Training Neural Networks to predict the outcome is an essential cycle of this utilit. Various techniques like feedforward neural networks, back propagation learning, supervised and unsupervised learning, are covered in this tutorial. This tutorial also contains example of XOR Logic function trained using a 3-layered Neural Network.
Lists in Python are a repository of items organized in order from first to last. For readers familiar with C/C++, lists are similar to arrays. Lists can contain any type of variable, be it numbers, letters, strings and even nested lists and they can contain as many variables as we want. They are written as a list of comma-separated values (items) between square brackets.
Matplotlib is a tool for data visualization and this tool built upon the Numpy and Scipy framework. It was developed by John Hunter in 2002. Matplotlib is a library for making 2D plots of arrays in Python. Matplotlib also able to create simple plots with just a few commands and along with limited 3D graphic support.
Decision tree is a binary (mostly) structure where each node best splits the data to classify a response variable. Tree starts with a Root which is the first node and ends with the final nodes which are known as leaves of the tree.
This is an introductory tutorial for Neural Network, it explains model representation, feed-forward propagation, multi-layer neural network and weighted functions, etc.
Once a machine learning model has been used, the next step is to estimate its performance. There are various performance metrics used to evaluate the machine learning models and some of them are discussed in this tutorial This tutorial takes the case study of binary classifier for facial and non-facial image. Performance matrix discussed in this tutorial are accuracy, confusion matrix, log-loss, ROC and AUC.
This tutorial series is about the data.table package in R that is used for Data Analysis. It is an ideal package for dataset handing in R. This tutorial contains techniques to create, subset and select a data.table, following by usage of various functions and operations on rows and columns; including chaining, indexing, etc.
This is a detailed tutorial for SciPy library in Python, which is used for scientific computation and sohpisticated mathematical calculations. This tutorial has an inrtoduction to the library along with various notable modules of the library. The tutorial has examples of codes and syntax for various functions like File Input/Output, Linear Algebra Operations, Special Functions in Scipy, Optimization functions, Fast Fourier Transformation, Numerical Integration, Image Processing and Signal Proccessing using SciPy library.
This tutorial presents a step-by-step guide to install Apache Spark in a standalone mode. It also includes installation of JAVA 8 for JVM and has examples of ETL (Extract, Transform and Load) operations on Spark.
This tutorial is an elaborate introduction to Apache Spark. Several questions are addressed in this tutorial:
- What is Apache Spark?
- Why was Apache Spark Developed?
- What is the importance and benefits of Apache Spark?
- Discussion about Hadoop and Apache Spark
- Apache Spark Components and Architecture
- What are Apache Spark Eco-system components?