A comprehensive tutorial on Convolutional Neural Networks (CNN) which talks about the motivation behind CNNs and Deep Learning in general, followed by a description of the various components involved in a typical CNN layer. It explains the theory involved with the different variants used in practice and also, gives a big picture of the whole network by putting everything together.
Next, there's a discussion of the various state-of-the-art frameworks being used to implement CNNs to tackle real-world classification and regression problems.
Finally, the implementation of the CNNs is demonstrated by implementing the paper 'Age ang Gender Classification Using Convolutional Neural Networks' by Hassner (2015).
What Is Deep Learning? | Introduction to Deep Learning | Deep Learning Tutori...Simplilearn
This Deep Learning Presentation will help you in understanding what is Deep learning, why do we need Deep learning, applications of Deep Learning along with a detailed explanation on Neural Networks and how these Neural Networks work. Deep learning is inspired by the integral function of the human brain specific to artificial neural networks. These networks, which represent the decision-making process of the brain, use complex algorithms that process data in a non-linear way, learning in an unsupervised manner to make choices based on the input. This Deep Learning tutorial is ideal for professionals with beginners to intermediate levels of experience. Now, let us dive deep into this topic and understand what Deep learning actually is.
Below topics are explained in this Deep Learning Presentation:
1. What is Deep Learning?
2. Why do we need Deep Learning?
3. Applications of Deep Learning
4. What is Neural Network?
5. Activation Functions
6. Working of Neural Network
Simplilearn’s Deep Learning course will transform you into an expert in deep learning techniques using TensorFlow, the open-source software library designed to conduct machine learning & deep neural network research. With our deep learning course, you’ll master deep learning and TensorFlow concepts, learn to implement algorithms, build artificial neural networks and traverse layers of data abstraction to understand the power of data and prepare you for your new role as deep learning scientist.
Why Deep Learning?
It is one of the most popular software platforms used for deep learning and contains powerful tools to help you build and implement artificial neural networks.
Advancements in deep learning are being seen in smartphone applications, creating efficiencies in the power grid, driving advancements in healthcare, improving agricultural yields, and helping us find solutions to climate change. With this Tensorflow course, you’ll build expertise in deep learning models, learn to operate TensorFlow to manage neural networks and interpret the results.
You can gain in-depth knowledge of Deep Learning by taking our Deep Learning certification training course. With Simplilearn’s Deep Learning course, you will prepare for a career as a Deep Learning engineer as you master concepts and techniques including supervised and unsupervised learning, mathematical and heuristic aspects, and hands-on modeling to develop algorithms.
There is booming demand for skilled deep learning engineers across a wide range of industries, making this deep learning course with TensorFlow training well-suited for professionals at the intermediate to advanced level of experience. We recommend this deep learning online course particularly for the following professionals:
1. Software engineers
2. Data scientists
3. Data analysts
4. Statisticians with an interest in deep learning
In machine learning, a convolutional neural network is a class of deep, feed-forward artificial neural networks that have successfully been applied fpr analyzing visual imagery.
A comprehensive tutorial on Convolutional Neural Networks (CNN) which talks about the motivation behind CNNs and Deep Learning in general, followed by a description of the various components involved in a typical CNN layer. It explains the theory involved with the different variants used in practice and also, gives a big picture of the whole network by putting everything together.
Next, there's a discussion of the various state-of-the-art frameworks being used to implement CNNs to tackle real-world classification and regression problems.
Finally, the implementation of the CNNs is demonstrated by implementing the paper 'Age ang Gender Classification Using Convolutional Neural Networks' by Hassner (2015).
What Is Deep Learning? | Introduction to Deep Learning | Deep Learning Tutori...Simplilearn
This Deep Learning Presentation will help you in understanding what is Deep learning, why do we need Deep learning, applications of Deep Learning along with a detailed explanation on Neural Networks and how these Neural Networks work. Deep learning is inspired by the integral function of the human brain specific to artificial neural networks. These networks, which represent the decision-making process of the brain, use complex algorithms that process data in a non-linear way, learning in an unsupervised manner to make choices based on the input. This Deep Learning tutorial is ideal for professionals with beginners to intermediate levels of experience. Now, let us dive deep into this topic and understand what Deep learning actually is.
Below topics are explained in this Deep Learning Presentation:
1. What is Deep Learning?
2. Why do we need Deep Learning?
3. Applications of Deep Learning
4. What is Neural Network?
5. Activation Functions
6. Working of Neural Network
Simplilearn’s Deep Learning course will transform you into an expert in deep learning techniques using TensorFlow, the open-source software library designed to conduct machine learning & deep neural network research. With our deep learning course, you’ll master deep learning and TensorFlow concepts, learn to implement algorithms, build artificial neural networks and traverse layers of data abstraction to understand the power of data and prepare you for your new role as deep learning scientist.
