Vector today announced it has expanded its partnership with Israeli technology company mPrest, to continue to develop and apply a machine learning and artificial. In linear algebra, a column vector or column matrix is an m 1 matrix, that is, a matrix consisting of a single column of m elements,. Similarly, a row. A lot of free vector art and graphics ideal for your designs. You can use them on your web, for prints or to design a unique Tshirt. Just download and enjoy. Table of Contents Intro to Linear classification Linear score function Interpreting a linear classifier Loss function. Multiclass SVM Softmax classifier. CS2. 31n Convolutional Neural Networks for Visual Recognition. Table of Contents In the last section we introduced the problem of Image Classification, which is the task of assigning a single label to an image from a fixed set of categories. This is the big list of Vector Elements which are great for infographics designing. Infographics are graphic visualization and representations of data and. Minnesota Map silhouette vector. Vector Us Map Dots' title='Vector Us Map Dots' />Morever, we described the k Nearest Neighbor k. NN classifier which labels images by comparing them to annotated images from the training set. As we saw, k. NN has a number of disadvantages The classifier must remember all of the training data and store it for future comparisons with the test data. This is space inefficient because datasets may easily be gigabytes in size. Classifying a test image is expensive since it requires a comparison to all training images. Overview. We are now going to develop a more powerful approach to image classification that we will eventually naturally extend to entire Neural Networks and Convolutional Neural Networks. The approach will have two major components a score function that maps the raw data to class scores, and a loss function that quantifies the agreement between the predicted scores and the ground truth labels. We will then cast this as an optimization problem in which we will minimize the loss function with respect to the parameters of the score function. Parameterized mapping from images to label scores. The first component of this approach is to define the score function that maps the pixel values of an image to confidence scores for each class. We will develop the approach with a concrete example. As before, lets assume a training dataset of images xi in RD, each associated with a label yi. Here i 1 dots N and yi in 1 dots K. That is, we have N examples each with a dimensionality D and K distinct categories. For example, in CIFAR 1. N 5. 0,0. 00 images, each with D 3. K 1. 0, since there are 1. We will now define the score function f RD mapsto RK that maps the raw image pixels to class scores. Linear classifier. In this module we will start out with arguably the simplest possible function, a linear mapping In the above equation, we are assuming that the image xi has all of its pixels flattened out to a single column vector of shape D x 1. The matrix W of size K x D, and the vector b of size K x 1 are the parameters of the function. In CIFAR 1. 0, xi contains all pixels in the i th image flattened into a single 3. W is 1. 0 x 3. 07. The parameters in W are often called the weights, and b is called the bias vector because it influences the output scores, but without interacting with the actual data xi. However, you will often hear people use the terms weights and parameters interchangeably. There are a few things to note First, note that the single matrix multiplication W xi is effectively evaluating 1. W. Notice also that we think of the input data xi, yi as given and fixed, but we have control over the setting of the parameters W,b. Our goal will be to set these in such way that the computed scores match the ground truth labels across the whole training set. We will go into much more detail about how this is done, but intuitively we wish that the correct class has a score that is higher than the scores of incorrect classes. An advantage of this approach is that the training data is used to learn the parameters W,b, but once the learning is complete we can discard the entire training set and only keep the learned parameters. That is because a new test image can be simply forwarded through the function and classified based on the computed scores. Lastly, note that to classifying the test image involves a single matrix multiplication and addition, which is significantly faster than comparing a test image to all training images. Foreshadowing Convolutional Neural Networks will map image pixels to scores exactly as shown above, but the mapping f will be more complex and will contain more parameters. Interpreting a linear classifier. Notice that a linear classifier computes the score of a class as a weighted sum of all of its pixel values across all 3 of its color channels. Depending on precisely what values we set for these weights, the function has the capacity to like or dislike depending on the sign of each weight certain colors at certain positions in the image. For instance, you can imagine that the ship class might be more likely if there is a lot of blue on the sides of an image which could likely correspond to water. You might expect that the ship classifier would then have a lot of positive weights across its blue channel weights presence of blue increases score of ship, and negative weights in the redgreen channels presence of redgreen decreases the score of ship. An example of mapping an image to class scores. For the sake of visualization, we assume the image only has 4 pixels 4 monochrome pixels, we are not considering color channels in this example for brevity, and that we have 3 classes red cat, green dog, blue ship class. Clarification in particular, the colors here simply indicate 3 classes and are not related to the RGB channels. We stretch the image pixels into a column and perform matrix multiplication to get the scores for each class. Note that this particular set of weights W is not good at all the weights assign our cat image a very low cat score. In particular, this set of weights seems convinced that its looking at a dog. Analogy of images as high dimensional points. Since the images are stretched into high dimensional column vectors, we can interpret each image as a single point in this space e. CIFAR 1. 