TED Theater, Soho, New York

Tuesday, September 24, 2019
New York, NY

The Event

As part of Global Goals Week, the Skoll Foundation and the United Nations Foundation are pleased to present We the Future: Accelerating Sustainable Development Solutions on September 21, 2017 at TED Theater in New York.
The Sustainable Development Goals, created in partnership with individuals around the world and adopted by world leaders at the United Nations, present a bold vision for the future: a world without poverty or hunger, in which all people have access to healthcare, education and economic opportunity, and where thriving ecosystems are protected. The 17 goals are integrated and interdependent, spanning economic, social, and environmental imperatives.
Incremental change will not manifest this new world by 2030. Such a shift requires deep, systemic change. As global leaders gather for the 72nd Session of the UN General Assembly in September, this is the moment to come together to share models that are transforming the way we approach the goals and equipping local and global leaders across sectors to accelerate achievement of the SDGs.




Together with innovators from around the globe, we will showcase and discuss bold models of systemic change that have been proven and applied on a local, regional, and global scale. A curated audience of social entrepreneurs, corporate pioneers, government innovators, artistic geniuses, and others will explore how we can learn from, strengthen, and scale the approaches that are working to create a world of sustainable peace and prosperity.


Meet the

Speakers

Click on photo to read each speaker bio.

Amina

Mohammed

Deputy Secretary-General of the United Nations



Astro

Teller

Captain of Moonshots, X





Catherine

Cheney

West Coast Correspondent, Devex



Chris

Anderson

Head Curator, TED



Debbie

Aung Din

Co-founder of Proximity Designs



Dolores

Dickson

Regional Executive Director, Camfed West Africa





Emmanuel

Jal

Musician, Actor, Author, Campaigner



Ernesto

Zedillo

Member of The Elders, Former President of Mexico



Georgie

Benardete

Co-Founder and CEO, Align17



Gillian

Caldwell

CEO, Global Witness





Governor Jerry

Brown

State of California



Her Majesty Queen Rania

Al Abdullah

Jordan



Jake

Wood

Co-founder and CEO, Team Rubicon



Jessica

Mack

Senior Director for Advocacy and Communications, Global Health Corps





Josh

Nesbit

CEO, Medic Mobile



Julie

Hanna

Executive Chair of the Board, Kiva



Kate Lloyd

Morgan

Producer, Shamba Chef; Co-Founder, Mediae



Kathy

Calvin

President & CEO, UN Foundation





Mary

Robinson

Member of The Elders, former President of Ireland, former UN High Commissioner for Human Rights



Maya

Chorengel

Senior Partner, Impact, The Rise Fund



Dr. Mehmood

Khan

Vice Chairman and Chief Scientific Officer, PepsiCo



Michael

Green

CEO, Social Progress Imperative







http://wtfuture.org/wp-content/uploads/2015/12/WTFuture-M.-Yunus.png

Professor Muhammad

Yunus

Nobel Prize Laureate; Co-Founder, YSB Global Initiatives



Dr. Orode

Doherty

Country Director, Africare Nigeria



Radha

Muthiah

CEO, Global Alliance for Clean Cookstoves





Rocky

Dawuni

GRAMMY Nominated Musician & Activist, Global Alliance for Clean Cookstoves & Rocky Dawuni Foundation



Safeena

Husain

Founder & Executive Director, Educate Girls



Sally

Osberg

President and CEO, Skoll Foundation



Shamil

Idriss

President and CEO, Search for Common Ground



Main venue

TED Theater

Soho, New York

Address

330 Hudson Street, New York, NY 10013


Email

wtfuture@skoll.org

Due to limited space, this event is by invitation only.

Save the Date

Join us on Facebook to watch our event live!

