Whitening with PCA with code demonstration
When we are training our model on images, the raw input is quite redundant because the pixels that are adjacent to each other are highly correlated. The goal of Whitening is to reduce redundancy in these images by making features less correlated to each other and same variance
Porter Stemmer algorithm
Stemming is the process of reducing a word to its stem that affixes to suffixes and prefixes or to the roots of words lemma. We cover the algorithmic steps in Porter Stemmer algorithm, a native implementation in Python, implementation using Porter Stemmer algorithm from NLTK library and conclusion.
Model Evaluation: a crucial step in solving a machine learning problem
Models like Googlenet is used across various problems and MobileNet are designed for computational limited resources. It is a challenge to find the best technique or model for a given problem. We evaluate a model based on Test Harness, Performance Measure, Cross validation and Testing Algorithms.
Fully Connected Layer: The brute force layer of a Machine Learning model
Fully Connected layers in a neural networks are those layers where all the inputs from one layer are connected to every activation unit of the next layer. In most popular machine learning models, the last few layers are full connected layers which compiles the data extracted by previous layers
Convolution Layer: The layer that takes over 70% of time in a Machine Learning model
Convolutional Layer is the most important layer in a Machine Learning model where the important features from the input are extracted and where most of the computational time (>=70% of the total inference time) is spent. Concepts involved are kernel size, padding, feature map and strides