Why Deep Learning?
It is one of the most popular software platforms used for deep learning and contains powerful tools to help you build and implement artificial neural networks.
Advancements in deep learning are being seen in smartphone applications, creating efficiencies in the power grid, driving advancements in healthcare, improving agricultural yields, and helping us find solutions to climate change. With this Tensorflow course, you’ll build expertise in deep learning models, learn to operate TensorFlow to manage neural networks and interpret the results.
You can gain in-depth knowledge of Deep Learning by taking our Deep Learning certification training course. With Simplilearn’s Deep Learning course, you will prepare for a career as a Deep Learning engineer as you master concepts and techniques including supervised and unsupervised learning, mathematical and heuristic aspects, and hands-on modeling to develop algorithms.
There is booming demand for skilled deep learning engineers across a wide range of industries, making this deep learning course with TensorFlow training well-suited for professionals at the intermediate to advanced level of experience. We recommend this deep learning online course particularly for the following professionals:
1. Software engineers
2. Data scientists
3. Data analysts
4. Statisticians with an interest in deep learning
In machine learning, a convolutional neural network is a class of deep, feed-forward artificial neural networks that have successfully been applied fpr analyzing visual imagery.
Provides a brief overview of what machine learning is, how it works (theory), how to prepare data for a machine learning problem, an example case study, and additional resources.
Convolutional neural network (CNN / ConvNet's) is a part of Computer Vision. Machine Learning Algorithm. Image Classification, Image Detection, Digit Recognition, and many more. https://technoelearn.com .
It’s long ago, approx. 30 years, since AI was not only a topic for Science-Fiction writers, but also a major research field surrounded with huge hopes and investments. But the over-inflated expectations ended in a subsequent crash and followed by a period of absent funding and interest – the so-called AI winter. However, the last 3 years changed everything – again. Deep learning, a machine learning technique inspired by the human brain, successfully crushed one benchmark after another and tech companies, like Google, Facebook and Microsoft, started to invest billions in AI research. “The pace of progress in artificial general intelligence is incredible fast” (Elon Musk – CEO Tesla & SpaceX) leading to an AI that “would be either the best or the worst thing ever to happen to humanity” (Stephen Hawking – Physicist).
What sparked this new Hype? How is Deep Learning different from previous approaches? Are the advancing AI technologies really a threat for humanity? Let’s look behind the curtain and unravel the reality. This talk will explore why Sundar Pichai (CEO Google) recently announced that “machine learning is a core transformative way by which Google is rethinking everything they are doing” and explain why "Deep Learning is probably one of the most exciting things that is happening in the computer industry” (Jen-Hsun Huang – CEO NVIDIA).
Either a new AI “winter is coming” (Ned Stark – House Stark) or this new wave of innovation might turn out as the “last invention humans ever need to make” (Nick Bostrom – AI Philosoph). Or maybe it’s just another great technology helping humans to achieve more.
Computer vision has received great attention over the last two decades.
This research field is important not only in security-related software but also in the advanced interface between people and computers, advanced control methods, and many other areas.
Dataset Preparation
Abstract: This PDSG workshop introduces basic concepts on preparing a dataset for training a model. Concepts covered are data wrangling, replacing missing values, categorical variable conversion, and feature scaling.
Level: Fundamental
Requirements: No prior programming or statistics knowledge required.
Deep learning (also known as deep structured learning or hierarchical learning) is the application of artificial neural networks (ANNs) to learning tasks that contain more than one hidden layer. Deep learning is part of a broader family of machine learning methods based on learning data representations, as opposed to task-specific algorithms. Learning can be supervised, partially supervised or unsupervised.
Hanjun Dai, PhD Student, School of Computational Science and Engineering, Geo...MLconf
Graph Representation Learning with Deep Embedding Approach:
Graphs are commonly used data structure for representing the real-world relationships, e.g., molecular structure, knowledge graphs, social and communication networks. The effective encoding of graphical information is essential to the success of such applications. In this talk I’ll first describe a general deep learning framework, namely structure2vec, for end to end graph feature representation learning. Then I’ll present the direct application of this model on graph problems on different scales, including community detection and molecule graph classification/regression. We then extend the embedding idea to temporal evolving user-product interaction graph for recommendation. Finally I’ll present our latest work on leveraging the reinforcement learning technique for graph combinatorial optimization, including vertex cover problem for social influence maximization and traveling salesman problem for scheduling management.