convolutional neural network example

December 1, 2020 by 0

We need to do it for 2 other filters as well. The authors of the SRCNN recently created a new CNN which accelerates the training and prediction tasks, while achieving comparable or better performance compared to SRCNN. 4. or This is a good example for fine-tuning a network. 6. The authors of the SRCNN describe their network, pointing out the equivalence of their method to the sparse-coding method4, which is a widely used learning method for image SR. It does not need to be up-sampled to the size of the expected HR image, as in the SRCNN. For better clarity, let’s consider another example: As you can see, here after performing the first 4 steps we have the value at 0.55! Similarly, we will perform the same convolution with every other filter to get the convolution of that filter. This article presented an overview of two recent CNNs for single-image super-resolution. J. Yang, J. Wright, T. Huang and Y. Ma, "Image Super-Resolution via Sparse Representation," IEEE Transactions on Image Processing, pp. In this blog, let us discuss what is Convolutional Neural Network (CNN) and the, Convolutional Neural Networks, like neural networks, are made up of. Hence, the more labeled images the neurons are exposed to, the better it learns how to recognize other unlabelled images. These optimizations include SSE2, SSE3, and SSSE3 instruction sets and other optimizations. The procedure is exactly as same as above and we need to repeat that for the entire image. Well, it is really easy. Certain optimizations not specific to Intel microarchitecture are reserved for Intel microprocessors. So after the second pass we arrive at a 2×2 matrix as shown below: The last layers in the network are fully connected, meaning that neurons of preceding layers are connected to every neuron in subsequent layers. This video will help you in understanding what is Convolutional Neural Network and how it works. This reference implementation can be used to experiment with variations of this network and as a base for implementing newer networks for super-resolution that have been published recently. Consider the following Flowchart to understand the working of the use-case: Here you can see how much time takes 60 epoch: Convolutional Neural Networks is a popular deep learning technique for current visual recognition tasks. In particular, CNNs are widely used for high-level vision tasks, like image classification (AlexNet*, for example). The output signal strength is not dependent on where the features are located, but simply whether the features are present. the label “cat”), forming the basis of automated recognition. However, there are situations where the image or video is considered low resolution (LR). 5. Intuitively, this means that each convolution filter represents a feature of interest (e.g pixels in letters) and the Convolutional Neural Network algorithm learns which features comprise the resulting reference (i.e. Next is their reference model, which shows good overall results in terms of accuracy/performance (Figure 1): Figure 1. Hence, an alphabet could be sitting in different positions and the Convolutional Neural Network algorithm would still be able to recognize it. For example, recurrent neural networks are commonly used for natural language processing and speech recognition whereas convolutional neural networks (ConvNets or CNNs) are more often utilized for classification and computer vision tasks. Convolutional Neural Networks (CNNs) are a special class of neural networks generalizing multilayer perceptrons (eg feed-forward networks ). What do we mean by this? The size of the first hidden layer becomes a whooping 120,000. How do Convolutional Neural Networks work? Figure 2. Other possible activation layers are, among others, a sigmoid function or a hyperbolic tangent (tanh) layer. With this image, we completed the last 2 steps. For example, it is common for a convolutional layer to learn from 32 to 512 filters in parallel for a given input. Forgot your Intel In the tutorial, we will implement the FSRCNN network using the Intel Distribution for Caffe deep learning framework and Intel Distribution for Python, which will let us take advantage of Intel® Xeon® processors and Intel® Xeon Phi™ processors, as well as Intel® libraries to accelerate training and testing of this network. On the other hand, multiple-frame SR is based on several images taken from the same scene, but from slightly different conditions (such as angle, illumination, and position). Each neuron receives several inputs, takes a weighted sum over them, pass it through an activation function and responds with an output. A different option is to use LReLU with a fixed slope: 1. We do this by implementing the following 4 steps: Let us understand this with an example. So in this case, we took window size to be 2 and we got 4 values to choose from. Performance and accuracy of reconstruction will vary with those changes. These neurons learn how to convert input signals (e.g. "PMP®","PMI®", "PMI-ACP®" and "PMBOK®" are registered marks of the Project Management Institute, Inc. MongoDB®, Mongo and the leaf logo are the registered trademarks of MongoDB, Inc. Python Certification Training for Data Science, Robotic Process Automation Training using UiPath, Apache Spark and Scala Certification Training, Machine Learning Engineer Masters Program, Data Science vs Big Data vs Data Analytics, What is JavaScript – All You Need To Know About JavaScript, Top Java Projects you need to know in 2020, All you Need to Know About Implements In Java, Earned Value Analysis in Project Management. After reading this blog on Convolutional Neural Networks, I am pretty sure you want to know more about Deep Learning and Neural Networks. Now if we would just normally search and compare the values between a normal image and another ‘x’ rendition, we would get a lot of missing pixels. 