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 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.