[FTUForum.com] [UDEMY] Algorithmic Problems in Python [FTU]

Creation Time	Dec. 4, 2023, 7:06 a.m.
Last Access Time	Sept. 9, 2025, 12:28 a.m.
File Size	907.0 MB
Keywords	Algorithmic Python FTUForum Problems UDEMY in FTU com
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Total Files	119
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5. Neural Network Implementation/7. Neural network implementation - testing.mp4 68.4 MB
3. Dynamic Programming/4. Knapsack problem introduction.mp4 31.2 MB
5. Neural Network Implementation/6. Neural network implementation - mean squared error.mp4 30.8 MB
3. Dynamic Programming/5. Knapsack problem example.mp4 30.1 MB
2. Backtracking/3. N-queens problem implementation.mp4 29.0 MB
4. Neural Network Theory/12. Optimization - cost function.mp4 25.9 MB
2. Backtracking/6. Hamiltonian cycle implementation.mp4 24.8 MB
5. Neural Network Implementation/5. Neural network implementation - backpropagation.mp4 24.5 MB
2. Backtracking/2. N-queens problem introduction.mp4 22.6 MB
3. Dynamic Programming/7. Coin change problem introduction.mp4 22.3 MB
2. Backtracking/7. Coloring problem introduction.mp4 22.0 MB
2. Backtracking/4. Hamiltonian cycle introduction.mp4 21.7 MB
2. Backtracking/10. Knight tour implementation.mp4 21.1 MB
2. Backtracking/13. Maze problem implementation.mp4 20.7 MB
4. Neural Network Theory/17. Gradient calculation I - output layer.mp4 20.3 MB
4. Neural Network Theory/13. Simplified feedforward network.mp4 19.4 MB
4. Neural Network Theory/2. Axons and neurons in the human brain.mp4 19.0 MB
3. Dynamic Programming/11. Rod cutting problem example.mp4 18.9 MB
3. Dynamic Programming/6. Knapsack problem implementation.mp4 18.7 MB
4. Neural Network Theory/11. Feedforward neural networks.mp4 18.4 MB
3. Dynamic Programming/9. Coin change problem implementation.mp4 17.7 MB
2. Backtracking/8. Coloring problem implementation.mp4 17.5 MB
4. Neural Network Theory/5. Artificial neurons - the model.mp4 16.3 MB
4. Neural Network Theory/3. Modeling human brain.mp4 16.0 MB
5. Neural Network Implementation/3. Neural network implementation - initialize.mp4 14.9 MB
4. Neural Network Theory/14. Feedforward neural network topology.mp4 14.7 MB
3. Dynamic Programming/12. Rod cutting problem implementation.mp4 14.6 MB
2. Backtracking/1. Backtracking introduction.mp4 14.4 MB
4. Neural Network Theory/6. Artificial neurons - activation functions.mp4 14.0 MB
4. Neural Network Theory/16. Error calculation.mp4 13.7 MB
3. Dynamic Programming/8. Coin change problem example.mp4 13.6 MB
5. Neural Network Implementation/4. Neural network implementation - feedforward.mp4 13.4 MB
3. Dynamic Programming/2. Fibonacci numbers introduction.mp4 13.4 MB
4. Neural Network Theory/15. The learning algorithm.mp4 13.3 MB
2. Backtracking/5. Hamiltonian cycle illustration.mp4 13.2 MB
3. Dynamic Programming/10. Rod cutting problem introduction.mp4 12.7 MB
4. Neural Network Theory/19. Backpropagation.mp4 12.7 MB
3. Dynamic Programming/3. Fibonacci numbers implementation.mp4 12.0 MB
1. Introduction/1. Introduction.mp4 11.9 MB
4. Neural Network Theory/7. Artificial neurons - an example.mp4 11.2 MB
4. Neural Network Theory/8. Neural networks - the big picture.mp4 10.6 MB
5. Neural Network Implementation/1. Neural network implementation - representations.mp4 10.1 MB
4. Neural Network Theory/22. Applications of neural networks II - stock market forecast.mp4 9.5 MB
4. Neural Network Theory/23. Deep learning.mp4 9.5 MB
4. Neural Network Theory/18. Gradient calculation II - hidden layer.mp4 9.2 MB
