Step by Step procedure in Conducting the Hypothesis Testing: prerequisites: Part 1: What is Hypothesis Testing and When it is Used? Part 2: How to decide Null and Alternate Hypothesis? https://www.isixsigma.com/ Before diving into the steps, first let’s understand the Important Terminology we need to understand: Null Hypothesis: It is a well-established fact which is believed by all the people. It can also defined as an Assumption that treats everything equally and similarly. Alternate Hypothesis: It is a new claim made against the Null Hypothesis. If we have enough evidence against the Null Hypothesis we will reject Null Hypothesis. P-value: Probability of Null Hypothesis being true. Significance level: probability of rejecting the Null Hypothesis when it is true. It is a critical point where we decide whether the Null Hypothesis is rejected or not. Generally, the significant level will be 0.05 which means a 5 percent risk while concluding
Vgg16 Architecture: Prerequisites: ALEXNet : ALEXNet Architecture * vgg refers to visual geometry group who has developed vgg16 architecture. * 16 refers to number of layers. VGG16 or VGGNet is a convolution neural network and it is a simplified and better version of Alexnet. Remembering Alexnet architecture is very difficult, Later on 2014, vgg16 came as an architecture and it is very simple to remember. VGG16 is very simple where: · It is built on Convolution operations with fixed kernel_size=(3x3), padding=’same’, and stride=1 for all Convolution layers. · And also Maxpooling operation with pool_size=(2x2) and stride=2 for all Maxpool layers. Let's see the architecture of vgg16 source:https://qph.fs.quoracdn.net/main-qimg-e657c195fc2696c7d5fc0b1e3682fde6 If you observe the figure, As said all convolution layers have 3x3 kernels and all maxpool layers have size of 2x2. Note 1: For maxpool layers the padding=‘valid’ which means there is no paddi