IncredibleAI

What is Bias and Variance? The figure on the left side classifies the circles and the cross "Just right". The second figure includes the cross and fails to classify optimally this is called as High Bias which is also an indication of Underfitting the data. The figure on the right side does something weird and classifies circles and the cross which is way on the opposite side, this classifier fails terribly and ends up overfitting the data. This is called as High Variance or Overfitting. The concepts of Bias and Variance is very important to understand because looking at High/ Low Bias and Variance we will be able to predict if our training data error is at fault or development data error. Analysis of these errors we will get an insight into HOW well are we fitting our data to the model. To understand Bias and Variance we need to understand the training set error and dev set error. Let's talk more about High Bias   For simplification, let's also assume ...

Why ConvNets? ConvNets are used for Image Analysis. ConvNets reduces a given image into a form which is easier to work on without losing its important features, Which helps in prediction. ConvNets are vastly prefered due to their 1) Parameter Sharing property & 2) Sparsity of Connections. 1)Parameter Sharing: A feature detector (edges, shapes) filter could be used for multiple parts of the same image. 2) Sparsity of Connections: In each layer, each output value depends only on a small number of inputs. What are ConvNets? ConvNets, also known as Convolutional Neural Networks are Neural Nets with Convolutional Layers between them. ConvNets also known as Feed Forward networks. The convolutional layers are made of filters. These filters detect various patterns in an image. The simple filters detect simple features such as Horizontal edges, vertical edges, circles and so on. The filters in the deeper layers of CNN can detect more complex patterns. These filters are initial...