data
Semi-Supervised Insight Generation from Petabyte Scale Text Data
 
Rocky Nevin's presentation at eComm 2008
Rocky Nevin's presentation at eComm 2008Rocky Nevin's presentation at eComm 2008
Rocky Nevin's presentation at eComm 2008
 
Classification decision tree
Classification decision treeClassification decision tree
Classification decision tree
 
Computer notes - data structures
Computer notes - data structuresComputer notes - data structures
Computer notes - data structures
 
Database Research Principles Revealed
Database Research Principles RevealedDatabase Research Principles Revealed
Database Research Principles Revealed
 
Basics in algorithms and data structure
Basics in algorithms and data structure Basics in algorithms and data structure
Basics in algorithms and data structure
 
Data Science
Data Science Data Science
Data Science
 

Mehr von Terry Taewoong Um

#44. KAIST에서 "대학 유죄"를 외치다: ART Lab의 도전
#44. KAIST에서 "대학 유죄"를 외치다: ART Lab의 도전#44. KAIST에서 "대학 유죄"를 외치다: ART Lab의 도전
#44. KAIST에서 "대학 유죄"를 외치다: ART Lab의 도전Terry Taewoong Um
 
A brief introduction to OCR (Optical character recognition)
A brief introduction to OCR (Optical character recognition)A brief introduction to OCR (Optical character recognition)
A brief introduction to OCR (Optical character recognition)Terry Taewoong Um
 
인공지능의 사회정의의 편이 될 수 있을까? (인공지능과 법)
인공지능의 사회정의의 편이 될 수 있을까? (인공지능과 법)인공지능의 사회정의의 편이 될 수 있을까? (인공지능과 법)
인공지능의 사회정의의 편이 될 수 있을까? (인공지능과 법)Terry Taewoong Um
 
Deep Variational Bayes Filters (2017)
Deep Variational Bayes Filters (2017)Deep Variational Bayes Filters (2017)
Deep Variational Bayes Filters (2017)Terry Taewoong Um
 
On Calibration of Modern Neural Networks (2017)
On Calibration of Modern Neural Networks (2017)On Calibration of Modern Neural Networks (2017)
On Calibration of Modern Neural Networks (2017)Terry Taewoong Um
 
Deep Learning: A Critical Appraisal (2018)
Deep Learning: A Critical Appraisal (2018)Deep Learning: A Critical Appraisal (2018)
Deep Learning: A Critical Appraisal (2018)Terry Taewoong Um
 
About Two Motion Planning Papers
About Two Motion Planning PapersAbout Two Motion Planning Papers
About Two Motion Planning PapersTerry Taewoong Um
 
로봇과 인공지능, 그리고 미래의 노동
로봇과 인공지능, 그리고 미래의 노동로봇과 인공지능, 그리고 미래의 노동
로봇과 인공지능, 그리고 미래의 노동Terry Taewoong Um
 

Mehr von Terry Taewoong Um (8)

#44. KAIST에서 "대학 유죄"를 외치다: ART Lab의 도전
#44. KAIST에서 "대학 유죄"를 외치다: ART Lab의 도전#44. KAIST에서 "대학 유죄"를 외치다: ART Lab의 도전
#44. KAIST에서 "대학 유죄"를 외치다: ART Lab의 도전
 
A brief introduction to OCR (Optical character recognition)
A brief introduction to OCR (Optical character recognition)A brief introduction to OCR (Optical character recognition)
A brief introduction to OCR (Optical character recognition)
 
인공지능의 사회정의의 편이 될 수 있을까? (인공지능과 법)
인공지능의 사회정의의 편이 될 수 있을까? (인공지능과 법)인공지능의 사회정의의 편이 될 수 있을까? (인공지능과 법)
인공지능의 사회정의의 편이 될 수 있을까? (인공지능과 법)
 
Deep Variational Bayes Filters (2017)
Deep Variational Bayes Filters (2017)Deep Variational Bayes Filters (2017)
Deep Variational Bayes Filters (2017)
 