For the full video of this presentation, please visit:
http://www.embedded-vision.com/platinum-members/embedded-vision-alliance/embedded-vision-training/videos/pages/sept-2016-member-meeting-mit
For more information about embedded vision, please visit:
http://www.embedded-vision.com
Vivienne Sze, Assistant Professor at MIT, delivers the presentation "Energy-efficient Hardware for Embedded Vision and Deep Convolutional Neural Networks" at the September 2016 Embedded Vision Alliance Member Meeting. Sze describes the results of her team's recent research on optimized hardware for deep learning.
Provides a brief overview of what machine learning is, how it works (theory), how to prepare data for a machine learning problem, an example case study, and additional resources.
Convolutional neural network (CNN / ConvNet's) is a part of Computer Vision. Machine Learning Algorithm. Image Classification, Image Detection, Digit Recognition, and many more. https://technoelearn.com .
It’s long ago, approx. 30 years, since AI was not only a topic for Science-Fiction writers, but also a major research field surrounded with huge hopes and investments. But the over-inflated expectations ended in a subsequent crash and followed by a period of absent funding and interest – the so-called AI winter. However, the last 3 years changed everything – again. Deep learning, a machine learning technique inspired by the human brain, successfully crushed one benchmark after another and tech companies, like Google, Facebook and Microsoft, started to invest billions in AI research. “The pace of progress in artificial general intelligence is incredible fast” (Elon Musk – CEO Tesla & SpaceX) leading to an AI that “would be either the best or the worst thing ever to happen to humanity” (Stephen Hawking – Physicist).
What sparked this new Hype? How is Deep Learning different from previous approaches? Are the advancing AI technologies really a threat for humanity? Let’s look behind the curtain and unravel the reality. This talk will explore why Sundar Pichai (CEO Google) recently announced that “machine learning is a core transformative way by which Google is rethinking everything they are doing” and explain why "Deep Learning is probably one of the most exciting things that is happening in the computer industry” (Jen-Hsun Huang – CEO NVIDIA).
Either a new AI “winter is coming” (Ned Stark – House Stark) or this new wave of innovation might turn out as the “last invention humans ever need to make” (Nick Bostrom – AI Philosoph). Or maybe it’s just another great technology helping humans to achieve more.
Computer vision has received great attention over the last two decades.
This research field is important not only in security-related software but also in the advanced interface between people and computers, advanced control methods, and many other areas.
Dataset Preparation
Abstract: This PDSG workshop introduces basic concepts on preparing a dataset for training a model. Concepts covered are data wrangling, replacing missing values, categorical variable conversion, and feature scaling.
Level: Fundamental
Requirements: No prior programming or statistics knowledge required.
Deep learning (also known as deep structured learning or hierarchical learning) is the application of artificial neural networks (ANNs) to learning tasks that contain more than one hidden layer. Deep learning is part of a broader family of machine learning methods based on learning data representations, as opposed to task-specific algorithms. Learning can be supervised, partially supervised or unsupervised.
Hanjun Dai, PhD Student, School of Computational Science and Engineering, Geo...MLconf
Graph Representation Learning with Deep Embedding Approach:
Graphs are commonly used data structure for representing the real-world relationships, e.g., molecular structure, knowledge graphs, social and communication networks. The effective encoding of graphical information is essential to the success of such applications. In this talk I’ll first describe a general deep learning framework, namely structure2vec, for end to end graph feature representation learning. Then I’ll present the direct application of this model on graph problems on different scales, including community detection and molecule graph classification/regression. We then extend the embedding idea to temporal evolving user-product interaction graph for recommendation. Finally I’ll present our latest work on leveraging the reinforcement learning technique for graph combinatorial optimization, including vertex cover problem for social influence maximization and traveling salesman problem for scheduling management.
For the full video of this presentation, please visit:
http://www.embedded-vision.com/platinum-members/embedded-vision-alliance/embedded-vision-training/videos/pages/sept-2016-member-meeting-mit
For more information about embedded vision, please visit:
http://www.embedded-vision.com
Vivienne Sze, Assistant Professor at MIT, delivers the presentation "Energy-efficient Hardware for Embedded Vision and Deep Convolutional Neural Networks" at the September 2016 Embedded Vision Alliance Member Meeting. Sze describes the results of her team's recent research on optimized hardware for deep learning.
Recent Progress on Object Detection_20170331Jihong Kang
This slide provides a brief summary of recent progress on object detection using deep learning.