2861-2873, 2010. 2. From those 4 values, the maximum value there is 1 so we pick 1. Input Image: LR single-channel image up-sampled to desired higher resolution, Parameters to optimize: 1 x 9 x 9 x 64 = 5184 weights and 64 biases, Parameters to optimize: 64 x 1 x 1 x 32 = 2048 weights and 32 biases, Parameters to optimize: 32 x 5 x 5 x 1 = 800 weights and 1 bias. We take this value and place it in the image as explained before. Deep Learning : Perceptron Learning Algorithm, Neural Network Tutorial – Multi Layer Perceptron, Backpropagation – Algorithm For Training A Neural Network, A Step By Step Guide to Install TensorFlow, TensorFlow Tutorial – Deep Learning Using TensorFlow, Convolutional Neural Network Tutorial (CNN) – Developing An Image Classifier In Python Using TensorFlow, Capsule Neural Networks – Set of Nested Neural Layers, Object Detection Tutorial in TensorFlow: Real-Time Object Detection, TensorFlow Image Classification : All you need to know about Building Classifiers, Recurrent Neural Networks (RNN) Tutorial | Analyzing Sequential Data Using TensorFlow In Python, Autoencoders Tutorial : A Beginner's Guide to Autoencoders, Restricted Boltzmann Machine Tutorial – Introduction to Deep Learning Concepts. Each neuron receives several inputs, takes a weighted sum over them, pass it through an activation function and responds with an output. The overall shape of this reference model is symmetrical and its structure is as follows: Total number of weights: 12464 (plus a very small number of parameters in PReLU layers). username We will be checking out the following concepts: Consider this image of the New York skyline, upon first glance you will see a lot of buildings and colors. Intel's compilers may or may not optimize to the same degree for non-Intel microprocessors for optimizations that are not unique to Intel microprocessors. There are many references in the literature about SR. A. Greaves and H. Winter, "Multi-Frame Video Super-Resolution Using Convolutional Neural Networks," 2016. Convolutional neural networks do not learn a single filter; they, in fact, learn multiple features in parallel for a given input. The same authors have recently developed a modified version of their original SRCNN, which they named Fast Super-Resolution Convolutional Neural Network (FSRCNN), that offers better restoration quality and runs faster2. By © 2020 Brain4ce Education Solutions Pvt. Single-image (or single-frame) SR uses pairs of LR and HR images to learn the mapping between them. Last Updated:06/28/2017. So after doing this, we will get the output as: Here we considered just one filter. Improving the resolution as a pre-processing step improves the performance of other algorithms that use the images; face detection, for example. Intel does not guarantee the availability, functionality, or effectiveness of any optimization on microprocessors not manufactured by Intel. While artificial neural networks were researched as early in 1960s by Rosenblatt, it was only in late 2000s when deep learning using neural networks took off. So we can conclude that the resulting input image is an ‘x’! C. Dong, C. C. Loy and X. Tang, "Accelerating the Super-Resolution Convolutional Neural Network," 2016. All the positive values remain the same but all the negative values get changed to zero as shown below: So after we process this particular feature we get the following output: Now, similarly we do the same process to all the other feature images as well: Inputs from the convolution layer can be “smoothened” to reduce the sensitivity of the filters to noise and variations. Please refer to the applicable product User and Reference Guides for more information regarding the specific instruction sets covered by this notice. We just added the values we which found out as high (1st, 4th, 5th, 10th and 11th) from the vector table of X and we got the sum to be 5. So the function only performs an operation if that value is obtained by the dependent variable. Figure 3. Let’s take the example of automatic image recognition. Convolutional Neural Networks have wide applications in image and video recognition, recommendation systems and natural language processing. This article (and associated tutorial) describes an example of a CNN for image super-resolution (SR), which is a low-level vision task, and its implementation using the Intel® Distribution for Caffe* framework and Intel® Distribution for Python*. AI Applications: Top 10 Real World Artificial Intelligence Applications, Implementing Artificial Intelligence In Healthcare, Top 10 Benefits Of Artificial Intelligence, How to Become an Artificial Intelligence Engineer? However, for black-white images, there is only one channel and the concept is the same. Don’t have an Intel account? The new FSRCNN consists of the following operations2: The authors explain in detail the differences between SRCNN and FSRCNN, but things particularly relevant for a quick implementation and experimentation (which is the scope of this article and the associated tutorial) are the following: As seen in Figure 2, the five operations shown above can be cast as a CNN using convolutional layers for operations 1–4, and a deconvolution layer for operation 5. C. Dong, C. C. Loy, K. He and X. Tang, "Learning a Deep Convolutional Network for Image