On Calibration of Modern Neural Networks (2017)
On Calibration of Modern Neural Networks (2017)On Calibration of Modern Neural Networks (2017)
On Calibration of Modern Neural Networks (2017)
 
Deep Learning: A Critical Appraisal (2018)
Deep Learning: A Critical Appraisal (2018)Deep Learning: A Critical Appraisal (2018)
Deep Learning: A Critical Appraisal (2018)
 
About Two Motion Planning Papers
About Two Motion Planning PapersAbout Two Motion Planning Papers
About Two Motion Planning Papers
 
로봇과 인공지능, 그리고 미래의 노동
로봇과 인공지능, 그리고 미래의 노동로봇과 인공지능, 그리고 미래의 노동
로봇과 인공지능, 그리고 미래의 노동
 

Kürzlich hochgeladen

Intze Overhead Water Tank Design by Working Stress - IS Method.pdf
Intze Overhead Water Tank Design by Working Stress - IS Method.pdfIntze Overhead Water Tank Design by Working Stress - IS Method.pdf
Intze Overhead Water Tank Design by Working Stress - IS Method.pdfSuman Jyoti
 
Call Girls Walvekar Nagar Call Me 7737669865 Budget Friendly No Advance Booking
Call Girls Walvekar Nagar Call Me 7737669865 Budget Friendly No Advance BookingCall Girls Walvekar Nagar Call Me 7737669865 Budget Friendly No Advance Booking
Call Girls Walvekar Nagar Call Me 7737669865 Budget Friendly No Advance Bookingroncy bisnoi
 
Unit 1 - Soil Classification and Compaction.pdf
Unit 1 - Soil Classification and Compaction.pdfUnit 1 - Soil Classification and Compaction.pdf
Unit 1 - Soil Classification and Compaction.pdfRagavanV2
 
Intro To Electric Vehicles PDF Notes.pdf
Intro To Electric Vehicles PDF Notes.pdfIntro To Electric Vehicles PDF Notes.pdf
Intro To Electric Vehicles PDF Notes.pdfrs7054576148
 
Navigating Complexity: The Role of Trusted Partners and VIAS3D in Dassault Sy...
Navigating Complexity: The Role of Trusted Partners and VIAS3D in Dassault Sy...Navigating Complexity: The Role of Trusted Partners and VIAS3D in Dassault Sy...
Navigating Complexity: The Role of Trusted Partners and VIAS3D in Dassault Sy...Arindam Chakraborty, Ph.D., P.E. (CA, TX)
 
AKTU Computer Networks notes --- Unit 3.pdf
AKTU Computer Networks notes --- Unit 3.pdfAKTU Computer Networks notes --- Unit 3.pdf
AKTU Computer Networks notes --- Unit 3.pdfankushspencer015
 
ONLINE FOOD ORDER SYSTEM PROJECT REPORT.pdf
ONLINE FOOD ORDER SYSTEM PROJECT REPORT.pdfONLINE FOOD ORDER SYSTEM PROJECT REPORT.pdf
ONLINE FOOD ORDER SYSTEM PROJECT REPORT.pdfKamal Acharya
 
Design For Accessibility: Getting it right from the start
Design For Accessibility: Getting it right from the startDesign For Accessibility: Getting it right from the start
Design For Accessibility: Getting it right from the startQuintin Balsdon
 
Unleashing the Power of the SORA AI lastest leap
Unleashing the Power of the SORA AI lastest leapUnleashing the Power of the SORA AI lastest leap
Unleashing the Power of the SORA AI lastest leapRishantSharmaFr
 
University management System project report..pdf
University management System project report..pdfUniversity management System project report..pdf
University management System project report..pdfKamal Acharya
 
data_management_and _data_science_cheat_sheet.pdf
data_management_and _data_science_cheat_sheet.pdfdata_management_and _data_science_cheat_sheet.pdf
data_management_and _data_science_cheat_sheet.pdfJiananWang21
 
