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To understand homographies and how to find them, you will need a good dose of projective geometry. I will briefly describe some preliminary concepts that you need to know before trying to find the homography, but don't expect to understand all these concepts with one reading iteration and only by reading this answer, if you are not familiar with them, ...


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In neural networks, the family of functions and the shapes that they can make for decision surfaces is determined by the activation function you use (in your case, tanh or hyperbolic tangent). Assuming at least one hidden layer, then the universal approximation theorem applies. How closely you can approximate any given function is limited by the number of ...


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Both PR and Theoretically Valid Although Anima Anandkumar's presentation is a puff piece for NVidia, her representation is not contrary to theory. ... next level [above NVidia's GPU success] ... means new algorithmic research. So, if you think about the current computations in our deep learning systems, they are all based on linear algebra. Can we come ...


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Reinforcement Learning is a method for learning to perform beneficial actions in an environment. One way this is accomplished is by learning to predict useful actions as a function of the observed state of the environment. Another is by learning to predict the expected utility gain of doing an action in a particular observed state. Usually the fact that the ...


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You might want to have a look at the wikipedia article of PCA, where it says: "The $k$th component can be found by subtracting the first $k − 1$ principal components from $\mathbf{X}$:" $$\hat{\mathbf{X}}_k = \mathbf{X} - \sum_{s=1}^{k-1}\mathbf{X}\mathbf{w}_s\mathbf{w}_s^T$$ Then you repeat the process to find the next component: $$\mathbf{w}_k = \...


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If you are working with supervised learning, each training example has a label. That label is your classification of the provided input. Just like linear or logistic regression, if your problem only has 2 classes (e.g. determine whether a tumor is malignant or not), your network will have a single output. An output value of 1.0 could represent one class and ...


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You can sometimes exploit the structure of your matrix to perform faster matrix multiplication. For example, if your matrix is sparse (or dense), there are algorithms that exploit this fact. In your case, you can actually compute $A^n$ in less time than $\mathcal{O}(n^3$). For example, have a look at this question at CS SE and this one at Stack Overflow (...


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Define P as a CSP where X, Y are the variables, domain of both is {1,2,3,4} and conditions in normal form are: node-condition X<4 arc-condition X=Y P is 2-consistent (arc consistent) because for any X value it is possible to find a Y value that fulfills the arc-condition X=Y. However, P is not 1-consistent (node-consistent) because exist a X value (X=4)...


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So I think in case of a logistic regression task a Neural Network works something like this. First of all I think all nodes perform the job of mapping a point to a quadrant, in a n-space co-ordinate system where the n-space is decided iteratively by the problem statement itself. In short the nodes decide which combination of polynomial terms of the input ...


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For other who were wondering the same questions as me, I'll answer it. My view above was inconsistent. Ultimately the last layer of simple feed-forward networks don't have any special properties previous layers exhibit. NNs are just glorified mathematical functions. It distorts space with linear(matrix multiplies) and non-linear functions. Theres no '...


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