In the previous post Word Embeddings and Document Vectors: Part 1. Similarity we laid the groundwork for using bag-of-words based document vectors in conjunction with word embeddings (pre-trained or custom-trained) for computing document similarity, as a precursor to classification. It seemed that document+word vectors were better at picking up on similarities… Read more »
Closed-form solutions are sweet. No hand-wringing/waving required to make a point. Given the assumptions, the model predictions are exact so we can readily evaluate the impact of assumptions. And, we get the means to evaluate alternate (e.g. numerical) approaches applied to these same limiting cases with the exact solution. We are… Read more »
Machine learning is alchemy – researchers in artificial intelligence at Google have recently proclaimed. Any high school or college student that has ever tried to solve nonlinear systems of equations with gradient descent method knows that already, kind of… Even for a perfect bowl-shaped cost-surface, gradient descent method will converge… Read more »
Michael Nielsen provides a visual demonstration in his web book Neural Networks and Deep Learning that a 1-layer deep neural network can match any function . It is just a matter of the number of neurons to get a prediction that is arbitrarily close – the more the neurons the better the approximation…. Read more »