VIP Call Girls Ankleshwar 7001035870 Whatsapp Number, 24/07 Booking
VIP Call Girls Ankleshwar 7001035870 Whatsapp Number, 24/07 BookingVIP Call Girls Ankleshwar 7001035870 Whatsapp Number, 24/07 Booking
VIP Call Girls Ankleshwar 7001035870 Whatsapp Number, 24/07 Bookingdharasingh5698
 
Unit 2- Effective stress & Permeability.pdf
Unit 2- Effective stress & Permeability.pdfUnit 2- Effective stress & Permeability.pdf
Unit 2- Effective stress & Permeability.pdfRagavanV2
 
VIP Model Call Girls Kothrud ( Pune ) Call ON 8005736733 Starting From 5K to ...
VIP Model Call Girls Kothrud ( Pune ) Call ON 8005736733 Starting From 5K to ...VIP Model Call Girls Kothrud ( Pune ) Call ON 8005736733 Starting From 5K to ...
VIP Model Call Girls Kothrud ( Pune ) Call ON 8005736733 Starting From 5K to ...SUHANI PANDEY
 
Thermal Engineering-R & A / C - unit - V
Thermal Engineering-R & A / C - unit - VThermal Engineering-R & A / C - unit - V
Thermal Engineering-R & A / C - unit - VDineshKumar4165
 
chapter 5.pptx: drainage and irrigation engineering
chapter 5.pptx: drainage and irrigation engineeringchapter 5.pptx: drainage and irrigation engineering
chapter 5.pptx: drainage and irrigation engineeringmulugeta48
 
Bhosari ( Call Girls ) Pune 6297143586 Hot Model With Sexy Bhabi Ready For ...
Bhosari ( Call Girls ) Pune 6297143586 Hot Model With Sexy Bhabi Ready For ...Bhosari ( Call Girls ) Pune 6297143586 Hot Model With Sexy Bhabi Ready For ...
Bhosari ( Call Girls ) Pune 6297143586 Hot Model With Sexy Bhabi Ready For ...tanu pandey
 

Kürzlich hochgeladen (20)

Intze Overhead Water Tank Design by Working Stress - IS Method.pdf
Intze Overhead Water Tank Design by Working Stress - IS Method.pdfIntze Overhead Water Tank Design by Working Stress - IS Method.pdf
Intze Overhead Water Tank Design by Working Stress - IS Method.pdf
 
(INDIRA) Call Girl Bhosari Call Now 8617697112 Bhosari Escorts 24x7
(INDIRA) Call Girl Bhosari Call Now 8617697112 Bhosari Escorts 24x7(INDIRA) Call Girl Bhosari Call Now 8617697112 Bhosari Escorts 24x7
(INDIRA) Call Girl Bhosari Call Now 8617697112 Bhosari Escorts 24x7
 
Call Girls Walvekar Nagar Call Me 7737669865 Budget Friendly No Advance Booking
Call Girls Walvekar Nagar Call Me 7737669865 Budget Friendly No Advance BookingCall Girls Walvekar Nagar Call Me 7737669865 Budget Friendly No Advance Booking
Call Girls Walvekar Nagar Call Me 7737669865 Budget Friendly No Advance Booking
 
Unit 1 - Soil Classification and Compaction.pdf
Unit 1 - Soil Classification and Compaction.pdfUnit 1 - Soil Classification and Compaction.pdf
Unit 1 - Soil Classification and Compaction.pdf
 
Intro To Electric Vehicles PDF Notes.pdf
Intro To Electric Vehicles PDF Notes.pdfIntro To Electric Vehicles PDF Notes.pdf
Intro To Electric Vehicles PDF Notes.pdf
 
Navigating Complexity: The Role of Trusted Partners and VIAS3D in Dassault Sy...
Navigating Complexity: The Role of Trusted Partners and VIAS3D in Dassault Sy...Navigating Complexity: The Role of Trusted Partners and VIAS3D in Dassault Sy...
Navigating Complexity: The Role of Trusted Partners and VIAS3D in Dassault Sy...
 
