Hinton cnn paper

    thank for the help this question, now..

    With images becoming the fastest growing content, image classification has become a major driving force for businesses to speed up processes. Rapid advances in computer vision and ongoing research has allowed enterprises to create solutions that enable automated image tagging and automatically add tags to images to allow users to search and filter more quickly. Enterprises that want to add more value to existing visual content or e-commerce businesses that deal with multiple product photos in a digital asset management DAM system or a web content management WCM environment used by an editorial staff on daily basis rely on image classification techniques to speed up business processes.

    The paper presented a new online database, a large-scale ontology of images that offers offers unparalleled opportunities to researchers in the computer vision community and serves as a catalyst for the AI boom. The annual ImageNet image recognition competition has improved the accuracy of classifying images and its winning researchers have donned on senior roles at Google, Baidu and Google-owned London-based DeepMind.

    Given the explosion of image data and the application of image classification research in Facebook tagging, land cover classification in agriculture and remote sensing in meterology, oceanography, geology, archaeology and other areas — AI-fuelled research has found a home in everyday applications. In this article, we list down top research papers dealing with convolutional neural networks and their resulting advances in object recognition, image captioning, semantic segmentation and human pose estimation.

    AlexNet In fact, marked the first year when a CNN was used to achieve a top 5 test error rate of GoogleNet Over the years, Google has been experimenting with neural networks to improve its image search ability and understand the content within Youtube videos.

    Google is leveraging these research advances and converting it into Google products such as in YouTube, image search and even self-driving cars. ZFNet This research paper authored by Matthew D Zeiler and Rob Fergus introduced a novel visualization technique that gave a peek into the functioning of intermediate feature layers and the operation of the classifier.

    This architecture was trained on 1. The paper proposed to outperform Krizhevsky on the ImageNet classification benchmark. This research paper, authored by two University of Maryland researchers Rama Chellappa, Swami Sankaranarayanan and GE Global researchers Arpit Jain and Ser Nam Lim proposed a simple learning algorithm that leveraged perturbations of intermediate layer activation to provide a stronger regularization while improving the robustness of deep network to adversarial data.

    The research dealt with the behaviour of CNNs as related to adversarial data and the intrigue it had generated in computer vision.

    Why Convolutions

    However, the effects of adversarial data on deeper networks had not been explored well. Residual Attention Network for Image Classification Richa Bhatia is a seasoned journalist with six-years experience in reportage and news coverage and has had stints at Times of India and The Indian Express. She is an avid reader, mum to a feisty two-year-old and loves writing about the next-gen technology that is shaping our world.

    Richa Bhatia Richa Bhatia is a seasoned journalist with six-years experience in reportage and news coverage and has had stints at Times of India and The Indian Express. Share This. Our Upcoming Events.I am going to be posting some loose notes on different biologically-inspired machine learning lectures. In this note I summarize a talk given in by Geoffrey Hinton where he discusses some shortcomings of convolutional neural networks CNNs.

    Convo nets have been remarkably successful. The current deep learning boom can be traced to a paper by Krizhevsky, Sutskever, and Hinton called ImageNet Classification with Deep Convolutional Networks which demonstrated for the first time how a deep CNN could vastly outperform other methods at image classification.

    Recently, Hinton expressed deep suspicion about backpropationsaying that he believes it is a very inefficient way of learning, in that it requires a lot of data. Pose information refers to 3D orientation relative to the viewer but also lighting and color. CNNs are known to have trouble when objects are rotated or when lighting conditions are changed. Convolutional networks use multiple layers of feature detectors. Each feature detector is local, so feature detectors are repeated across space.

    Pooling gives some translational invariance in much deeper layers, but only in a crude way. According to Hinton, the psychology of shape perception suggests that the human brain achieves translational invariance in a much better way.

    This leads to simultanagnosiaa rare neurological condition where patients can only perceive one object at a time. We know that edge detectors in the first layer of the visual cortex V1 do not have translational invariance — each detector only detects things in a small visual field. The same is true in CNNs.

    hinton cnn paper

    The difference between the brain and a CNN occurs in the higher levels. According to Hinton, CNNs do routing by pooling. Pooling was introduced to reduce redundancy of representation and reduce the number of parameters, recognizing that precise location is not important for object detection. Pooling does routing in a very crude way - for instance max pooling just picks the neuron with the highest activation, not the one that is most likely relevant to the task at hand.