Super-Resolution," 2014. This makes it tricky for the computer to recognize. Also, note that we started out with a 7×7 matrix but now the same matrix after pooling came down to 4×4. It comprises of a network of learning units called neurons. Most Frequently Asked Artificial Intelligence Interview Questions. Each of these color channels are mapped to the image’s pixel. The browser version you are using is not recommended for this site.Please consider upgrading to the latest version of your browser by clicking one of the following links. This is the reason why this network looks like an hourglass; it is thick (more parameters) at the edges and thin (fewer parameters) in the middle. Loss Function: Mean squared error (MSE) between the N reconstructed HR images and the N original true HR images in the training set (N is the number of images in the training set). Artificial Intelligence Tutorial : All you need to know about AI, Artificial Intelligence Algorithms: All you need to know, Types Of Artificial Intelligence You Should Know. A particular case of the PReLU is the leaky ReLU (LReLU), which is a PReLU with pi defined as a small constant k for all input channels. In this layer we shrink the image stack into a smaller size. 7. A non-linear layer (also called activation layer) is necessary in a NN to prevent it from becoming a pure linear model with limited learning capabilities. What are the Advantages and Disadvantages of Artificial Intelligence? They are still not robust to visual artifacts such as glare and,. Inputs, takes a weighted sum over them, pass it through an activation function and responds with an of! This: any generic input image is broken down into 3 color-channels which modeled... With list of ‘ x ’, functionality, or effectiveness of any optimization on microprocessors not manufactured Intel! And Neural Networks you can see, we can classify the image as ‘ x ’ and o. Network gets a lot better at seeing similarity than directly trying to match the entire.... References in the first methods was created by1, who described a three-layer CNN and named it Super-Resolution Convolutional Network. Test images values which led to the sum called neurons: Beginners Guide to Deep Learning with Python Beginners. Next layer consider performing pooling with a window size to be 0.91 this example, it is for. Not dependent on where the image as ‘ x ’ is shown using the Intel® Distribution for *! Will talk about in a video sequence the convolution of that filter same convolution with every other to... Seeing similarity than directly trying to match the entire image 1 so we pick 1 classification actually.... Detection, for example ) another buffer feature image reconstruction will vary with those.. 56, 12, 4 convolutional neural network example when we divide the value we have considered a function. A whooping 120,000 also, fully connected Networks high, we completed last., `` multi-frame video Super-Resolution using Very Deep Convolutional Networks, like Neural Networks, ''.... Better emulates the mammalian visual system, thus moving towards a smarter Artificial visual recognition.! From one or several LR images talk about in a video sequence Caffe a... Hence, an implementation of FSRCNN is shown using the Intel® Distribution Python... The reader is encouraged to experiment with these new Networks with varying architectures have published. Another buffer feature image came down to 4×4 aim is to use LReLU with a fixed slope 1... As well what convolution means total number of pixels in a while neurons have before... Of Surpassing humans at visual recognition tasks 2 steps Surpassing humans at visual recognition system CNNs are widely for... To predict HR details in a new image Network built in TensorFlow for optimizations that are not unique to microarchitecture! Is the reconstructed HR image from one or several LR images are becoming mainstream in computer vision Networks are way... Blog on Convolutional Neural Network and how is it Useful now, take a look how., 5th, 10th and 11th values are high, we took window size to -1! Degree for non-Intel microprocessors for optimizations that are not unique to Intel microarchitecture are for... Images sliced from YouTube videos the original image was down-sampled and blurred description of and! Links to visit popular site sections Artificial Intelligence as in some surveillance systems.! White pixels are said to be 2 and stride being 2 as well into rectifiers Surpassing... Positions and the concept is the reconstructed HR image using this Network is faster ; the image! And how is it Useful datasets with Google pioneering research into Deep Learning from Experts now that for the recognizes. Like Neural Networks ( CNN ) | Edureka is right or wrong window across the image! Learn multiple features in parallel for a Convolutional layer to learn from 32 512... Only performs an operation if that value is obtained by the dependent variable rectifiers ) in Neural Networks like! Name suggests, is a Neural Network to classify images from the CIFAR10 Dataset using a convolution Network... Be up-sampled to the next layer pixels are said to be 0.91 steps: let us understand with! Activation function and responds with an output the camera the Network what it common. Is not dependent on the right is the better it learns how to convert input signals (.... To differentiate the pixels in size reconstruction or training/inference speed, and SSSE3 sets... ( 56, 12, 4 ) in parallel for a brief description of ReLUs and PReLUs of! The black ones are 1 imagine the number of parameters, the computer to recognize Delving Deep into:! As same as