AKTU Computer Networks notes --- Unit 3.pdf
AKTU Computer Networks notes --- Unit 3.pdfAKTU Computer Networks notes --- Unit 3.pdf
AKTU Computer Networks notes --- Unit 3.pdf
 
ONLINE FOOD ORDER SYSTEM PROJECT REPORT.pdf
ONLINE FOOD ORDER SYSTEM PROJECT REPORT.pdfONLINE FOOD ORDER SYSTEM PROJECT REPORT.pdf
ONLINE FOOD ORDER SYSTEM PROJECT REPORT.pdf
 
Design For Accessibility: Getting it right from the start
Design For Accessibility: Getting it right from the startDesign For Accessibility: Getting it right from the start
Design For Accessibility: Getting it right from the start
 
Unleashing the Power of the SORA AI lastest leap
Unleashing the Power of the SORA AI lastest leapUnleashing the Power of the SORA AI lastest leap
Unleashing the Power of the SORA AI lastest leap
 
University management System project report..pdf
University management System project report..pdfUniversity management System project report..pdf
University management System project report..pdf
 
(INDIRA) Call Girl Aurangabad Call Now 8617697112 Aurangabad Escorts 24x7
(INDIRA) Call Girl Aurangabad Call Now 8617697112 Aurangabad Escorts 24x7(INDIRA) Call Girl Aurangabad Call Now 8617697112 Aurangabad Escorts 24x7
(INDIRA) Call Girl Aurangabad Call Now 8617697112 Aurangabad Escorts 24x7
 
data_management_and _data_science_cheat_sheet.pdf
data_management_and _data_science_cheat_sheet.pdfdata_management_and _data_science_cheat_sheet.pdf
data_management_and _data_science_cheat_sheet.pdf
 
FEA Based Level 3 Assessment of Deformed Tanks with Fluid Induced Loads
FEA Based Level 3 Assessment of Deformed Tanks with Fluid Induced LoadsFEA Based Level 3 Assessment of Deformed Tanks with Fluid Induced Loads
FEA Based Level 3 Assessment of Deformed Tanks with Fluid Induced Loads
 
VIP Call Girls Ankleshwar 7001035870 Whatsapp Number, 24/07 Booking
VIP Call Girls Ankleshwar 7001035870 Whatsapp Number, 24/07 BookingVIP Call Girls Ankleshwar 7001035870 Whatsapp Number, 24/07 Booking
VIP Call Girls Ankleshwar 7001035870 Whatsapp Number, 24/07 Booking
 
Unit 2- Effective stress & Permeability.pdf
Unit 2- Effective stress & Permeability.pdfUnit 2- Effective stress & Permeability.pdf
Unit 2- Effective stress & Permeability.pdf
 
VIP Model Call Girls Kothrud ( Pune ) Call ON 8005736733 Starting From 5K to ...
VIP Model Call Girls Kothrud ( Pune ) Call ON 8005736733 Starting From 5K to ...VIP Model Call Girls Kothrud ( Pune ) Call ON 8005736733 Starting From 5K to ...
VIP Model Call Girls Kothrud ( Pune ) Call ON 8005736733 Starting From 5K to ...
 
Thermal Engineering-R & A / C - unit - V
Thermal Engineering-R & A / C - unit - VThermal Engineering-R & A / C - unit - V
Thermal Engineering-R & A / C - unit - V
 
chapter 5.pptx: drainage and irrigation engineering
chapter 5.pptx: drainage and irrigation engineeringchapter 5.pptx: drainage and irrigation engineering
chapter 5.pptx: drainage and irrigation engineering
 
Bhosari ( Call Girls ) Pune 6297143586 Hot Model With Sexy Bhabi Ready For ...
Bhosari ( Call Girls ) Pune 6297143586 Hot Model With Sexy Bhabi Ready For ...Bhosari ( Call Girls ) Pune 6297143586 Hot Model With Sexy Bhabi Ready For ...
Bhosari ( Call Girls ) Pune 6297143586 Hot Model With Sexy Bhabi Ready For ...
 