    Another difference between CNNs and human vision is the human vision system appears to impose a rectangular coordinate frames on objects. Some simple examples found by the psychologist Irving Rock are as follows:. Very roughly speaking, the square and diamond look like very different shapes, because we represent them in rectangular coordinates. If they were in polar coordinates, they would differ by a single scalar angular phase factor and their numerical representations would be much similar.

    The fact the brain embeds things in a rectangular coordinate system means that linear translation is easy for the brain to handle but rotation is hard. Studies have found the mental rotation takes time proportionate to the amount of rotation required.

    CNNs cannot handle rotation at all - if they are trained on objects in one orientation, they will have trouble when the orientation is changed. In other words, CNNs could never tell a left shoe from a right shoe, even if they were trained on both. Taking the concept of a capsule further and speaking very hypothetically, Hinton proposes that capsules may be related to cortical minicolumns. Capsules may encode information such as orientation, scale, velocity, and color. Like neurons in the output layer of a CNN, a capsule outputs a probability of whether an entity is present, but additionally has pose metadata attached to it.

    This is very useful, because it can allow the brain to figure out if two objects, such as mouth and a nose, are subcomponents of an underlying object a face. Hinton suggests it is easy to determine non-coincidental poses in high dimensions. Hinton says that computer vision should be like inverse graphics. So, while a graphics engine multiplies a rotation matrix times a vector to get the appearance of an object in a particular pose relative to the viewer, a vision system should take appearance and back out the matrix that gives that pose.

    Toward the end of the lecture, Hinton shows a system that combines these concepts. Hintons code was written in Matlab and not optimized for speed, so future implementations, especially utilizing GPUs and other parallel hardware, could make them quite competitive with CNNs. Some simple examples found by the psychologist Irving Rock are as follows: Very roughly speaking, the square and diamond look like very different shapes, because we represent them in rectangular coordinates.Empirical studies on Capsule Network representation and improvements implemented with PyTorch.

    Another implementation of Hinton's capsule networks in tensorflow. A simple tensorflow implementation of CapsNet by Dr. Hintonbased on my understanding.

    hinton cnn paper

    This repository is built with an aim to simplify the concept, implement and understand it. The code implements Hinton's matrix capsule with em routing for Cifar dataset.

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    Updated Jan 12, Python. Updated Jan 22, Python. Updated Jan 28, Python. MXNet implementation of CapsNet. Updated Nov 29, Python.

    Updated Mar 16, Python. Updated Feb 19, Python.After a prolonged winter, artificial intelligence is experiencing a scorching summer mainly thanks to advances in deep learning and artificial neural networks. To be more precise, the renewed interest in deep learning is largely due to the success of convolutional neural networks CNNsa neural network structure that is especially good at dealing with visual data. But what if I told you that CNNs are fundamentally flawed?

    That was what Geoffrey Hinton, one of the pioneers of deep learningtalked about in his keynote speech at the AAAI conference, one of the main yearly AI conferences. As with all his speeches, Hinton went into a lot of technical details about what makes convnets inefficient—or different—compared to the human visual system. Following is some of the key points he raised. But first, as is our habit, some background on how we got here and why CNNs have become such a great deal for the AI community.

    Since the early days of artificial intelligence, scientists sought to create computers that could see the world like humans. The efforts have led to their own field of research collectively known as computer vision. Early work in computer vision involved the use of symbolic artificial intelligencesoftware in which every single rule must be specified by human programmers. The problem is, not every function of the human visual apparatus can be broken down in explicit computer program rules.

    The approach ended up having very limited success and use. A different approach was the use of machine learning. Contrary to symbolic AI, machine learning algorithms are given a general structure and unleashed to develop their own behavior by examining training examples. However, most early machine learning algorithms still required a lot of manual effort to engineers the parts that detect relevant features in images. Convolutional neural networks, on the other hand, are end-to-end AI models that develop their own feature-detection mechanisms.

