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Books related to Artificial Intelligence, Machine Learning, Deep Learning and Neural Networks

Artifical Intelligence & Machine Learning & Deep Learning & Neural Networks Books

Books related to Artificial Intelligence, Machine Learning, Deep Learning and Neural Networks

Online Books about AI and Deep Learning

Math For AI and AI

Deep Learning

Neural Networks

Tensor Flow

Machine Learning

Data Science And Big Data

R and Data Science

MathLab and Deep Learning

Linear Algebra Algorithms Video Tutorials - Numerical Computing with Python

Gauss Elimination Method with Pivoting

In this tutorial, the basic steps of Gauss Elimination (or Gaussian Elimination) method to solve a system of linear equations are explained in details with examples, algorithms and Python codes. Gauss elimination (after Carl Friedrich Gauss, 1777-1855) is a the basis of all other elimination methods applied to solve systems of linear equations.

Cholesky Factorization Method - Decomposition

In this video, Cholesky factorization method (after André-Louis Cholesky) is explained with examples. The tutorial includes the definitions of the LU-decomposition and Cholesky decomposition, the conditions of Cholesky decomposition, the use of Numpy eigenvalue functions to test the positive definiteness, the derivation of Cholesky algorithm and Coding in Python.

Gauss-Jordan Method Tutorial - Step-By-Step Theory & Coding

In this tutorial, the procedure of Gauss-Jordan elimination method is explained step-by-step using symbolic and numeric examples. The general formulas and Gauss-Jordan algorithm are applied to write a Python code to solve the numeric example.

Lagrange Interpolation Method: Algorithm, Computation and Plot

Lagrange interpolation (or Lagrangian interpolation) method is one of the most basic and common methods to apply the interpolation polynomials. It was named after the great mathematician Joseph-Louis Lagrange (1736-1813). This tutorial explains the Lagrangian polynomial form of the interpolation function, the algorithm of the method and the Python code by using Python lists with basic for loops and by using the Numpy arrays by using conditional slicing in addition to plotting the interpolation function versus the given data points by using matplotlib.pyplot module.

Binomial Distributions - Probabilities of Probabilities

The binomial distribution consists of the probabilities of each of the possible numbers of successes on N trials for independent events that each have a probability of π (the Greek letter pi) of occurring. For the coin flip example, N = 2 and π = 0.5.

Normal Distribution & Probability Problems

This calculus video tutorial provides a basic introduction into normal distribution and probability. It explains how to solve normal distribution problems using a simple chart and using calculus by evaluating the definite integral of the probability density function for a bell shaped curve or normal distribution curve. This video contains 1 practice problem in the form of a word problem with many parts giving you plenty of examples to master this topic. In this video, I explain how to evaluate the definite integral using wolfram's alpha online calculator for definite integrals. You need to determine the population mean mu and standard deviation sigma as well as the lower and upper limits of integration in order to determine the probability of an event occurring within a certain range of X values where X is a continuous random variable. You need to be familiar with the 68-95-99.7 rule. Approximately 68% of the population lies within 1 standard deviation of the population mean or average. 95% of the population lies within 2 standard deviations of the mean and 99.7% lies within 3 standard deviations of the mean.