Jash Sheth - OpenGenus IQ: Learn Algorithms, DL, System Design

Implementing CNN in Python with Tensorflow for MNIST digit recognition

In this article, we will develop and train a convolutional neural network (CNN) in Python using TensorFlow for digit recognifition with MNIST as our dataset. We will give an overview of the MNIST dataset and the model architecture we will work on before diving into the code.

Machine Learning (ML)

Training, saving and loading Artificial Neural Networks in Keras

We demonstrate how to code a Artificial neural network model and train and save it in JSON or H5 format which can be loaded later for any inference task. We use Keras/ TensorFlow to demonstrate this transfer learning and used Pima Indian Diabetes dataset in CSV format

Machine Learning (ML)

Random Decision Forest

Random forests or random decision forests are an ensemble learning method for classification, regression and other tasks that operates by constructing a multitude of decision trees at training time. There are three important hyperparameters namely n_estimators, random_state and max_features

Machine Learning (ML)

Logistic Regression

Logistic Regression is an efficient regression algorithm that aims to predict categorical values, often binary. It is widely used in the medical field to classify sick and healthy individuals and areas that need to determine a client's risk such as financial companies.

Machine Learning (ML)

Principal Component Regression (PCR)

Principal Component Regression (PCR) is an algorithm for reducing the multi-collinearity of a dataset. PCR is basically using PCA, and then performing Linear Regression on these new PCs. The key idea of how PCR aims to do this, is to use PCA on the dataset before regression.

Machine Learning (ML)

Ridge Regression

Ridge regression is an efficient regression technique that is used when we have multicollinearity or when the number of predictor variables in a set exceed the number of observations. It uses L2 regularization and solves the problem of overfitting. Concepts of overfitting and regularization is basis

Algorithms

Expectation Maximization Clustering Algorithm

Expectation Maximization Clustering algorithm is much more robust than K-Means, as it uses two parameters, Mean and Standard Deviation to define a particular cluster. This simple addition of calculating the Standard Deviation, helps the EM algorithm do well in a lot of fail cases of K-Means

Algorithms

Mean Shift Clustering Algorithm

Mean Shift clustering is an unsupervised clustering algorithm that groups data directly without being trained on labelled data. It is hierarchical in nature. It starts off with a kernel, which is basically a circular sliding window. The bandwidth the radius of this sliding window is pre-decided

clustering algorithm

K+ Means Clustering algorithm

K+ Means algorithm is a clustering algorithm and an improvement to K means clustering algorithm and solves the problem of choosing K (number of clusters). It is great at detecting outliers and forming new clusters. The complexity is O(t*(k^2)*n) which is slightly more than K means algorithm