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Algorithmic Problems & Neural Networks in Python
English | Size: 826.48 MB
Category: Tutorial
Learn the basic algorithmic methodologies from backtracking to dynamic programming: Sudoku, Knapsack problem
What you'll learn

Understand backtracking
Understand dynamic programming
Solve problems from scratch
Implement feedforward neural networks from scratch

Requirements

Basic Python
Description

This course is about the fundamental concepts of algorithmic problems, focusing on backtracking and dynamic programming. As far as I am concerned these techniques are very important nowadays, algorithms can be used (and have several applications) in several fields from software engineering to investment banking or R&D.

Section 1:

what is backtracking
n-queens problem
Hamiltonian cycle problem
knight's tour problem
coloring problem
NP-complete problems
Section 2:

what is dynamic programming
Fibonacci numbers
knapsack problem
coin change problem
rod cutting problem
Section 3:

what are neural networks
feedforward neural networks
neural network implementation from sratch
In each section we will talk about the theoretical background for all of these algorithms then we are going to implement these problems one by one.

The first chapter is about backtracking: we will talk about problems such as n-queens problem or hamiltonian cycles and coloring problem. In the second chapter we will talk about dynamic programming, theory first then the concrete examples one by one: Fibonacci sequence problem and knapsack problem. Finally we are going to discuss neural networks.

Thanks for joining the course, let's get started!

Who this course is for:

This course is meant for newbies who are not familiar with algorithmic problems in the main or students looking for some refresher
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