Udemy | Algorithmic Problems in Python [FTU]

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1. Introduction
  • 1. Introduction.mp4 (11.9 MB)
  • 1. Introduction.vtt (2.1 KB)
2. Backtracking
  • 1. Backtracking introduction.mp4 (14.4 MB)
  • 1. Backtracking introduction.vtt (6.8 KB)
  • 10. Knight tour implementation.mp4 (21.1 MB)
  • 10. Knight tour implementation.vtt (9.7 KB)
  • 11. UPDATE Knight's Tour.html (0.7 KB)
  • 12. Maze problem introduction.mp4 (6.8 MB)
  • 12. Maze problem introduction.vtt (4.1 KB)
  • 13. Maze problem implementation.mp4 (20.7 MB)
  • 13. Maze problem implementation.vtt (9.0 KB)
  • 14. NP-complete problems.mp4 (9.0 MB)
  • 14. NP-complete problems.vtt (4.5 KB)
  • 2. N-queens problem introduction.mp4 (22.6 MB)
  • 2. N-queens problem introduction.vtt (12.1 KB)
  • 3. N-queens problem implementation.mp4 (29.0 MB)
  • 3. N-queens problem implementation.vtt (14.4 KB)
  • 4. Hamiltonian cycle introduction.mp4 (21.7 MB)
  • 4. Hamiltonian cycle introduction.vtt (9.9 KB)
  • 5. Hamiltonian cycle illustration.mp4 (13.2 MB)
  • 5. Hamiltonian cycle illustration.vtt (6.6 KB)
  • 6. Hamiltonian cycle implementation.mp4 (24.8 MB)
  • 6. Hamiltonian cycle implementation.vtt (11.2 KB)
  • 7. Coloring problem introduction.mp4 (22.0 MB)
  • 7. Coloring problem introduction.vtt (10.8 KB)
  • 8. Coloring problem implementation.mp4 (17.5 MB)
  • 8. Coloring problem implementation.vtt (7.9 KB)
  • 9. Knight tour introduction.mp4 (9.0 MB)
  • 9. Knight tour introduction.vtt (4.4 KB)
3. Dynamic Programming
  • 1. Dynamic programming introduction.mp4 (8.6 MB)
  • 1. Dynamic programming introduction.vtt (3.8 KB)
  • 10. Rod cutting problem introduction.mp4 (12.7 MB)
  • 10. Rod cutting problem introduction.vtt (6.0 KB)
  • 11. Rod cutting problem example.mp4 (18.9 MB)
  • 11. Rod cutting problem example.vtt (9.3 KB)
  • 12. Rod cutting problem implementation.mp4 (14.6 MB)
  • 12. Rod cutting problem implementation.vtt (6.7 KB)
  • 2. Fibonacci numbers introduction.mp4 (13.4 MB)
  • 2. Fibonacci numbers introduction.vtt (6.4 KB)
  • 3. Fibonacci numbers implementation.mp4 (12.0 MB)
  • 3. Fibonacci numbers implementation.vtt (5.4 KB)
  • 4. Knapsack problem introduction.mp4 (31.2 MB)
  • 4. Knapsack problem introduction.vtt (13.9 KB)
  • 5. Knapsack problem example.mp4 (30.1 MB)
  • 5. Knapsack problem example.vtt (13.9 KB)
  • 6. Knapsack problem implementation.mp4 (18.7 MB)
  • 6. Knapsack problem implementation.vtt (7.9 KB)
  • 7. Coin change problem introduction.mp4 (22.3 MB)
  • 7. Coin change problem introduction.vtt (10.1 KB)
  • 8. Coin change problem example.mp4 (13.6 MB)
  • 8. Coin change problem example.vtt (6.6 KB)
  • 9. Coin change problem implementation.mp4 (17.7 MB)
  • 9. Coin change problem implementation.vtt (8.5 KB)
4. Neural Network Theory
  • 1. ---------- NEURAL NETWORKS INTRODUCTION ----------.html (0.0 KB)
  • 10. ---------- BACKPROPAGATION ----------.html (0.0 KB)
  • 11. Feedforward neural networks.mp4 (18.4 MB)
  • 11. Feedforward neural networks.vtt (8.9 KB)
  • 12. Optimization - cost function.mp4 (25.9 MB)
  • 12. Optimization - cost function.vtt (11.9 KB)
  • 13. Simplified feedforward network.mp4 (19.4 MB)
  • 13. Simplified feedforward network.vtt (9.0 KB)
  • 14. Feedforward neural network topology.mp4 (14.7 MB)
  • 14. Feedforward neural network topology.vtt (6.6 KB)
  • 15. The learning algorithm.mp4 (13.3 MB)
  • 15. The learning algorithm.vtt (6.0 KB)
  • 16. Error calculation.mp4 (13.7 MB)
  • 16. Error calculation.vtt (6.5 KB)
  • 17. Gradient calculation I - output layer.mp4 (20.3 MB)
  • 17. Gradient calculation I - output layer.vtt (9.3 KB)
  • 18. Gradient calculation II - hidden layer.mp4 (9.2 MB)
  • 18. Gradient calculation II - hidden layer.vtt (4.1 KB)
  • 19. Backpropagation.mp4 (12.7 MB)
  • 19. Backpropagation.vtt (5.7 KB)
  • 2. Axons and neurons in the human brain.mp4 (19.0 MB)
  • 2. Axons and neurons in the human brain.vtt (9.4 KB)
  • 20. Backpropagation II.mp4 (4.7 MB)
  • 20. Backpropagation II.vtt (2.0 KB)
  • 21. Applications of neural networks I - character recognition.mp4 (8.8 MB)
  • 21. Applications of neural networks I - character recognition.vtt (4.4 KB)
  • 22. Applications of neural networks II - stock market forecast.mp4 (9.5 MB)
  • 22. Applications of neural networks II - stock market forecast.vtt (4.7 KB)
  • 23. Deep learning.mp4 (9.5 MB)
  • 23. Deep learning.vtt (4.6 KB)
  • 3. Modeling human brain.mp4 (16.0 MB)
  • 3. Modeling human brain.vtt (8.4 KB)
  • 4. Learning paradigms.mp4 (6.4 MB)
  • 4. Learning paradigms.vtt (3.1 KB)
  • 5. Artificial neurons - the model.mp4 (16.3 MB)
  • 5. Artificial neurons - the model.vtt (7.3 KB)
  • 6. Artificial neurons - activation functions.mp4 (14.0 MB)
  • 6. Artificial neurons - activation functions.vtt (6.6 KB)
  • 7. Artificial neurons - an example.mp4 (11.2 MB)
  • 7. Artificial neurons - an example.vtt (4.6 KB)
  • 8. Neural networks - the big picture.mp4 (10.6 MB)
  • 8. Neural networks - the big picture.vtt (4.9 KB)
  • 9. Applications of neural networks.mp4 (5.2 MB)
  • 9. Applications of neural

