TensorFlow Tutorial Part2
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1. The document discusses TensorFlow tutorials for building machine learning models including logistic regression, multi-layer perceptrons (MLPs), and convolutional neural networks (CNNs). 2. It outlines the steps to load a custom dataset, define each model type, define necessary functions, and train each model. 3. The tutorials cover loading packages, defining the models, training functions, and comparing the different model types for classification tasks.
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2016년 11월 14일에 서울시립대학교 대학원 수업에서 진행한 텐서플로 걸음마 슬라이드입니다. 한국에서 출판된 "텐서플로 첫걸음"이라는 책을 바탕으로 만들었습니다. TensorFlow에 대한 간략한 설명과 5가지 예제 코드를 다룹니다. 특히 그 중 MNIST 데이터셋을 CNN으로 분류하는 과정을 자세히 설명했습니다 ^^
TensorFlow
TensorFlowSang-Houn Choi TensorFlow에 대한 분석 내용
- TensorFlow?
- 배경
- DistBelief
- Tutorial - Logistic regression
- TensorFlow - 내부적으로는
- Tutorial - CNN, RNN
- Benchmarks
- 다른 오픈 소스들
- TensorFlow를 고려한다면
- 설치
- 참고 자료
Neural Networks with Google TensorFlow
Neural Networks with Google TensorFlowDarshan Patel This slides explains how Convolution Neural Networks can be coded using Google TensorFlow.
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Deep Learning - Convolutional Neural Networks - Architectural Zoo
Deep Learning - Convolutional Neural Networks - Architectural ZooChristian Perone This document discusses different convolutional neural network architectures including traditional architectures using convolutional, pooling, and fully connected layers, siamese networks for learning visual similarity, dense prediction networks for tasks like semantic segmentation and image colorization, video classification networks, music recommendation networks, and networks for tasks like object localization, detection, and alignment. It provides examples of specific networks that have been applied to each type of architecture.
Deep Learning - Convolutional Neural Networks
Deep Learning - Convolutional Neural NetworksChristian Perone This document provides an agenda for a presentation on deep learning, neural networks, convolutional neural networks, and interesting applications. The presentation will include introductions to deep learning and how it differs from traditional machine learning by learning feature representations from data. It will cover the history of neural networks and breakthroughs that enabled training of deeper models. Convolutional neural network architectures will be overviewed, including convolutional, pooling, and dense layers. Applications like recommendation systems, natural language processing, and computer vision will also be discussed. There will be a question and answer section.
Robot Era
Robot Erapintailfp7 The document discusses the Robot-Era project which aims to develop and demonstrate advanced robotic services integrated into smart environments to provide independent living assistance for elderly people. The project involves different robots cooperating with each other and being coordinated by a configuration module. One of the services tested was a shopping delivery service where an elderly user created a shopping list using a GUI or speech and received their items delivered by a robot. Localization of users was performed using a Kalman filter fusing data from wearable sensors and presence sensors.
Recent Trends in Neural Net Policy Learning
Recent Trends in Neural Net Policy LearningSungjoon Choi This paper proposes a method called constrained guided policy search to train a real-world robot to perform contact-rich manipulation skills without requiring prior knowledge of dynamics. The method uses iterative linear quadratic regulation to define a guiding distribution for samples that are then used to train a neural network policy via importance sampled policy search. The trained policy allows a PR2 robot to successfully accomplish tasks such as stacking blocks, threading rings onto pegs, and assembling objects.
Connection between Bellman equation and Markov Decision Processes
Connection between Bellman equation and Markov Decision ProcessesSungjoon Choi In this slide, we investigate the relationship between Bellman equation and Markov decision processes (MDPs). While the principle of optimality directly gives us the relationships, we derive this connection by solving the KKT conditions of infinite horizon optimal control problems.
Value iteration networks
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"Semantic Segmentation for Scene Understanding: Algorithms and Implementation...
"Semantic Segmentation for Scene Understanding: Algorithms and Implementation...Edge AI and Vision Alliance For the full video of this presentation, please visit:
http://www.embedded-vision.com/platinum-members/auvizsystems/embedded-vision-training/videos/pages/may-2016-embedded-vision-summit
For more information about embedded vision, please visit:
http://www.embedded-vision.com
Nagesh Gupta, Founder and CEO of Auviz Systems, presents the "Semantic Segmentation for Scene Understanding: Algorithms and Implementations" tutorial at the May 2016 Embedded Vision Summit.
Recent research in deep learning provides powerful tools that begin to address the daunting problem of automated scene understanding. Modifying deep learning methods, such as CNNs, to classify pixels in a scene with the help of the neighboring pixels has provided very good results in semantic segmentation. This technique provides a good starting point towards understanding a scene. A second challenge is how such algorithms can be deployed on embedded hardware at the performance required for real-world applications. A variety of approaches are being pursued for this, including GPUs, FPGAs, and dedicated hardware.
This talk provides insights into deep learning solutions for semantic segmentation, focusing on current state of the art algorithms and implementation choices. Gupta discusses the effect of porting these algorithms to fixed-point representation and the pros and cons of implementing them on FPGAs.
Scene classification using Convolutional Neural Networks - Jayani Withanawasam
Scene classification using Convolutional Neural Networks - Jayani WithanawasamWithTheBest The document discusses scene classification using convolutional neural networks (CNNs). It begins with an outline of the topic, then provides background on computer vision as an AI problem and the importance and challenges of scene classification. It introduces CNNs as a deep learning technique for visual pattern recognition, describing their hierarchical organization and components like convolution and pooling layers. The document also discusses traditional machine learning approaches versus deep learning for scene classification and frameworks like Caffe that can be used to implement CNNs.
Differentiable Neural Computer
Differentiable Neural ComputerTaehoon Kim Hybrid computing using a neural network with dynamic external memory
http://www.nature.com/nature/journal/v538/n7626/full/nature20101.html
"Semantic Segmentation for Scene Understanding: Algorithms and Implementation...
"Semantic Segmentation for Scene Understanding: Algorithms and Implementation...Edge AI and Vision Alliance
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