    A well-trained CNN with multiple layers automatically recognizes features in a hierarchical way, starting with simple edges and corners down to complex objects such as faces, chairs, cars, dogs, etc.

    hinton cnn paper

    But because of their immense compute and data requirements, they fell by the wayside and gained very limited adoption. It took three decades and advances in computation hardware and data storage technology for CNNs to manifest their full potential. Today, thanks to the availability of large computation clusters, specialized hardware, and vast amounts of data, convnets have found many useful applications in image classification and object recognition.

    One of the key challenges of computer vision is to deal with the variance of data in the real world. Our visual system can recognize objects from different angles, against different backgrounds, and under different lighting conditions. Creating AI that can replicate the same object recognition capabilities has proven to be very difficult. This means that a well-trained convnet can identify an object regardless of where it appears in an image. One approach to solving this problem, according to Hinton, is to use 4D or 6D maps to train the AI and later perform object detection.

    For the moment, the best solution we have is to gather massive amounts of images that display each object in various positions. Then we train our CNNs on this huge dataset, hoping that it will see enough examples of the object to generalize and be able to detect the object with reliable accuracy in the real world.

    Datasets such as ImageNet, which contains more than 14 million annotated images, aim to achieve just that. In fact, ImageNet, which is currently the go-to benchmark for evaluating computer vision systems, has proven to be flawed. Despite its huge size, the dataset fails to capture all the possible angles and positions of objects.

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    It is mostly composed of images that have been taken under ideal lighting conditions and from known angles. This is acceptable for the human vision system, which can easily generalize its knowledge. In fact, after we see a certain object from a few angles, we can usually imagine what it would look like in new positions and under different visual conditions.

    In effect, the CNN will be trained on multiple copies of every image, each being slightly different. This will help the AI better generalize over variations of the same object.

    Data augmentation, to some degree, makes the AI model more robust. There have been efforts to solve this generalization problem by creating computer vision benchmarks and training datasets that better represent the messy reality of the real world.

    And those new situations will befuddle even the largest and most advanced AI system. From the points raised above, it is obvious that CNNs recognize objects in a way that is very different from humans. But these differences are not limited to weak generalization and the need for many more examples to learn an object.The original paper's primary result was that the depth of the model was essential for its high performance, which was computationally expensive, but made feasible due to the utilization of graphics processing units GPUs during training.

    Chellapilla et al. According to the AlexNet paper, [3] Ciresan's earlier net is "somewhat similar. Weng's method called max-pooling. AlexNet contained eight layers; the first five were convolutional layers, some of them followed by max-pooling layers, and the last three were fully connected layers. AlexNet is considered one of the most influential papers published in computer vision, having spurred many more papers published employing CNNs and GPUs to accelerate deep learning.

    Alex Krizhevsky born in Ukraineraised in Canada is a computer scientist most noted for his work on artificial neural networks and deep learning. From Wikipedia, the free encyclopedia. Retrieved 5 October Communications of the ACM. In Lorette, Guy ed. Gambardella; Jurgen Schmidhuber Retrieved 17 November Retrieved Retrieved 14 January Multi-column deep neural networks for image classification. LeCun, B. Boser, J. Denker, D.

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    Henderson, R. Howard, W. Hubbard, L. Proceedings of the IEEE. Retrieved October 7, Bibcode : SchpJ Biological Cybernetics. Retrieved 16 November GitHub is home to over 40 million developers working together to host and review code, manage projects, and build software together.

    If nothing happens, download GitHub Desktop and try again. If nothing happens, download Xcode and try again. If nothing happens, download the GitHub extension for Visual Studio and try again. In NovemberG. Hinton et al. This paper promises a breakthrough in the deep learning community.

    This new type of neural network CapsNet is based on the so-called "Capsules". CapsNet enables new applications, especially, it can overcome the main drawback of CNNs. CapsNet is not sensible to linear operations, i. Moreover, unlike CNNs, CapsNet can take into account orientations and spatial relationships between features. In second part, the project aims to go further with one potential application in finance: the time-series bi-labels classification problem.

    In this part, results of the paper are reproduced. Then, the reconstruction part of images is highlighted and the Capsnet capacity to identify over-lapped digits is also tested. The reconstruction of input image was a success. Capsnet demonstrated the capacity to identify overlapped digits. In finance, and especially in time-series problems, the time is an important component to take into account. Because of the Capsnet's capacity to consider spatial relationships between features.

