Machine Learning Deployments

Machine learning became available after being able to store and process large amounts of data. To be able to process we need to convert that data into numerical form. Often times we have tabular structures which is converted into matrices. For higher dimensional problems we could have tensors or 4d tensors. Then, in order to process that information we need to rely on operations on matrices. Thus we will utilize linear algebra. The linear regression formulation we have mentioned uses weighted sum of input features. Let us remind that these features are the rows of the data table converted into numerical form. This weighted sum is inner product of two vectors could be written as : w = [w1, w2, ...] x = [x1, x2, ...] s = w1*x1 + w2*x2 ... The dot product of two vectors is a scalar. If vectors are normalized the result would be cosine similarity. Now, this is model of single neuron in neural networks. It gives single value output for single input vector. If we have multiple dimens...

lesson.land/mldeploy

As from previous post, linear regression has analytical solution. For a given dataset the optimization problem reduces to linear algebra psudeo-inverse problem.  ``` W = (XX')^-1XY``` This would be the training step for the algorithm. Python code would be very easy.  np.linalg.inv(X.dot(X.T)).dot(X).dot(Y) Later once the parameters W is defined runtime would be a single matrix multiplication as our linear model. Y' = WX  Could be written as  Y_est = W.dot(X)