The concept of selected previous works(R-CNN series/YOLO/SSD) and 6 recent papers (uploaded to the Arxiv between Dec/2016 and Mar/2017) are introduced in this slide.
Most papers are focusing on improving the performance of small object detection.
Improving Hardware Efficiency for DNN ApplicationsChester Chen
Speaker: Dr. Hai (Helen) Li is the Clare Boothe Luce Associate Professor of Electrical and Computer Engineering and Co-director of the Duke Center for Evolutionary Intelligence at Duke University
In this talk, I will introduce a few recent research spotlights by the Duke Center for Evolutionary Intelligence. The talk will start with the structured sparsity learning (SSL) method which attempts to learn a compact structure from a bigger DNN to reduce computation cost. It generates a regularized structure with high execution efficiency. Our experiments on CPU, GPU, and FPGA platforms show on average 3~5 times speedup of convolutional layer computation of AlexNet. Then, the implementation and acceleration of DNN applications on mobile computing systems will be introduced. MoDNN is a local distributed system which partitions DNN models onto several mobile devices to accelerate computations. ApesNet is an efficient pixel-wise segmentation network, which understands road scenes in real-time, and has achieved promising accuracy. Our prospects on the adoption of emerging technology will also be given at the end of this talk, offering the audiences an alternative thinking about the future evolution and revolution of modern computing systems.
Super resolution in deep learning era - Jaejun YooJaeJun Yoo
Abstract (Eng/Kor):
Image restoration (IR) is one of the fundamental problems, which includes denoising, deblurring, super-resolution, etc. Among those, in today's talk, I will more focus on the super-resolution task. There are two main streams in the super-resolution studies; a traditional model-based optimization and a discriminative learning method. I will present the pros and cons of both methods and their recent developments in the research field. Finally, I will provide a mathematical view that explains both methods in a single holistic framework, while achieving the best of both worlds. The last slide summarizes the remaining problems that are yet to be solved in the field.
영상 복원(Image restoration, IR)은 low-level vision에서 매우 중요하게 다루는 근본적인 문제 중 하나로서 denoising, deblurring, super-resolution 등의 다양한 영상 처리 문제를 포괄합니다. 오늘 발표에서는 영상 복원 분야 중에서도 super-resolution 문제에 대해 집중적으로 다루겠습니다. 전통적인 model-based optimization 방식과 deep learning을 적용하여 문제를 푸는 방식에 대해, 각각의 장단점과 최신 연구 발전 흐름을 소개하겠습니다. 마지막으로는 이 둘을 하나로 잇는 통일된 관점을 제시하고 관련 연구들 살펴본 후, super-resolution 분야에서 아직 남아있는 문제점들을 정리하겠습니다.
Ukrainian Catholic University
Faculty of Applied Sciences
Data Science Master Program
January 21st
Abstract. In this paper we review different approaches to use probabilistic methods in existing AutoML solutions using Reinforcement Learning. We focus on providing additional knowledge about probability distribution provided to Reinforcement Learning agents solving Neural Architecture Search tasks. Based on the results of the research we come with an agent designed to model Neural Architectures for image classification tasks.
http://imatge-upc.github.io/telecombcn-2016-dlcv/
Deep learning technologies are at the core of the current revolution in artificial intelligence for multimedia data analysis. The convergence of big annotated data and affordable GPU hardware has allowed the training of neural networks for data analysis tasks which had been addressed until now with hand-crafted features. Architectures such as convolutional neural networks, recurrent neural networks and Q-nets for reinforcement learning have shaped a brand new scenario in signal processing. This course will cover the basic principles and applications of deep learning to computer vision problems, such as image classification, object detection or text captioning.