    The project aimes to explore the application of Capsules for time-series classification problem. The goal of the algorithm is to predict, for a given stock, the sign of the next day return. The architecture of the network is modified because of the nature of the input and output and also to reduce the observed CapsNet tendency to overfit.

    The project introduces the usage of dropout in CapsNet, still in order to reduce overfitting. The experiment was run with auto-regressive entry. It is not taking into account the relations between the different stocks.

    Exploring this way should lead to better results. It should be interesting because of the CapsNet capacity to identify orientation and spatial relations. Skip to content. Dismiss Join GitHub today GitHub is home to over 40 million developers working together to host and review code, manage projects, and build software together.

    Sign up. No description, website, or topics provided. Jupyter Notebook Python. Jupyter Notebook Branch: master. Find file. Sign in Sign up.CNN As police across the US brace for continued emergency calls in the wake of the coronavirus outbreak, one Oregon police department is dealing with calls for an entirely different type of emergency: Residents are calling because they've run out of toilet paper.

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    You will survive without our assistance. Toilet paper is unavailable at many stores and supermarkets as people across the US stock up on household essentials due to fears over the coronavirus outbreak. Many sellers on Amazon are also out of stock. The psychology behind why toilet paper, of all things, is the latest coronavirus panic buy. The police offered up some humorous, friendly tips for those that are dealing with the shortage. Ancient Romans used a sea sponge on a stick, also soaked in salt water.

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    Hinton cnn paper

    Step 1 : overlay the filter to the input, perform element wise multiplication, and add the result. Step 2 : move the overlay right one position or according to the stride settingand do the same calculation above to get the next result.

    And so on. Stride governs how many cells the filter is moved in the input to calculate the next cell in the result. Notice the the dimension of the result has changed due to padding. See the following section on how to calculate output dimension. When the input has more than one channels e. To calculate one output cell, perform convolution on each matching channel, then add the result together.

    Multiple filters can be used in a convolution layer to detect multiple features.

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    The output of the layer then will have the same number of channels as the number of filters in the layer. This is convolution with 1 x 1 filter. This is a sample network with three convolution layers. At the end of the network, the output of the convolution layer is flattened and is connected to a logistic regression or a softmax output layer. Pooling layer is used to reduce the size of the representations and to speed up calculations, as well as to make some of the features it detects a bit more robust.

    Sample types of pooling are max pooling and avg poolingbut these days max pooling is more common. When done on input with multiple channels, pooling reduces the height and width nW and nH but keeps nC unchanged:.

    Lecun, L. Bottou, Y. Bengio and P. Haffner :. The number 16 refers to the fact that the network has 16 trainable layers i. The problem with deeper neural networks are they are harder to train and once the number of layers reach certain number, the training error starts to raise again. Deep networks are also harder to train due to exploding and vanishing gradients problem. In the image above, the skip connection is depicted by the red line.

    The motivation of the inception network is, rather than requiring us to pick the filter size manually, let the network decide what is best to put in a layer. We give it choices and hopefully it will pick up what is best to use in that layer:.

    hinton cnn paper

    The problem with the above network is computation cost e. With this idea, an inception module will look like this:.

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    Below is an inception network called GoogLeNetdescribed in Going Deeper with Convolutions paper by Szegedy et allwhich has 9 inception modules:. Pada AlexNet kedalaman dari conv2 27x27x dan conv3 13x13xangka dan itu berasal dari mana? Apakah dari hasil experimen maka dipakailah angka itu?

    Suka Suka. Menurut saya, seperti juga pada pemilihan jumlah neuron di hidden layers di neural network standar, pemilihannya lebih berdasarkan pada eksperimentasi. You are commenting using your WordPress.Link to Part 1 Link to Part 2. The first half of the list AlexNet to ResNet deals with advancements in general network architecture, while the second half is just a collection of interesting papers in other subareas.

    The next best entry achieved an error of Safe to say, CNNs became household names in the competition from then on out. In the paper, the group discussed the architecture of the network which was called AlexNet. They used a relatively simple layout, compared to modern architectures. The network was made up of 5 conv layers, max-pooling layers, dropout layers, and 3 fully connected layers. The network they designed was used for classification with possible categories.