0 is a point in 3. Analogously, the entire dataset is a labeled set of points. Since we defined the score of each class as a weighted sum of all image pixels, each class score is a linear function over this space. We cannot visualize 3. Cartoon representation of the image space, where each image is a single point, and three classifiers are visualized. Using the example of the car classifier in red, the red line shows all points in the space that get a score of zero for the car class. The red arrow shows the direction of increase, so all points to the right of the red line have positive and linearly increasing scores, and all points to the left have a negative and linearly decreasing scores. As we saw above, every row of W is a classifier for one of the classes. The geometric interpretation of these numbers is that as we change one of the rows of W, the corresponding line in the pixel space will rotate in different directions. The biases b, on the other hand, allow our classifiers to translate the lines. In particular, note that without the bias terms, plugging in xi 0 would always give score of zero regardless of the weights, so all lines would be forced to cross the origin. Interpretation of linear classifiers as template matching. Another interpretation for the weights W is that each row of W corresponds to a template or sometimes also called a prototype for one of the classes. The score of each class for an image is then obtained by comparing each template with the image using an inner product or dot product one by one to find the one that fits best. With this terminology, the linear classifier is doing template matching, where the templates are learned. Vector launches artificial intelligence system to manage Aucklands electricity network. Vector today announced it has expanded its partnership with Israeli technology company m. Prest, to continue to develop and apply a machine learning and artificial intelligence system that will better manage Aucklands changing energy demands. The m. DERMS programme, developed by Vector Ltd. Prest engineers, uses the latest technology to better monitor, analyse, and control Aucklands energy network, which connects traditional infrastructure like electricity lines and substations with new technology like solar and battery energy solutions, or DERs Distributed Energy Resources, to power more than half a million 5. With customer energy needs and expectations rapidly changing, m. DERMS is an overlaying system of systems that can integrate, oversee, manage, and make use of these DERs and their controlling systems on Vectors electricity network. With capability for the system to be applied to industries outside of the utilities sector, Vector has also entered into an investment and reselling agreement with m. Prest to assist the company with expanding its reach throughout Australasia. Simon Mackenzie, Chief Executive Officer of Vector Ltd, says the company is excited to expand its relationship with m. Prest.  With the transformation of the energy sector in mind, weve already been leading the development of new customer solutions using our existing expertise in running energy systems combined with advanced technology and international partners. Natan Barak, Chief Executive Officer of m. Prest, says Our partnership with Vector is what we strive for a happy customer who gains confidence in our software and comes back not only wanting to invest in it, but also to help replicate its success for other customers. Its common for our customers to expand their relationship with us after experiencing the benefits of our products. Vectors interest in transitioning from customer to investor and business partner is a powerful vote of confidence in us an established player in the utility and energy space is not only willing to implement m. Prest into production, but is willing to stand behind our product as a distributor as well. What is m. DERMS m. DERMS is a system of systems which connects the dots into a unified smart grid. Sometimes referred to as the Internet of Energy, m. DERMS gathers and analyses Distributed Energy Resources DERs like solar panels, energy batteries, electric vehicles EVs, etc, and integrates them onto one platform that can be managed by Vector to optimise this complex energy system for the end consumers. The smart grid of the future will be less like an electron corridor, and more like a vibrant energy marketplace. DERMS will enable this marketplace to exist, allowing our communities and business access to different sources of energy when and where they need it, and at the cheapest price possible. Text Scanner Program. The software uses integration, artificial intelligence and analytics to manage electricity demand and network data across Auckland, as well as enabling monitoring and control capabilities as the network grows. DERMS is also able to adapt for integration with future DERs and systems in the energy sector. Simon Mackenzie, Chief Executive Officer of Vector Ltd, says This internet of energy platform capability is world leading and is seen globally as the next big advancement in energy system evolution. It means that from Vectors control room, with one operating system, our engineers can better predict and manage outages by optimising DERs to enable a streamlined, efficient delivery of energy to and from the grid. Thats never been a possibility, up until now. This helps democratise energy, enabling customers to easily access low cost energy and control different network inputs to optimise their energy use and cost. StatisticsIn 2. Aucklanders used about 8,5. Gigawatt hours of energy. Vectors energy network currently, there are     Almost 3,5. Almost 7. 00 battery connections    3. Auckland with more to comey 2. Vector is planning for about 4. DERs solar panels, household batteries, electric vehicles, etc to become connected to the grid.