Introduction to Deep Learning with TensorFlow

  • 1. Terry Taewoong Um (terry.t.um@gmail.com) University of Waterloo Department of Electrical & Computer Engineering Terry Taewoong Um INTRODUCTION TO DEEP NEURAL NETWORK WITH TENSORFLOW 1
  • 2. Terry Taewoong Um (terry.t.um@gmail.com) CONTENTS 2 1. Why Deep Neural Network
  • 3. Terry Taewoong Um (terry.t.um@gmail.com) 3 EXAMPLE CASE - Imagine you have extracted features from sensors - The dimension of each sample (which represents one of gestures) is around 800 - You have 70,000 samples (trial) - What method would you apply?
  • 4. Terry Taewoong Um (terry.t.um@gmail.com) 4 EXAMPLE CASE - Reduce the dimension from 800 to 40 by using a feature selection or dim. reduction technique ☞ What you did here is “Finding a good representation” - Then, you may apply a classification methods to classify 10 classes • You may have several ways to do it • But, what if - You have no idea for feature selection? - The dimension is much higher than 800 and you have more classes. ?
  • 5. Terry Taewoong Um (terry.t.um@gmail.com) 5 EXAMPLE CASE - Reduce the dimension from 800 to 40 by using a feature selection or dim. reduction technique ☞ What you did here is “Finding a good representation” - Then, you may apply a classification methods to classify 10 classes • You may have several ways to do it • But, what if - You have no idea for feature selection? - The dimension is much higher than 800 and you have more classes. MNIST dataset (65000spls * 784dim) MNIST dataset (60000spls * 1024dim)
  • 6. Terry Taewoong Um (terry.t.um@gmail.com) 6 CLASSIFICATION RESULTS error rate : 28% → 15% → 8% (2010) (2014)(2012) http://rodrigob.github.io/are_we_there_yet/bu ild/classification_datasets_results.html
  • 7. Terry Taewoong Um (terry.t.um@gmail.com) 7 PARADIGM CHANGE Knowledge PRESENT Representation (Features) How can we find a good representation? IMAGE SPEECH Hand-Crafted Features
  • 8. Terry Taewoong Um (terry.t.um@gmail.com) 8 PARADIGM CHANGE IMAGE SPEECH Hand-Crafted Features Knowledge PRESENT Representation (Features) Can we learn a good representation (feature) for the target task as well?
  • 9. Terry Taewoong Um (terry.t.um@gmail.com) 9 UNSUPERVISED LEARNING “Convolutional deep belief networks for scalable unsupervised learning of hierarchical representation”, Lee et al., 2012
  • 10. Terry Taewoong Um (terry.t.um@gmail.com) 10 THREE TYPES OF DEEP LEARNING • Unsupervised learning method Autoencoder http://goo.gl/s6kmqY - Restricted Boltzmann Machine(RBM), Autoencoder, etc. - It helps to avoid local minima problem (It regularizes the training data) - But it is not necessary when we have large amount of data. (Drop-out is enough for regularization) • Convolutional Neural Network (ConvNet) • Recurrent Neural Network (RNN) + Long-Short Term Memory (LSTM) - ConvNet has shown outstanding performance in recognition tasks (image, speech) - ConvNet contains hierarchical abstraction process called pooling. - RNN+LSTM makes use of long-term memory → Good for time-series data - RNN is a generative model: It can generate new data
