Udemy | Algorithmic Problems in Python [FTU]
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| 1. (FreeTutorials.Us) Download Udemy Paid Courses For Free.url | 307.2 B | ||
| 1. ---------- NEURAL NETWORKS INTRODUCTION ----------.html | 0 B | ||
| 1. Backtracking introduction.mp4 | 14.4 MB | ||
| 1. Backtracking introduction.vtt | 6.8 KB | ||
| 1. Course material.html | 204.8 B | ||
| 1. DISCOUNT FOR OTHER COURSES!.html | 3.7 KB | ||
| 1. Dynamic programming introduction.mp4 | 8.6 MB | ||
| 1. Dynamic programming introduction.vtt | 3.8 KB | ||
| 1. Introduction.mp4 | 11.9 MB | ||
| 1. Introduction.vtt | 2.1 KB | ||
| 1. Neural network implementation - representations.mp4 | 10.1 MB | ||
| 1. Neural network implementation - representations.vtt | 4.9 KB | ||
| 10. ---------- BACKPROPAGATION ----------.html | 0 B | ||
| 10. Knight tour implementation.mp4 | 21.1 MB | ||
| 10. Knight tour implementation.vtt | 9.7 KB | ||
| 10. Rod cutting problem introduction.mp4 | 12.7 MB | ||
| 10. Rod cutting problem introduction.vtt | 6 KB | ||
| 11. Feedforward neural networks.mp4 | 18.4 MB | ||
| 11. Feedforward neural networks.vtt | 8.9 KB | ||
| 11. Rod cutting problem example.mp4 | 18.9 MB | ||
| 11. Rod cutting problem example.vtt | 9.3 KB | ||
| 11. UPDATE Knight's Tour.html | 716.8 B | ||
| 12. Maze problem introduction.mp4 | 6.8 MB | ||
| 12. Maze problem introduction.vtt | 4.1 KB | ||
| 12. Optimization - cost function.mp4 | 25.9 MB | ||
| 12. Optimization - cost function.vtt | 11.9 KB | ||
| 12. Rod cutting problem implementation.mp4 | 14.6 MB | ||
| 12. Rod cutting problem implementation.vtt | 6.7 KB | ||
| 13. Maze problem implementation.mp4 | 20.7 MB | ||
| 13. Maze problem implementation.vtt | 9 KB | ||
| 13. Simplified feedforward network.mp4 | 19.4 MB | ||
| 13. Simplified feedforward network.vtt | 9 KB | ||
| 14. Feedforward neural network topology.mp4 | 14.7 MB | ||
| 14. Feedforward neural network topology.vtt | 6.6 KB | ||
| 14. NP-complete problems.mp4 | 9 MB | ||
| 14. NP-complete problems.vtt | 4.5 KB | ||
| 15. The learning algorithm.mp4 | 13.3 MB | ||
| 15. The learning algorithm.vtt | 6 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. (FreeCoursesOnline.Me) Download Udacity, Masterclass, Lynda, PHLearn, Pluralsight Free.url | 307.2 B | ||
| 2. Axons and neurons in the human brain.mp4 | 19 MB | ||
| 2. Axons and neurons in the human brain.vtt | 9.4 KB | ||
| 2. Fibonacci numbers introduction.mp4 | 13.4 MB | ||
| 2. Fibonacci numbers introduction.vtt | 6.4 KB | ||
| 2. N-queens problem introduction.mp4 | 22.6 MB | ||
| 2. N-queens problem introduction.vtt | 12.1 KB | ||
| 2. Neural network implementation - helper methods.mp4 | 8.1 MB | ||
| 2. Neural network implementation - helper methods.vtt | 3.8 KB | ||
| 20. Backpropagation II.mp4 | 4.7 MB | ||
| 20. Backpropagation II.vtt | 2 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. (NulledPremium.com) Download Cracked Website Themes, Plugins, Scripts And Stock Images.url | 204.8 B | ||
| 3. Fibonacci numbers implementation.mp4 | 12 MB | ||
| 3. Fibonacci numbers implementation.vtt | 5.4 KB | ||
