Surya Pratap Singh - OpenGenus IQ: Learn Algorithms, DL, System Design

Understand Autoencoders by implementing in TensorFlow

Autoencoders are a type of unsupervised neural networks and has two components: encoder and decoder. We have provided the intuitive explanation of the working of autoencoder along with a step by step TensorFlow implementation.

Machine Learning (ML)

Understand Stacked Generalization (blending) in depth with code demonstration

Basic idea behind stacking is to combine the predictions of 2 or more models to get predictions of higher accuracy than the previous submodels. We demonstrate the idea using a code example

Machine Learning (ML)

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

Machine Learning (ML)

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.

Machine Learning (ML)

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.

Machine Learning (ML)

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

Machine Learning (ML)

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