Basics of RNNs and its applications with following papers:
- Generating Sequences With Recurrent Neural Networks, 2013
- Show and Tell: A Neural Image Caption Generator, 2014
- Show, Attend and Tell: Neural Image Caption Generation with Visual Attention, 2015
- DenseCap: Fully Convolutional Localization Networks for Dense Captioning, 2015
- Deep Tracking- Seeing Beyond Seeing Using Recurrent Neural Networks, 2016
- Robust Modeling and Prediction in Dynamic Environments Using Recurrent Flow Networks, 2016
- Social LSTM- Human Trajectory Prediction in Crowded Spaces, 2016
- DESIRE- Distant Future Prediction in Dynamic Scenes with Interacting Agents, 2017
- Predictive State Recurrent Neural Networks, 2017
Uncertainty in Deep Learning, Gal (2016)
Representing Inferential Uncertainty in Deep Neural Networks Through Sampling, McClure & Kriegeskorte (2017)
Uncertainty-Aware Reinforcement Learning from Collision Avoidance, Khan et al. (2016)
Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles, Lakshminarayanan et al. (2017)
What Uncertainties Do We Need in Bayesian Deep Learning for Computer Vision?, Kendal & Gal (2017)
Uncertainty-Aware Learning from Demonstration Using Mixture Density Networks with Sampling-Free Variance Modeling, Choi et al. (2017)
Bayesian Uncertainty Estimation for Batch Normalized Deep Networks, Anonymous (2018)
1. Y. Gal, Uncertainty in Deep Learning, 2016
2. P. McClure, Representing Inferential Uncertainty in Deep Neural Networks Through Sampling, 2017
3. G. Khan et al., Uncertainty-Aware Reinforcement
Learning from Collision Avoidance, 2016
4. B. Lakshminarayanan et al., Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles, 2017
5. A. Kendal and Y. Gal, What Uncertainties Do We Need in
Bayesian Deep Learning for Computer Vision?, 2017
6. S. Choi et al., Uncertainty-Aware Learning from Demonstration Using Mixture Density Networks with Sampling-Free Variance Modeling, 2017
7. Anonymous, Bayesian Uncertainty Estimation for
Batch Normalized Deep Networks, 2017
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.
CNN is not just used for efficient feature extractor but this paper finds an analogy between operations in CNN and value iteration algorithm in reinforcement learning.
Deep Learning in Robotics
- There are two major branches in applying deep learning techniques in robotics.
- One is to combine DL with Q learning algorithms. For example, awesome work on playing Atari games done by deep mind is a representative study. While this approach can effectively handle several problems that can hardly be solved via traditional methods, these methods are not appropriate for real manipulators as it often requires an enormous number of training data.
- The other branch of work uses a concept of guided policy search. It combines trajectory optimization methods with supervised learning algorithm like CNNs to come up with a robust 'policy' function that can actually be used in real robots, e.g., Baxter of PR2.
Explore the innovative world of trenchless pipe repair with our comprehensive guide, "The Benefits and Techniques of Trenchless Pipe Repair." This document delves into the modern methods of repairing underground pipes without the need for extensive excavation, highlighting the numerous advantages and the latest techniques used in the industry.
Learn about the cost savings, reduced environmental impact, and minimal disruption associated with trenchless technology. Discover detailed explanations of popular techniques such as pipe bursting, cured-in-place pipe (CIPP) lining, and directional drilling. Understand how these methods can be applied to various types of infrastructure, from residential plumbing to large-scale municipal systems.
Ideal for homeowners, contractors, engineers, and anyone interested in modern plumbing solutions, this guide provides valuable insights into why trenchless pipe repair is becoming the preferred choice for pipe rehabilitation. Stay informed about the latest advancements and best practices in the field.
Final project report on grocery store management system..pdfKamal Acharya
In today’s fast-changing business environment, it’s extremely important to be able to respond to client needs in the most effective and timely manner. If your customers wish to see your business online and have instant access to your products or services.
Online Grocery Store is an e-commerce website, which retails various grocery products. This project allows viewing various products available enables registered users to purchase desired products instantly using Paytm, UPI payment processor (Instant Pay) and also can place order by using Cash on Delivery (Pay Later) option. This project provides an easy access to Administrators and Managers to view orders placed using Pay Later and Instant Pay options.
In order to develop an e-commerce website, a number of Technologies must be studied and understood. These include multi-tiered architecture, server and client-side scripting techniques, implementation technologies, programming language (such as PHP, HTML, CSS, JavaScript) and MySQL relational databases. This is a project with the objective to develop a basic website where a consumer is provided with a shopping cart website and also to know about the technologies used to develop such a website.
This document will discuss each of the underlying technologies to create and implement an e- commerce website.
Water scarcity is the lack of fresh water resources to meet the standard water demand. There are two type of water scarcity. One is physical. The other is economic water scarcity.
Overview of the fundamental roles in Hydropower generation and the components involved in wider Electrical Engineering.
This paper presents the design and construction of hydroelectric dams from the hydrologist’s survey of the valley before construction, all aspects and involved disciplines, fluid dynamics, structural engineering, generation and mains frequency regulation to the very transmission of power through the network in the United Kingdom.
Author: Robbie Edward Sayers
Collaborators and co editors: Charlie Sims and Connor Healey.