    The neural network developed by Krizhevsky, Sutskever, and Hinton in was the coming out party for CNNs in the computer vision community. This was the first time a model performed so well on a historically difficult ImageNet dataset.

    Utilizing techniques that are still used today, such as data augmentation and dropout, this paper really illustrated the benefits of CNNs and backed them up with record breaking performance in the competition. Named ZF Net, this model achieved an This architecture was more of a fine tuning to the previous AlexNet structure, but still developed some very keys ideas about improving performance.

    Another reason this was such a great paper is that the authors spent a good amount of time explaining a lot of the intuition behind ConvNets and showing how to visualize the filters and weights correctly.

    While we do currently have a better understanding than 3 years ago, this still remains an issue for a lot of researchers! The main contributions of this paper are details of a slightly modified AlexNet model and a very interesting way of visualizing feature maps. An input image is fed into the CNN and activations are computed at each level. This is the forward pass. We would store the activations of this one feature map, but set all of the other activations in the layer to 0, and then pass this feature map as the input into the deconvnet.

    This deconvnet has the same filters as the original CNN. This input then goes through a series of unpool reverse maxpoolingrectify, and filter operations for each preceding layer until the input space is reached.

    The reasoning behind this whole process is that we want to examine what type of structures excite a given feature map. Like we discussed in Part 1the first layer of your ConvNet is always a low level feature detector that will detect simple edges or colors in this particular case.

    hinton cnn paper

    We can see that with the second layer, we have more circular features that are being detected. One thing to note is that as you may remember, after the first conv layer, we normally have a pooling layer that downsamples the image for example, turns a 32x32x3 volume into a 16x16x3 volume. The effect this has is that the 2 nd layer has a broader scope of what it can see in the original image. For more info on deconvnet or the paper in general, check out Zeiler himself presenting on the topic.

    ZF Net was not only the winner of the competition inbut also provided great intuition as to the workings on CNNs and illustrated more ways to improve performance. The visualization approach described helps not only to explain the inner workings of CNNs, but also provides insight for improvements to network architectures. The fascinating deconv visualization approach and occlusion experiments make this one of my personal favorite papers.

    Simplicity and depth. Karen Simonyan and Andrew Zisserman of the University of Oxford created a 19 layer CNN that strictly used 3x3 filters with stride and pad of 1, along with 2x2 maxpooling layers with stride 2. Simple enough right? VGG Net is one of the most influential papers in my mind because it reinforced the notion that convolutional neural networks have to have a deep network of layers in order for this hierarchical representation of visual data to work.

    Keep it deep.With David E. Rumelhart and Ronald J. WilliamsHinton was co-author of a highly cited paper published in that popularized the backpropagation algorithm for training multi-layer neural networks, [14] although they were not the first to propose the approach.

    Hinton was educated at King's College, Cambridge graduating inwith a Bachelor of Arts in experimental psychology. Hinton taught a free online course on Neural Networks on the education platform Coursera in He is planning to "divide his time between his university research and his work at Google". Hinton's research investigates ways of using neural networks for machine learningmemoryperception and symbol processing.

    He has authored or co-authored over peer reviewed publications. Rumelhart and Hinton and Ronald J. Williams applied the backpropagation algorithm to multi-layer neural networks.

    Their experiments showed that such networks can learn useful internal representations of data. Rumelhart came up with the basic idea of backpropagation, so it's his invention. In Hinton coauthored an unsupervised learning paper titled Unsupervised learning of image transformations. In October and November respectively, Hinton published two open access research papers [42] [43] on the theme of capsule neural networkswhich according to Hinton are "finally something that works well.

    Top 5 Image Classification Research Papers Every Data Scientist Should Know

    Inhe was elected a foreign member of National Academy of Engineering "For contributions to the theory and practice of artificial neural networks and their application to speech recognition and computer vision". He has won the BBVA Foundation Frontiers of Knowledge Award in the Information and Communication Technologies category "for his pioneering and highly influential work" to endow machines with the ability to learn.

    Together with Yann LeCunand Yoshua BengioHinton won the Turing Award for conceptual and engineering breakthroughs that have made deep neural networks a critical component of computing. Hinton is the great-great-grandson both of logician George Boole whose work eventually became one of the foundations of modern computer science, and of surgeon and author James Hinton.

