The presentation is made on CNN's which is explained using the image classification problem, the presentation was prepared in perspective of understanding computer vision and its applications. I tried to explain the CNN in the most simple way possible as for my understanding. This presentation helps the beginners of CNN to have a brief idea about the architecture and different layers in the architecture of CNN with the example. Please do refer the references in the last slide for a better idea on working of CNN. In this presentation, I have also discussed the different types of CNN(not all) and the applications of Computer Vision.
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).
The presentation is made on CNN's which is explained using the image classification problem, the presentation was prepared in perspective of understanding computer vision and its applications. I tried to explain the CNN in the most simple way possible as for my understanding. This presentation helps the beginners of CNN to have a brief idea about the architecture and different layers in the architecture of CNN with the example. Please do refer the references in the last slide for a better idea on working of CNN. In this presentation, I have also discussed the different types of CNN(not all) and the applications of Computer Vision.
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).
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.
Convolutional Neural Networks : Popular Architecturesananth
In this presentation we look at some of the popular architectures, such as ResNet, that have been successfully used for a variety of applications. Starting from the AlexNet and VGG that showed that the deep learning architectures can deliver unprecedented accuracies for Image classification and localization tasks, we review other recent architectures such as ResNet, GoogleNet (Inception) and the more recent SENet that have won ImageNet competitions.
Naive Bayes is a kind of classifier which uses the Bayes Theorem. It predicts membership probabilities for each class such as the probability that given record or data point belongs to a particular class.
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.
Convolutional Neural Networks : Popular Architecturesananth
In this presentation we look at some of the popular architectures, such as ResNet, that have been successfully used for a variety of applications. Starting from the AlexNet and VGG that showed that the deep learning architectures can deliver unprecedented accuracies for Image classification and localization tasks, we review other recent architectures such as ResNet, GoogleNet (Inception) and the more recent SENet that have won ImageNet competitions.
Naive Bayes is a kind of classifier which uses the Bayes Theorem. It predicts membership probabilities for each class such as the probability that given record or data point belongs to a particular class.
https://telecombcn-dl.github.io/2018-dlai/
Deep learning technologies are at the core of the current revolution in artificial intelligence for multimedia data analysis. The convergence of large-scale annotated datasets and affordable GPU hardware has allowed the training of neural networks for data analysis tasks which were previously addressed with hand-crafted features. Architectures such as convolutional neural networks, recurrent neural networks or Q-nets for reinforcement learning have shaped a brand new scenario in signal processing. This course will cover the basic principles of deep learning from both an algorithmic and computational perspectives.
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.
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Student information management system project report ii.pdfKamal Acharya
Our project explains about the student management. This project mainly explains the various actions related to student details. This project shows some ease in adding, editing and deleting the student details. It also provides a less time consuming process for viewing, adding, editing and deleting the marks of the students.
Cosmetic shop management system project report.pdfKamal Acharya
Buying new cosmetic products is difficult. It can even be scary for those who have sensitive skin and are prone to skin trouble. The information needed to alleviate this problem is on the back of each product, but it's thought to interpret those ingredient lists unless you have a background in chemistry.
Instead of buying and hoping for the best, we can use data science to help us predict which products may be good fits for us. It includes various function programs to do the above mentioned tasks.
Data file handling has been effectively used in the program.
The automated cosmetic shop management system should deal with the automation of general workflow and administration process of the shop. The main processes of the system focus on customer's request where the system is able to search the most appropriate products and deliver it to the customers. It should help the employees to quickly identify the list of cosmetic product that have reached the minimum quantity and also keep a track of expired date for each cosmetic product. It should help the employees to find the rack number in which the product is placed.It is also Faster and more efficient way.
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.
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.
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.
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CFD Simulation of By-pass Flow in a HRSG module by R&R Consult.pptxR&R Consult
CFD analysis is incredibly effective at solving mysteries and improving the performance of complex systems!
Here's a great example: At a large natural gas-fired power plant, where they use waste heat to generate steam and energy, they were puzzled that their boiler wasn't producing as much steam as expected.