(C) 2024 Robbie E. Sayers
Quality defects in TMT Bars, Possible causes and Potential Solutions.PrashantGoswami42
Maintaining high-quality standards in the production of TMT bars is crucial for ensuring structural integrity in construction. Addressing common defects through careful monitoring, standardized processes, and advanced technology can significantly improve the quality of TMT bars. Continuous training and adherence to quality control measures will also play a pivotal role in minimizing these defects.
NO1 Uk best vashikaran specialist in delhi vashikaran baba near me online vas...Amil Baba Dawood bangali
Contact with Dawood Bhai Just call on +92322-6382012 and we'll help you. We'll solve all your problems within 12 to 24 hours and with 101% guarantee and with astrology systematic. If you want to take any personal or professional advice then also you can call us on +92322-6382012 , ONLINE LOVE PROBLEM & Other all types of Daily Life Problem's.Then CALL or WHATSAPP us on +92322-6382012 and Get all these problems solutions here by Amil Baba DAWOOD BANGALI
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Industrial Training at Shahjalal Fertilizer Company Limited (SFCL)MdTanvirMahtab2
This presentation is about the working procedure of Shahjalal Fertilizer Company Limited (SFCL). A Govt. owned Company of Bangladesh Chemical Industries Corporation under Ministry of Industries.
About
Indigenized remote control interface card suitable for MAFI system CCR equipment. Compatible for IDM8000 CCR. Backplane mounted serial and TCP/Ethernet communication module for CCR remote access. IDM 8000 CCR remote control on serial and TCP protocol.
• Remote control: Parallel or serial interface.
• Compatible with MAFI CCR system.
• Compatible with IDM8000 CCR.
• Compatible with Backplane mount serial communication.
• Compatible with commercial and Defence aviation CCR system.
• Remote control system for accessing CCR and allied system over serial or TCP.
• Indigenized local Support/presence in India.
• Easy in configuration using DIP switches.
Technical Specifications
Indigenized remote control interface card suitable for MAFI system CCR equipment. Compatible for IDM8000 CCR. Backplane mounted serial and TCP/Ethernet communication module for CCR remote access. IDM 8000 CCR remote control on serial and TCP protocol.
Key Features
Indigenized remote control interface card suitable for MAFI system CCR equipment. Compatible for IDM8000 CCR. Backplane mounted serial and TCP/Ethernet communication module for CCR remote access. IDM 8000 CCR remote control on serial and TCP protocol.
• Remote control: Parallel or serial interface
• Compatible with MAFI CCR system
• Copatiable with IDM8000 CCR
• Compatible with Backplane mount serial communication.
• Compatible with commercial and Defence aviation CCR system.
• Remote control system for accessing CCR and allied system over serial or TCP.
• Indigenized local Support/presence in India.
Application
• Remote control: Parallel or serial interface.
• Compatible with MAFI CCR system.
• Compatible with IDM8000 CCR.
• Compatible with Backplane mount serial communication.
• Compatible with commercial and Defence aviation CCR system.
• Remote control system for accessing CCR and allied system over serial or TCP.
• Indigenized local Support/presence in India.
• Easy in configuration using DIP switches.
Saudi Arabia stands as a titan in the global energy landscape, renowned for its abundant oil and gas resources. It's the largest exporter of petroleum and holds some of the world's most significant reserves. Let's delve into the top 10 oil and gas projects shaping Saudi Arabia's energy future in 2024.
COLLEGE BUS MANAGEMENT SYSTEM PROJECT REPORT.pdfKamal Acharya
The College Bus Management system is completely developed by Visual Basic .NET Version. The application is connect with most secured database language MS SQL Server. The application is develop by using best combination of front-end and back-end languages. The application is totally design like flat user interface. This flat user interface is more attractive user interface in 2017. The application is gives more important to the system functionality. The application is to manage the student’s details, driver’s details, bus details, bus route details, bus fees details and more. The application has only one unit for admin. The admin can manage the entire application. The admin can login into the application by using username and password of the admin. The application is develop for big and small colleges. It is more user friendly for non-computer person. Even they can easily learn how to manage the application within hours. The application is more secure by the admin. The system will give an effective output for the VB.Net and SQL Server given as input to the system. The compiled java program given as input to the system, after scanning the program will generate different reports. The application generates the report for users. The admin can view and download the report of the data. The application deliver the excel format reports. Because, excel formatted reports is very easy to understand the income and expense of the college bus. This application is mainly develop for windows operating system users. In 2017, 73% of people enterprises are using windows operating system. So the application will easily install for all the windows operating system users. The application-developed size is very low. The application consumes very low space in disk. Therefore, the user can allocate very minimum local disk space for this application.
