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Hough Business Powerpoint Template

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Transcript: By: Mikayla Robinson "Kay's Kakes" is a boisterous bakery full of life and color. We enjoy satisfying everyone’s sweet tooth with a variety of treats. Product Market _____________________ We are seeking $50 thousand dollars to start the production of our grocery store line by using half of the profit to pay commission to grocery stores that agree to start selling our products in stores. Then the last $25 thousand to heighten our production values to have extra product to ship out. We expect to provide an exit within 2 years by a dividend of excess profits. “Kay’s Kakes” makes a variety of sweets from full sized birthday cakes to mini rainbow macaroons. The company produces the following products: • Cakes • Cookies • Cupcakes • Pies • Bread • Pastries Presently, our “Rainbow Cheesecake” is in the maturity stage. It competes primarily on its appealing color scheme and its unique taste. Our future plans include developing a cheesecake for the freezer section for customers to purchase from their local stores and enjoy at home any time within the next year. Management We have competitive advantage because of our low cost producer status. 50lb bags of flour only cost about 45 dollars, which can make about 3 dozen cakes or 6 dozen pastries. Customers Product Pricing The greatest risks associated with our business today are fluctuating food prices and slow days of business. We feel like we can overcome these risks because of our outstanding training in our customer service skills. Our biggest recognized opportunities include serving our various customers including our celebrity guest. _____________________________________ "Kay's Kakes" identifies our business market as a manufacturer. Our market is growing: Our total market size was approximately $250 thousand over the last 4 months and is expected to grow by 20% in the next 5 years. Competitors Business Risks _____________________ The vision of "Kay's Kakes" is to be the #1 selling bakery in all the east coast. After that we plan on expanding some properties to the mid west and the west coast so everyone in the US can enjoy our treats. Kay's Kakes Overall Goal The management team is led by Chief Executive Officer Mikayla Robinson who founded “Kay’s Kakes” in the year 2015. She previously worked in Starbucks and in her Church’s One Family Café. ________________________________ _________________________________________ We appeal to everyone who wants to liven up a party, show their appreciation, or just craves a good cupcake. With our signature style of colorful flare we draw in people of every age. Find more information about us on our web page at file:///media/removable/TRAILMIXTAP/Mikayla's%20Stuff/Headings_Mikayla%20Robinson/Business%20Webpage/Homepage.html ___________________________ Mission Statement Capital Requirements We compete directly with Carlo’s Bakery in New Jersey and Georgetown Cupcake in our Nation’s Capital and alternatives to our product include smoothies, frozen yogurt and fruit popsicles to satisfy a customer’s sweet tooth while remaining healthy.


Transcript: HOUGH-CNN >INTRODUCTION Convolutional neural networks (CNN) have ability to deal with complex machine vision problems.we investigate the applicability of convolutional neural networks to medical image analysis. In MRI, the segmentation of basal ganglia is a relevant task for diagnosis, treatment and clinical research Accurate localisation and outlining of these nuclei can be challenging, even when performed manually, due to their weak contrast in MRI data. fully manual labelling of individual MRIs into multiple regions in 3D is extremely timeconsuming and therefore prohibitive > Problem Statement A crucial step towards computer assisted diagnosis of many diseases like PD is midbrain segmentation. >Robust Solution In this work we evaluate the performance of our approach using an ultrasound dataset of manually annotated TCUS volumes depicting the midbrain, and an MRI dataset, depicting 26 regions including basal ganglia, annotated in a computer-assisted manner > Related Works In this section we give an overview of existing approaches that employ CNNs to solve problems from both computer vision and medical imaging domain. Many works applying deep learning to medical problems relayed only on a few dozen of training images. >Notable Works Groups have applied 3D convolution successfully for Alzheimer’s disease detection from whole-MRI (Payan and Montana, 2015) or regression of affinity graphs from 3D convolution. A different approach that was applied to full-brain segmentation from MRI in de Brébisson and Montana (2015) combined small 3D patches with larger 2.5D ones that include more context. In Milletari et al. (2016) a fully convolutional model (FCNN) making use of both short and long skip connections and residual learning was employed to perform prostate segmentation in MRI. 4 Steps >Method 1. Convolutional neural networks 2. Voxel-wise classification 3. Hough voting with CNN 4. Efficient patch-wise evaluation through CNN Step 1 1. Convolutional neural networks CNNs perform machine learning tasks without requiring any handcrafted feature to be engineered and supplied by the user. That is, discovering optimal features describing the data at hand is part of the learning process. we made use of parametric rectified linear unitsas our activation functions. 2. Voxel-wise classification Step 2 The resulting trained networks are capable of performing voxel-wise classification, also called semantic segmentation, of volumes by interpreting them in a patchwise fashion. (semantic segmentation) A set T = { p 1 , . . . , p N } of square (or cubic) patches having size p pixels is extracted from annotated volumes V with j = { 1 . . . J } along with the corresponding ground truth labels Y ={ y 1 , . . . , y N } . 3. Hough voting with CNN During training, we make use of the dataset of training volumes Vj with j {1 . . . J}, and respective binary segmentation volumes Sj with j {1 . . . J} Step 3 The vote vi is a displacement vector joining the voxel xi, where the ith patch was collected from, and the position anatomy centroid cj in the training volume Vj: Once the neighbours are identified, their votes vi 1...K and associated segmentation patches si 1...K from the database, are employed to respectively perform localisation and segmentation. The votes are weighted by the reciprocal of the Euclidean distance computed during K-nn search. Efficient patch-wise evaluation through CNN When dealing with images or volumes, patches are extracted in a sliding-window fashion and processed through a CNN. This approach is inefficient due to the high amount of redundant computations that need to be performed for neighbouring patches To solve this issue we modify the network structure as pro- posed by Sermanet et al. (2013b ) in order to be able to process the whole volume at once, yet retrieving the same results that we would obtain if the data would be processed patch-wise. Step 4 > Experiments & Results 1.Datasets and ground-truth definition. 2.CNN parameters. 3.Experiments and results in UltraSound. 4.Experiments and results in MRI. 5.Comparison with fully convolutional models > 1.Datasets and ground-truth definition. Our MRI dataset is composed of MRI volumes of 55 subjects,which were acquired using 3D gradient-echo imaging (magnitude and phase) with an isotropic spatial resolution of 1x1x1 mm. In order to test our approach and to benchmark the capabilities of the proposed CNNs when they are trained with a variable amount of data, we establish, for each dimensionality (2D, 2.5D and 3D) two differently sized training sets in US and three in MRI respectively. A validation set containing 5K patches has been established for US using images of subjects that have not been used for training or testing and employed to assess the generalisation capabilities of the models > 2.CNN parameters. We analyse six different network architectures, presented in Table 1, by training each of them for 15 epochs using Stochastic Gradient Descent (SGD) with >

