Algorithmic Problems & Neural Networks in Python

Algorithmic Problems & Neural Networks in Python

Algorithmic Problems & Neural Networks in Python. With the help of this course you can Learn the basic algorithmic methodologies from backtracking to dynamic programming: Sudoku, Knapsack problem.

This course was created by Holczer Balazs. It was rated 4.4 out of 5 by approx 3414 ratings. There are approx 85584 users enrolled with this course, so don’t wait to download yours now. This course also includes 5.5 hours on-demand video, 5 Articles, 3 Supplemental Resources, Full lifetime access, Access on mobile and TV & Certificate of Completion.

What Will You Learn?

  • Understand backtracking

  • Understand dynamic programming

  • Solve problems from scratch

  • Implement feedforward neural networks from scratch

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 research&development.

In each section we will talk about the theoretical background for all of these algorithms then we are going to implement these problems together.

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.

Rating:
4.9

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