    Hinton's father was Howard Hinton. Hinton moved from the U. Hinton has petitioned against lethal autonomous weapons. Regarding existential risk from artificial intelligenceHinton typically declines to make predictions more than five years into the future, noting that exponential progress makes the uncertainty too great. Asked by Nick Bostrom why he continues research despite his grave concerns, Hinton stated, "I could give you the usual arguments. But the truth is that the prospect of discovery is too sweet.

    According to the same report, Hinton does not categorically rule out human beings controlling an artificial superintelligence, but warns that "there is not a good track record of less intelligent things controlling things of greater intelligence". Archived from the original on 11 November Retrieved 9 March CS1 maint: BOT: original-url status unknown link. From Wikipedia, the free encyclopedia.

    hinton cnn paper

    British-Canadian computer scientist and psychologist. Wimbledon, London. Applications of Backpropagation Boltzmann machine Deep learning Capsule neural network. Machine learning Neural networks Artificial intelligence Cognitive science Object recognition [2].

    C 4.7 - Complete ConvNet - CNN - Machine Learning - Object Detection - EvODN

    Neal [8] Ruslan Salakhutdinov [9] Ilya Sutskever [10]. Geoffrey Everest". Who's Who. Biographical Memoirs of Fellows of the Royal Society. A minimum description length framework for unsupervised learning PhD thesis. University of Toronto.A Capsule Neural Network CapsNet is a machine learning system that is a type of artificial neural network ANN that can be used to better model hierarchical relationships.

    The approach is an attempt to more closely mimic biological neural organization. This vector is similar to what is done for example when doing classification with localization in CNNs. Among other benefits, capsnets address the "Picasso problem" in image recognition: images that have all the right parts but that are not in the correct spatial relationship e. InGeoffrey Hinton et al. So-called credibility networks described the joint distribution over the latent variables and over the possible parse trees.

    A dynamic routing mechanism for capsule networks was introduced by Hinton and his team in Results were claimed to be considerably better than a CNN on highly overlapped digits.

    In Hinton's original idea one minicolumn would represent and detect one multidimensional entity. An invariant is an object property that does not change as a result of some transformation.

    For example, the area of a circle does not change if the circle is shifted to the left. Informally, an equivariant is a property that changes predictably under transformation. For example, the center of a circle moves by the same amount as the circle when shifted. A nonequivariant is a property whose value does not change predictably under a transformation. In computer vision, the class of an object is expected to be an invariant over many transformations.

    However, many other properties are instead equivariant. The volume of a cat changes when it is scaled. Equivariant properties such as a spatial relationship are captured in a posedata that describes an object's translationrotationscale and reflection. Translation is a change in location in one or more dimensions.

    Geoffrey Hinton

    Rotation is a change in orientation. Scale is a change in size. Reflection is a mirror image.

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    Unsupervised capsnets learn a global linear manifold between an object and its pose as a matrix of weights. In other words, capsnets can identify an object independent of its pose, rather than having to learn to recognize the object while including its spatial relationships as part of the object.

    In capsnets, the pose can incorporate properties other than spatial relationships, e. Multiplying the object by the manifold poses the object for an object, in space. Capsnets reject the pooling layer strategy of conventional CNNs that reduces the amount of detail to be processed at the next higher layer. Pooling allows a degree of translational invariance it can recognize the same object in a somewhat different location and allows a larger number of feature types to be represented.The Jaguars just don't have enough firepower to score a lot of points.

    Tough to do, given that the Jaguars own the No. Also not helping this pick: Earlier this season, Seattle was throttled by the Titans (before playing frantic catch-up football). I'm not confidentBlake Bortles and the Jags' receivers can capitalize on the Achilles' heel of Seattle right now: a banged-up secondary missing Richard Sherman and Kam Chancellor. Offensively, maybe the Seahawks should come out in no-huddle, letting Russell Wilson play with a sense of urgency in quarter No.

    Wilson is tied with Eli Manning(2011) for the most fourth-quarter touchdown passes in a single season. Blake Bortles played well against the Colts last week, and I think it will carry over here. It won't be as good, but good enough. The Jaguars win a low-scoring game. Schematically, these two defenses are mirror images of each other, but Jacksonville has the superior unit at this point.