Description

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Learn recursion, backtracking (n-queens problem etc.) and dynamic programming (knapsack problem etc.)

BESTSELLER

Created by : Holczer Balazs
Last updated : 4/2019
Language : English
Caption (CC) : Included
Torrent Contains : 119 Files, 8 Folders
Course Source : https://www.udemy.com/algorithmic-problems-in-python/

What you'll learn

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

Course content
all 40 lectures 04:04:05

Requirements

• Basic Python

Description

This course is about the fundamental concepts of algorithmic problems, focusing on recursion, 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 recursion
• stack memory and recursion
• factorial numbers problem
• Fibonacci numbers
• towers of Hanoi problem
• recursion vs iteration

Section 2:

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

Section 3:

• what is dynamic programming
• Fibonacci numbers
• knapsack problem
• coin change problem
• rod cutting problem

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 recursion. Why is it crucial to know about recursion as a computer scientist? Why stack memory is crucial in recursion? We will consider several recursion related problems such as factorial problem or Fibonacci numbers. The second chapter is about backtracking: we will talk about problems such as n-queens problem or hamiltonian cycles and coloring problem. In the last chapter we will talk about dynamic programming, theory first then the concrete examples one by one: Fibonacci sequence problem and knapsack problem.

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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Udemy | Algorithmic Problems in Python [FTU]

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Download torrent
907 MB
seeders:15
leechers:5
Udemy | Algorithmic Problems in Python [FTU]

Torrent hash: 8C0BE3A454C0C48A94E7809BC6DFC9754FE9D82A