Automobile Management System Project Report.pdfKamal Acharya
The proposed project is developed to manage the automobile in the automobile dealer company. The main module in this project is login, automobile management, customer management, sales, complaints and reports. The first module is the login. The automobile showroom owner should login to the project for usage. The username and password are verified and if it is correct, next form opens. If the username and password are not correct, it shows the error message.
When a customer search for a automobile, if the automobile is available, they will be taken to a page that shows the details of the automobile including automobile name, automobile ID, quantity, price etc. “Automobile Management System” is useful for maintaining automobiles, customers effectively and hence helps for establishing good relation between customer and automobile organization. It contains various customized modules for effectively maintaining automobiles and stock information accurately and safely.
When the automobile is sold to the customer, stock will be reduced automatically. When a new purchase is made, stock will be increased automatically. While selecting automobiles for sale, the proposed software will automatically check for total number of available stock of that particular item, if the total stock of that particular item is less than 5, software will notify the user to purchase the particular item.
Also when the user tries to sale items which are not in stock, the system will prompt the user that the stock is not enough. Customers of this system can search for a automobile; can purchase a automobile easily by selecting fast. On the other hand the stock of automobiles can be maintained perfectly by the automobile shop manager overcoming the drawbacks of existing system.
3. What is deep learning?
3
“Deep learning is a branch of machine learning based on a set of
algorithms that attempt to model high-level abstractions in data by
using multiple processing layers, with complex structures or otherwise,
composed of multiple non-linear transformations.”
Wikipedia says:
Machine
Learning
High-level
abstraction Network
4. Is it brand new?
4
Neural Nets McCulloch & Pitt 1943
Perception Rosenblatt 1958
RNN Grossberg 1973
CNN Fukushima 1979
RBM Hinton 1999
DBN Hinton 2006
D-AE Vincent 2008
AlexNet Alex 2012
GoogLeNet Szegedy 2015
5. Deep architectures
5
Feed-Forward: multilayer neural nets, convolutional nets
Feed-Back: Stacked Sparse Coding, Deconvolutional Nets
Bi-Directional: Deep Boltzmann Machines, Stacked Auto-Encoders
Recurrent: Recurrent Nets, Long-Short Term Memory
7. CNN
7
CNNs are basically layers of convolutions followed by
subsampling and fully connected layers.
Intuitively speaking, convolutions and subsampling
layers works as feature extraction layers while a fully
connected layer classifies which category current input
belongs to using extracted features.
19. Gradient descent?
There are three variants of gradient descent
Differ in how much data we use to compute
gradient
We make a trade-off between the accuracy
and computing time
20. Batch gradient descent
In batch gradient decent, we use the entire
training dataset to compute the gradient.
21. Stochastic gradient descent
In stochastic gradient descent (SGD), the
gradient is computed from each training
sample, one by one.
22. Mini-batch gradient decent
In mini-batch gradient decent, we take the
best of both worlds.
Common mini-batch sizes range between 50
and 256 (but can vary).
23. Challenges
Choosing a proper learning rate is cumbersome.
Learning rate schedule
Avoiding getting trapped in suboptimal local
minima
26. Adagrad
It adapts the learning rate to the parameters,
performing larger updates for infrequent and
smaller updates for frequent parameters.
𝜃𝑡+1,𝑖 = 𝜃𝑡,𝑖 −
𝜂
𝐺𝑡,𝑖𝑖 + 𝜖
𝑔𝑡,𝑖
Performing larger updates for infrequent and
smaller updates for frequent parameters.
27. Adadelta
Adadelta is an extension of Adagrad that seeks
to reduce its monotonically decreasing learning
rate.
It restricts the window of accumulated past
gradients to some fixed size 𝑤.
𝐸 𝑔2
𝑡 = 𝛾𝐸 𝑔2
𝑡−1 + 1 − 𝛾 𝑔𝑡
2
𝐸 ∆𝜃2
𝑡 = 𝛾𝐸 ∆𝜃2
𝑡−1 + 1 − 𝛾 ∆𝜃𝑡
2
𝜃𝑡+1 = 𝜃𝑡 −
𝐸 ∆𝜃2
𝑡 + 𝜖
𝐸 𝑔2
𝑡 + 𝜖
𝑔𝑡
No learning rate!
78. Weakly Supervised Object Localization
78
Usually supervised learning of localization is annotated with bounding box
What if localization is possible with image label without bounding box
annotations?
Today’s seminar: Learning Deep Features for Discriminative
Localization
1512.04150v1 Zhou et al. 2015 CVPR2016
80. Class activation map (CAM)
80
• Identify important image regions by projecting back
the weights of output layer to convolutional feature
maps.