| 3. Modeling human brain.mp4 | 16 MB | ||
| 3. Modeling human brain.vtt | 8.4 KB | ||
| 3. N-queens problem implementation.mp4 | 29 MB | ||
| 3. N-queens problem implementation.vtt | 14.4 KB | ||
| 3. Neural network implementation - initialize.mp4 | 14.9 MB | ||
| 3. Neural network implementation - initialize.vtt | 5.8 KB | ||
| 4. (FTUApps.com) Download Cracked Developers Applications For Free.url | 204.8 B | ||
| 4. Hamiltonian cycle introduction.mp4 | 21.7 MB | ||
| 4. Hamiltonian cycle introduction.vtt | 9.9 KB | ||
| 4. Knapsack problem introduction.mp4 | 31.2 MB | ||
| 4. Knapsack problem introduction.vtt | 13.9 KB | ||
| 4. Learning paradigms.mp4 | 6.4 MB | ||
| 4. Learning paradigms.vtt | 3.1 KB | ||
| 4. Neural network implementation - feedforward.mp4 | 13.4 MB | ||
| 4. Neural network implementation - feedforward.vtt | 5.4 KB | ||
| 5. (Discuss.FTUForum.com) FTU Discussion Forum.url | 307.2 B | ||
| 5. Artificial neurons - the model.mp4 | 16.3 MB | ||
| 5. Artificial neurons - the model.vtt | 7.3 KB | ||
| 5. Hamiltonian cycle illustration.mp4 | 13.2 MB | ||
| 5. Hamiltonian cycle illustration.vtt | 6.6 KB | ||
| 5. Knapsack problem example.mp4 | 30.1 MB | ||
| 5. Knapsack problem example.vtt | 13.9 KB | ||
| 5. Neural network implementation - backpropagation.mp4 | 24.5 MB | ||
| 5. Neural network implementation - backpropagation.vtt | 8.8 KB | ||
| 6. Artificial neurons - activation functions.mp4 | 14 MB | ||
| 6. Artificial neurons - activation functions.vtt | 6.6 KB | ||
| 6. Hamiltonian cycle implementation.mp4 | 24.8 MB | ||
| 6. Hamiltonian cycle implementation.vtt | 11.2 KB | ||
| 6. Knapsack problem implementation.mp4 | 18.7 MB | ||
| 6. Knapsack problem implementation.vtt | 7.9 KB | ||
| 6. Neural network implementation - mean squared error.mp4 | 30.8 MB | ||
| 6. Neural network implementation - mean squared error.vtt | 3.9 KB | ||
| 7. Artificial neurons - an example.mp4 | 11.2 MB | ||
| 7. Artificial neurons - an example.vtt | 4.6 KB | ||
| 7. Coin change problem introduction.mp4 | 22.3 MB | ||
| 7. Coin change problem introduction.vtt | 10.1 KB | ||
| 7. Coloring problem introduction.mp4 | 22 MB | ||
| 7. Coloring problem introduction.vtt | 10.8 KB | ||
| 7. Neural network implementation - testing.mp4 | 68.4 MB | ||
| 7. Neural network implementation - testing.vtt | 5.9 KB | ||
| 8. Coin change problem example.mp4 | 13.6 MB | ||
| 8. Coin change problem example.vtt | 6.6 KB | ||
| 8. Coloring problem implementation.mp4 | 17.5 MB | ||
| 8. Coloring problem implementation.vtt | 7.9 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 networks.vtt | 2.4 KB | ||
| 9. Coin change problem implementation.mp4 | 17.7 MB | ||
| 9. Coin change problem implementation.vtt | 8.5 KB | ||
| 9. Knight tour introduction.mp4 | 9 MB | ||
| 9. Knight tour introduction.vtt | 4.4 KB | ||
| How you can help Team-FTU.txt | 204.8 B | ||
| ▲ 119 total files | |||
Description
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Our Forum for discussion >>> https://discuss.ftuforum.com/
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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