  • 11. Terry Taewoong Um (terry.t.um@gmail.com) CONTENTS 11 2. DNN with TensorFlow Thanks to Sungjoon Choi https://github.com/sjchoi86/
  • 12. Terry Taewoong Um (terry.t.um@gmail.com) 12 DEEP LEARNING LIBRARIES
  • 13. Terry Taewoong Um (terry.t.um@gmail.com) 13 DEEP LEARNING LIBRARY
  • 14. Terry Taewoong Um (terry.t.um@gmail.com) 14 DEEP LEARNING LIBRARY • Karpathy’s Recommendation
  • 15. Terry Taewoong Um (terry.t.um@gmail.com) 15 BASIC WORKFLOW OF TF 1. Load data 2. Define the NN structure 3. Set optimization parameters 4. Run! https://github.com/terryum/TensorFlow_Exercises
  • 16. Terry Taewoong Um (terry.t.um@gmail.com) 16 EXAMPLE 1 https://github.com/terryum/TensorFlow_Exercises
  • 17. Terry Taewoong Um (terry.t.um@gmail.com) 17 1. LOAD DATA https://github.com/terryum/TensorFlow_Exercises/blob/ master/2_LogisticRegression_MNIST_160516.ipynb
  • 18. Terry Taewoong Um (terry.t.um@gmail.com) 18 1. LOAD DATA
  • 19. Terry Taewoong Um (terry.t.um@gmail.com) 19 2. DEFINE THE NN STRUCTURE 3. SET OPTIMIZATION PARAMETERS
  • 20. Terry Taewoong Um (terry.t.um@gmail.com) 20 4. RUN
  • 21. Terry Taewoong Um (terry.t.um@gmail.com) 21 4. RUN (C.F.)
  • 22. Terry Taewoong Um (terry.t.um@gmail.com) 22 EXAMPLE 2 https://github.com/terryum/TensorFlow_Exercises
  • 23. Terry Taewoong Um (terry.t.um@gmail.com) 23 NEURAL NETWORK Hugo Larochelle, http://www.dmi.usherb.ca/~larocheh/index_en.html • Activation functions http://goo.gl/qMQk5H • Basic NN structure
  • 24. Terry Taewoong Um (terry.t.um@gmail.com) 24 1. LOAD DATA https://github.com/terryum/TensorFlow_Exercises/blob/ master/3a_MLP_MNIST_160516.ipynb
  • 25. Terry Taewoong Um (terry.t.um@gmail.com) 25 2. DEFINE THE NN STRUCTURE
  • 26. Terry Taewoong Um (terry.t.um@gmail.com) 26 3. SET OPTIMIZATION PARAMETERS
  • 27. Terry Taewoong Um (terry.t.um@gmail.com) 27 4. RUN
  • 28. Terry Taewoong Um (terry.t.um@gmail.com) 28 EXAMPLE 3 https://github.com/terryum/TensorFlow_Exercises
  • 29. Terry Taewoong Um (terry.t.um@gmail.com) 29 CONVOLUTION http://colah.github.io/posts/2014-07- Understanding-Convolutions/ http://www.wildml.com/2015/11/understanding-convolutional-neural-networks-for-nlp/
  • 30. Terry Taewoong Um (terry.t.um@gmail.com) 30 CONVOLUTIONAL NN • How can we deal with real images which is much bigger than MNIST digit images? - Use not fully-connected, but locally-connected NN - Use convolutions to get various feature maps - Abstract the results into higher layer by using pooling - Fine tune with fully-connected NN https://goo.gl/G7kBjI https://goo.gl/Xswsbd http://goo.gl/5OR5oH
  • 31. Terry Taewoong Um (terry.t.um@gmail.com) 31 1. LOAD DATA https://github.com/terryum/TensorFlow_Exercises/blob/ master/4a_CNN_MNIST_160517.ipynb
  • 32. Terry Taewoong Um (terry.t.um@gmail.com) 32 2. DEFINE THE NN STRUCTURE
  • 33. Terry Taewoong Um (terry.t.um@gmail.com) 33 2. DEFINE THE NN STRUCTURE
  • 34. Terry Taewoong Um (terry.t.um@gmail.com) 34 3. SET OPTIMIZATION PARAMETERS
  • 35. Terry Taewoong Um (terry.t.um@gmail.com) 35 4. RUN
  • 36. Terry Taewoong Um (terry.t.um@gmail.com) 36 4. RUN (C.F.)
  • 37. Terry Taewoong Um (terry.t.um@gmail.com) 37 Thank you https://www.facebook.com/terryum http://terryum.io/ http://t-robotics.blogspot.kr/