    In fact, the Jaguars are the most talented team in football on the defensive side. The advantage Seattle has in this matchup is the fact the Seahawks offense practices against this scheme every day. Now, they have to travel all the way across the country, and Jacksonville is ready for a slugfest. This is a supreme test for both teams.

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    The 9 Deep Learning Papers You Need To Know About (Understanding CNNs Part 3)

    Published by Houghton Mifflin. Published by Houghton Mifflin Company. Toggle navigationNavigation openNavigation closedCatalogBrowseSearchFor EnterpriseLog InSign UpCourse Languages259 results of the English of Course LanguagesEnglish2591 result of the Spanish of Course LanguagesSpanish11 result of the Chinese (Simplified) of Course LanguagesChinese (Simplified)1Subtitle Languages260 results of the English of Subtitle LanguagesEnglish26016 results of the Spanish of Subtitle LanguagesSpanish1616 results of the Russian of Subtitle LanguagesRussian16All Topics180 results of the Data Science of All TopicsData Science180119 results of the Business of All TopicsBusiness11928 results of the Computer Science of All TopicsComputer Science28You searched for statistics.

    They provide simple summaries about the sample and the measures. Together with simple graphics analysis, they form the basis of virtually every quantitative analysis of data.

    With descriptive statistics you are simply describing what is or what the data shows. With inferential statistics, you are trying to reach conclusions that extend beyond the immediate data alone.

    For instance, we use inferential statistics to try to infer from the sample data what the population might think. Or, we use inferential statistics to make judgments of the probability that an observed difference between groups is a dependable one or one that might have happened by chance in this study.

    In a research study we may have lots of measures. Or we may measure a large number of people on any measure. Descriptive statistics help us to simplify large amounts of data in a sensible way. Each descriptive statistic reduces lots of data into a simpler summary. For instance, consider a simple number used to summarize how well a batter is performing in baseball, the batting average.

    This single number is simply the number of hits divided by the number of times at bat (reported to three significant digits). A batter who is hitting.A 3 finger tap will return you to the main screen, a 4 finger tap will cause a refresh. TIP: Some popular lightweight but powerful editors include Brackets, Sublime Text and Atom.

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    Get Started NowFind out how to install PhoneGap, then create and preview an app on your device almost as fast as a robot could do it. Read Our BlogHave you read our latest blog post yet.

    If not, go now there is solid gold in that post. Use of this website signifies your agreement to the Terms of Use and Privacy Policy and Cookies. The intention of this simulation framework is to first and foremost, generate and examine different Tangle environments under different conditions. These simulations help us to further improve and optimize the IOTA Tangle itself, but to also show the resiliency of the Tangle against attacks or to study new potential attacks.

    With the IOTA Foundation officially being set up, we intend to take this research to the next level and will start sharing more regular updates from the IOTA Research Team. This work on the simulation framework has been lead by Alon and I on two different fronts. In this blog post, with the accompanying paper, we will share some of the first results gathered from these simulations. The simulation software itself is currently being prepared for a public release.

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    Learn moreGet updatesGet updates. Minnesota returns to non-conference play Saturday with its first SEC road game in 16 seasons. Nebraska opened up a 16-point lead early in the second half en route to a 78-68 win over No.

    Nate Mason scored a season-high 26 points as No. Location Bud Walton Arena - Fayetteville, Ark. Tickets Click Here TV SEC Network Live Video WatchESPN Radio KFAN 100. Arkansas: Third Meeting (1-1)Minnesota vs. The game is the second of a home-and-home series that began last year in Minneapolis, an 85-71 Gopher win. It is Minnesota's first road game at an SEC school since Dec.

    Murphy has posted 10 double-doubles (highest in the country) thus far this season, and the three-time Big Ten Player of the Week became the first Gopher to win the award in back-to-back weeks. Murphy is also just the second Gopher to win it three times in the same season. He is additionally leads the country in offensive rebounds (5. He needs just six more to tie Joel Przybilla for the most by any Gopher in two or fewer seasons of play in the Maroon and Gold (165). Last season, the Gophers dropped a 75-74 overtime decision versus the Spartans, and had previously lost conference-opening tilts at Ohio State, at Purdue during 2014-15, and at home versus Michigan.


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