• CAMs can be generated for each class in single image.
• Regions for each categories are different in given image.
• palace, dome, church …
81. Results
81
• CAM on top 5 predictions on an image
• CAM for one object class in images
82. GAP vs. GMP
82
• Oquab et al. CVPR2015
Is object localization for free? weakly-supervised learning with convolutional neural
networks.
• Use global max pooling(GMP)
• Intuitive difference between GMP and GAP?
• GAP loss encourages identification on the extent of an object.
• GMP loss encourages it to identify just one discriminative part.
• GAP, average of a map maximized by finding all discriminative
parts of object
• if activations is all low, output of particular map reduces.
• GMP, low scores for all image regions except the most
discriminative part
• do not impact the score when perform MAX
pooling
83. GAP & GMP
83
• GAP (upper) vs GMP (lower)
• GAP outperforms GMP
• GAP highlights more complete
object regions and less
background noise.
• Loss for average pooling
benefits when the network
identifies all discriminative
regions of an object
85. Concept localization
85
Concept localization in weakly
labeled images
• Positive set: short phrase in text caption
• Negative set: randomly selected images
• Model catch the concept, phrases are
much more abstract than object name.
Weakly supervised text detector
• Positive set: 350 Google StreeView
images that contain text.
• Negative set: outdoor scene images in
SUN dataset
• Text highlighted without bounding box
annotations.
157. LSTM comes in!
157
Long Short Term Memory
This is just a standard RNN.
http://colah.github.io/posts/2015-08-Understanding-LSTMs/
158. LSTM comes in!
158
Long Short Term Memory
This is just a standard RNN.This is the LSTM!
http://colah.github.io/posts/2015-08-Understanding-LSTMs/
159. Overall Architecture
159
(Cell) state
Hidden State
Forget Gate
http://colah.github.io/posts/2015-08-Understanding-LSTMs/
Input Gate
Output Gate
Next (Cell) State
Next Hidden State
Input
Output
Output = Hidden state
162. VQA: Dataset and Problem definition
162
VQA dataset - Example
Q: How many dogs are seen?
Q: What animal is this?
Q: What color is the car?
Q: What is the mustache made of?Q: Is this vegetarian pizza?
163. Solving VQA
163
Approach
[Malinowski et al., 2015] [Ren et al., 2015] [Andres et al., 2015]
[Ma et al., 2015] [Jiang et al., 2015]
Various methods have been proposed
164. DPPnet
164
Motivation
Common pipeline of using deep learning for vision
CNN trained on ImageNet
Switch the final layer and fine-tune for the New Task
In VQA, Task is determined by a question
Observation:
166. DPPnet
166
Parameter Explosion
Number of parameter for fc-layer (R):
DynamicParameterLayer
Question Feature
Predicted Parameter
M
N
Q
P
: Dimension of hidden state
fc-layer
N=Q×P R=Q×P×M Q=1000, P=1000, M=500
For example:
R=500,000,000
1.86GB for single layer
Number of parameters for
VGG19: 144,000,000
167. DPPnet
167
Parameter Explosion
Number of parameter for fc-layer (R):
DynamicParameterLayer
Question Feature
Predicted Parameter
M
N
Q
P
: Dimension of hidden state
fc-layer
Solution:
R=Q×P×M R= N×M
N=Q×P N<Q×P
We can control N
168. DPPnet
168
Weight Sharing with Hashing Trick
Weights of Dynamic Parameter Layer are picked from Candidate weights by Hashing
Question Feature
Candidate Weights
fc-layer
0.11.2-0.70.3-0.2
0.1 0.1 -0.2 -0.7
1.2 -0.2 0.1 -0.7
-0.7 1.2 0.3 -0.2
0.3 0.3 0.1 1.2
DynamicParameterLayer
Hasing
[Chen et al., 2015]
250. Visual texture synthesis
250
Which one do you think is real?
Right one is real.
Goal of texture synthesis is to produce (arbitrarily many)
new samples from an example texture.
262. Reconstruction from feature map
262
𝑋 𝑎
Input a
𝐹𝑎
1 𝐹𝑎
2 𝐹𝑎
3
𝑋 𝑏
Input b
𝐹𝑏
1
𝐹𝑏
2
𝐹𝑏
3
number of filters
Let’s make this features similar!
By changing the input image!
266. How?
266
Style Image
Content Image
Mixed ImageNeural Art
Texture Synthesis Using
Convolutional Neural Networks
Understanding Deep Image
Representations by Inverting Them