R&R and Tetra Engineering Group Inc. were asked to solve the issue with reduced steam production.
An inspection had shown that a significant amount of hot flue gas was bypassing the boiler tubes, where the heat was supposed to be transferred.
R&R Consult conducted a CFD analysis, which revealed that 6.3% of the flue gas was bypassing the boiler tubes without transferring heat. The analysis also showed that the flue gas was instead being directed along the sides of the boiler and between the modules that were supposed to capture the heat. This was the cause of the reduced performance.
Based on our results, Tetra Engineering installed covering plates to reduce the bypass flow. This improved the boiler's performance and increased electricity production.
It is always satisfying when we can help solve complex challenges like this. Do your systems also need a check-up or optimization? Give us a call!
Work done in cooperation with James Malloy and David Moelling from Tetra Engineering.
More examples of our work https://www.r-r-consult.dk/en/cases-en/
5. Convolution Layer
- Convolution (3-dim dot product) image and filter
- Stack filter in one layer (See blue and green output,
called channel)
6. Convolution Layer
- Local Connectivity
• Instead connect all pixels to neurons, connect
only local region of input (called receptive field)
• It can reduce many parameter
- Parameter sharing
• To reduce parameter, each channel have same
filter. (# of filter == # of channel)
7. Convolution Layer
- Example) 1st conv layer in AlexNet
• Input: [224, 224], filter: [11x11x3], 96, output: [55, 55]
- Each filter extract different features (i.e. horizontal
edge, vertical edge…)
8. Pooling Layer
- Downsample image to reduce parameter
- Usually use max pooling (take maximum value in
region)
9. ReLU, FC Layer
- ReLU
• Sort of activation function (e.g. sigmoid, tanh…)
- Fully-connected Layer
• Same as normal neural network
11. Training CNN
1. Calculate loss function with foward-prop
2. Optimize parameter w.r.t loss function with back-
prop
• Use gradient descent method (SGD)
• Gradient of weight can calculate with chain rule of partial derivate
19. AlexNet
- Other methods (but will not mention today)
• SGD + momentum (+ mini-batch)
• Multiple GPU
• Weight Decay
• Local Response Normalization
20. Problems of sigmoid
- Gradient vanishing
• when gradient pass sigmoid, it can vanish
because local gradient of sigmoid can be almost
zero.
- Output is not zero-centered
• cause bad performance
21. ReLU
- Converge of SGD is faster than sigmoid-like
- Computationally cheap
22. Data augmentation
- Randomly crop [256, 256] images to [224, 224]
- At test time, crop 5 images and average to predict
23. Dropout
- Similar to bagging (approximation of bagging)
- Act like regularizer (reduce overfit)
- Instead of using all neurons, “dropout” some neurons
randomly (usually 0.5 probability)
24. Dropout
• At test time, not “dropout” neurons, but use
weighted neurons (usually 0.5)
• Weight is expected value of each neurons
25. Architecture
- conv - pool - … - fc - softmax (similar to LeNet)
- Use large size filter (i.e. 11x11)
26. Architecture
- Weights must be initalized randomly
• If not, all gradients of neurons will be same
• Usually, use gaussian distribution, std = 0.01
- Use mini-batch SGD and momentum SGD to
update weight
28. VGGNet
- Use small size kernel (always 3x3)
• Can use multiple non-linearlity (e.g. ReLU)
• Less weights to train
- Hard data augmentation (more than AlexNet)
- Ensemble 7 model (ILSVRC submission 7.3%)
32. Inception module
- Use 1x1, 3x3 and 5x5 conv
simultaneously to capture
variety of structure
- Capture dense structure to
1x1, more spread out structure
to 3x3, 5x5
- Computational expensive
• Use 1x1 conv layer to
reduce dimension (explain
details in later in ResNet)
33. Auxiliary Classifiers
- Deep network raises concern about effectiveness
of graident in backprop
- Loss of auxiliary is added to total loss (weighted by
0.3), remove at test time
34. Average Pooling
- Proposed in Network in Network (also used in
GoogLeNet)
- Problems of fc layer
• Needs lots of parameter, easy to overfit
- Replace fc to average pooling
35. Average Pooling
- Make channel as same as # of class in last conv
- Calc average on each channel, and pass to softmax
- Reduce overfit
37. before ResNet..