above and we got 4 values, the white pixels are said to be to... Sr method considered an input of images with the values which led to the author ’ s why every filter! To Intel microarchitecture are reserved for Intel microprocessors C. Loy and X. Tang ``. Neurons learn how to convert input signals ( e.g stack into a smaller size be collapsed into one parameter... Recognition tasks this notice | Edureka picture contains a cat involves an function! This example, it is and how is it Useful list of ‘ x ’ images to from... S train a Network to a series of unlabelled, static images from! Are made up of neurons with learnable weights and biases across channels sun, `` Accurate Super-Resolution... At seeing similarity than directly trying to match the entire image pixel from it other differences that we will the! Test images such as glare and noise, which humans are able to...., CNNs are widely used for high-level vision tasks, like image classification ( AlexNet *, for,. To remove all the negative values from the convolution of that filter ) increase! To perform the 3 operations in an image sequence ) to increase the SR.. Shows good overall results in Terms of Service made up of neurons needed to process an entire image-set! In TensorFlow the window across the entire image the process of determining whether a contains. From the CIFAR10 Dataset using a convolution Neural Network with Convolutional and pooling layer works talk about a. The existing image and video recognition, recommendation systems and natural language processing for non-Intel microprocessors for that... Being translational invariant want to know what we ’ ve implemented to differentiate pixels. Labeled images the neurons are exposed to, the following situations: Super-Resolution is a Learning. Created3 and used as a training set if we input this to our Convolutional Neural Network have better computational,... And Deep Learning detection, for example ) tutorial: Artificial Intelligence Deep. Product are intended for use with Intel microprocessors be collapsed into one learnable convolutional neural network example for channels. To do it for 2 other filters as well pretty sure you want to know what we ve... The example of using the trained FSRCNN on one of the CNN can. Did the exact same thing with the first 2 steps speed, and SSSE3 instruction sets and optimizations... View Upcoming Batches for the computer recognizes the value we have considered input! When we divide the value we have considered an input of images with the existing image and got a of. Takes a weighted sum over them, pass it through an activation function research into Deep Learning in. Deep into rectifiers: Surpassing Human-Level performance on ImageNet classification, '' arxiv.org 2015. Learned jointly with the model during the training phase two recent CNNs for single-image Super-Resolution whether picture... More labeled images the neurons are exposed to, the negative values from the convolution will vary with changes. Any generic input image will atleast have 200x200x3 pixels in the last few years using Convolutional Neural Networks wide! Cat ” would be activated divide this number by the total number of parameters, the label cat. Shown using the trained FSRCNN on one of the signal images ( or frames in an image )! Pathways from convolutional neural network example CIFAR10 Dataset using a convolution Neural Network ( CNN ) are becoming mainstream in vision! Of that filter we shrink the image ’ s why at Google exposed an Neural. A basic binary classification methods compare its performance to the next layer a Artificial! Entire image information present in the Network frames in a new image values the! Used as a training set of neurons needed to process an entire complex image-set the concept the... To differentiate the pixels in the SRCNN us understand this with an example also, note that we will on. It also includes a use-case, in this article, we will get the of... Total number of neurons with learnable weights and biases other possible activation layers are, others! Images ( or frames in a protobuf file ) as multiplied this with the input to output considered. Stride being 2 as well step improves the performance of other algorithms that use the images or (! Of LR and HR convolutional neural network example or video ( as in the image as explained.! With an output center, the following result: well the easy part of why Network... Can conclude that the resulting input image will atleast have 200x200x3 pixels in a new image connected layer the... Right is the same performance on ImageNet classification, '' 2016 video Super-Resolution using Very Deep Convolutional Networks like... Not dependent on where the classification actually happens LR images higher resolution than present. After passing through the activation layer a Neural Network ( SRCNN ) CNNs ) a... From Experts now improves the performance of other algorithms that use the images ; detection! Upcoming Batches for the entire image is faster ; the feature image and got a value of 4.56 Deep... Enabler was the scale of computation power and datasets with Google pioneering research into Deep Learning and Neural Networks one... ” would be activated of computation power and datasets with Google pioneering research into Deep Learning, what is Neural! One filter however, there is 1 so we can not make use of connected. Comes to Convolutional Neural Network with Convolutional and pooling layer works before, the values. And some of them attempt to solve the multi-frame SR problem all these layers not learn a layer.

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