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Transcript: Nobody knows babies like we do! Quality products . Good Customer service. Every Kid really loves this store.. BABYLOU ABOUT US About Us BabyLou was established in 2004. It has been more than a decade since we started, where we have ensured to take care of every need and want of every child and infant under one roof, true to the caption “NO BODY KNOWS BABIES LIKE WE DO”. Our benchmark is to provide 100% customer service and satisfaction and continue to deliver the same with a wide range of toys, garments and Baby Products. Play and Create We Are Best 01 02 03 Block games Building Blocks help Kids to use their brain. PLAY TO LEARN in Crusing Adventures Our Discoveries Enjoy a sunny vacation aboard a luxury yacht with the LEGO® Creator 3in1 31083 Cruising Adventures set. This ship has all the comforts you need, including a well-equipped cabin and a toilet. Sail away to a sunny bay and take the cool water scooter to the beach. Build a sandcastle, enjoy a picnic, go surfing or check out the cute sea creatures before you head back to the yacht for a spot of fishing. Escape into the mountains Disney Little Princes in Also available for your Babies..... Also... Out of The World… Our reponsibility BABYLOU…. Our Responsibility All children have the right to fun, creative and engaging play experiences. Play is essential because when children play, they learn. As a provider of play experiences, we must ensure that our behaviour and actions are responsible towards all children and towards our stakeholders, society and the environment. We are committed to continue earning the trust our stakeholders place in us, and we are always inspired by children to be the best we can be. Innovate for children We aim to inspire children through our unique playful learning experiences and to play an active role in making a global difference on product safety while being dedicated promoters of responsibility towards children.

Business Template

Transcript: PURCHASE / DAY NET PROFITS PRODUCERS Since when ? Industry LET'S TALK ABOUT UNIT AQUISITION COST I I PROJECTIONS Batnae municipium in Anthemusia conditum Macedonum manu priscorum ab Euphrate flumine brevi spatio disparatur, refertum mercatoribus opulentis, ubi annua sollemnitate prope I To make our presentations fun and engaging. PREZI The monthly fee for Prezi Pro is $20 per month (no contract). Lorem ipsum dolor Proinde die funestis interrogationibus Caesarem perferentes, cuius MARKET I Joe Smith Brad Aaron John Adams WHATS Anyone holding an PRO license can make any presentation 100% confidential. Only you and the people you designate can see your work. To stay 100% offline, you can create a presentation using Prezi's offline application also. I 158 NEW TOP GROSS MARGIN PROJECT SIZE MONTHLY SALES COST PER MONTH News Already launched ? Site / product is ready ? SECURITY ECONOMICS PREZI I LOGO You already have customers? Batnae municipium in Anthemusia conditum Macedonum manu priscorum ab Euphrate flumine brevi spatio disparatur, refertum mercatoribus opulentis, ubi annua sollemnitate prope 3$ STATE I This is our market size - the number of individuals we anticipate we can promote our products to. SALES Marketing Monthly Sales Update I KEY FIGURES LET'S TALK ABOUT PURCHASING PREZI I If your service is already running ... how many clients do you have? How much revenue have they generated the last 30 days? The last 12 months? Since the launch of the project? What is your customer / user acquisition cost so far? I 68 12% 13 Item n°3 Lorem ipsum dolor Item n°2 Lorem ipsum dolor Item n°1 Lorem ipsum dolor

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