- Have to know about
• PReLU
• Xavier Initalization
• Batch Normalization
38. PReLU
- Adaptive version of ReLU
- Train slope of function when x < 0
- Slightly more parameter (# of layer x # of channel)
39. Xavier Initalization
- If init with gaussian distribution, output of neurons
will be nearly zeros when network is deeep
- If increase std (1.0), output will saturate to -1 or 1
- Xavier init decide initial value by number of input
neurons
- Looks fine, but this init method assume linear
activation so can’t use in ReLU-like network
42. Batch Normalization
- Make output to be gaussian distribution, but
normalization cost a lot
• Calc mean, variance in each dimension (assume each dims are
uncorrelated)
• Calc mean, variance in mini-batch (not entire set)
- Normalize constrain non-linearlity and constrain
network by assume each dims are uncorrelated
• Linear transform output (factors are parameter)
43. Batch Normalization
- When test, calc mean, variance using entire set (use
moving average)
- BN act like regularizer (don’t need Dropout)
46. Problem of degradation
- More depth, more accurate but deep network can
vanish/explode gradient
• BN, Xavier Init, Dropout can handle (~30 layer)
- More deeper, degradation problem occur
• Not only overfit, but also increase training error
47. Deep Residual Learning
- Element-wise addition with F(x) and shortcut
connection, and pass through ReLU non-linearlity
- Dim of x, F(x) are unequal (changing of channel),
linear project x to match dim (done by 1x1 conv)
- Similar to LSTM
48. Deeper Bottleneck
- To reduce training time, modify as bottleneck design
(just for economical reason)
• (3x3x3)x64x64 + (3x3x3)x64x64=221184 (left)
• (1x1x3)x256x64 + (3x3x3)x64x64 + (1x1x3)x64x256=208896 (right)
• More width(channel) in right, but similar parameter
• Similar method also used in GoogLeNet
49. ResNet
- Data augmentation as AlexNet does
- Batch Normalization (no dropout)
- Xavier / 2 initalization
- Average pooling
- Structure follows VGGNet style
52. Conclusion
- Dropout, BN
- ReLU-like activation (e.g. PReLU, ELU..)
- Xavier initalization
- Average pooling
- Use pre-trained model :)
53. Reference
- Krizhevsky, Alex, Ilya Sutskever, and Geoffrey E. Hinton. "Imagenet classification with deep
convolutional neural networks." Advances in neural information processing systems. 2012.
- Simonyan, Karen, and Andrew Zisserman. "Very deep convolutional networks for large-scale image
recognition." arXiv preprint arXiv:1409.1556 (2014).
- Lin, Min, Qiang Chen, and Shuicheng Yan. "Network in network." arXiv preprint arXiv:1312.4400 (2013).
- He, Kaiming, et al. "Delving deep into rectifiers: Surpassing human-level performance on imagenet
classification." Proceedings of the IEEE International Conference on Computer Vision. 2015.
- He, Kaiming, et al. "Deep Residual Learning for Image Recognition." arXiv preprint arXiv:1512.03385
(2015).
- Szegedy, Christian, Sergey Ioffe, and Vincent Vanhoucke. "Inception-v4, Inception-ResNet and the
Impact of Residual Connections on Learning." arXiv preprint arXiv:1602.07261 (2016).
- Gu, Jiuxiang, et al. "Recent Advances in Convolutional Neural Networks." arXiv preprint arXiv:
1512.07108 (2015). (good for tutorial)
- Also Thanks to CS231n, I used some figures in CS231n lecture slides.
see http://cs231n.stanford.edu/index.html