2. Backtracking/9. Knight tour introduction.mp4 9.0 MB
2. Backtracking/14. NP-complete problems.mp4 9.0 MB
4. Neural Network Theory/21. Applications of neural networks I - character recognition.mp4 8.8 MB
3. Dynamic Programming/1. Dynamic programming introduction.mp4 8.6 MB
5. Neural Network Implementation/2. Neural network implementation - helper methods.mp4 8.1 MB
2. Backtracking/12. Maze problem introduction.mp4 6.8 MB
4. Neural Network Theory/4. Learning paradigms.mp4 6.4 MB
4. Neural Network Theory/9. Applications of neural networks.mp4 5.2 MB
4. Neural Network Theory/20. Backpropagation II.mp4 4.7 MB
2. Backtracking/3. N-queens problem implementation.vtt 14.4 KB
3. Dynamic Programming/5. Knapsack problem example.vtt 13.9 KB
3. Dynamic Programming/4. Knapsack problem introduction.vtt 13.9 KB
2. Backtracking/2. N-queens problem introduction.vtt 12.1 KB
4. Neural Network Theory/12. Optimization - cost function.vtt 11.9 KB
2. Backtracking/6. Hamiltonian cycle implementation.vtt 11.2 KB
2. Backtracking/7. Coloring problem introduction.vtt 10.8 KB
3. Dynamic Programming/7. Coin change problem introduction.vtt 10.1 KB
2. Backtracking/4. Hamiltonian cycle introduction.vtt 9.9 KB
2. Backtracking/10. Knight tour implementation.vtt 9.7 KB
4. Neural Network Theory/2. Axons and neurons in the human brain.vtt 9.4 KB
3. Dynamic Programming/11. Rod cutting problem example.vtt 9.3 KB
4. Neural Network Theory/17. Gradient calculation I - output layer.vtt 9.3 KB
2. Backtracking/13. Maze problem implementation.vtt 9.0 KB
4. Neural Network Theory/13. Simplified feedforward network.vtt 9.0 KB
4. Neural Network Theory/11. Feedforward neural networks.vtt 8.9 KB
5. Neural Network Implementation/5. Neural network implementation - backpropagation.vtt 8.8 KB
3. Dynamic Programming/9. Coin change problem implementation.vtt 8.5 KB
4. Neural Network Theory/3. Modeling human brain.vtt 8.4 KB
2. Backtracking/8. Coloring problem implementation.vtt 7.9 KB
3. Dynamic Programming/6. Knapsack problem implementation.vtt 7.9 KB
4. Neural Network Theory/5. Artificial neurons - the model.vtt 7.3 KB
2. Backtracking/1. Backtracking introduction.vtt 6.8 KB
3. Dynamic Programming/12. Rod cutting problem implementation.vtt 6.7 KB
2. Backtracking/5. Hamiltonian cycle illustration.vtt 6.6 KB
4. Neural Network Theory/6. Artificial neurons - activation functions.vtt 6.6 KB
3. Dynamic Programming/8. Coin change problem example.vtt 6.6 KB
4. Neural Network Theory/14. Feedforward neural network topology.vtt 6.6 KB
4. Neural Network Theory/16. Error calculation.vtt 6.5 KB
3. Dynamic Programming/2. Fibonacci numbers introduction.vtt 6.4 KB
4. Neural Network Theory/15. The learning algorithm.vtt 6.0 KB
3. Dynamic Programming/10. Rod cutting problem introduction.vtt 6.0 KB
5. Neural Network Implementation/7. Neural network implementation - testing.vtt 5.9 KB
5. Neural Network Implementation/3. Neural network implementation - initialize.vtt 5.8 KB
4. Neural Network Theory/19. Backpropagation.vtt 5.7 KB
3. Dynamic Programming/3. Fibonacci numbers implementation.vtt 5.4 KB
5. Neural Network Implementation/4. Neural network implementation - feedforward.vtt 5.4 KB
4. Neural Network Theory/8. Neural networks - the big picture.vtt 4.9 KB
5. Neural Network Implementation/1. Neural network implementation - representations.vtt 4.9 KB
4. Neural Network Theory/22. Applications of neural networks II - stock market forecast.vtt 4.7 KB
4. Neural Network Theory/7. Artificial neurons - an example.vtt 4.6 KB
4. Neural Network Theory/23. Deep learning.vtt 4.6 KB
2. Backtracking/14. NP-complete problems.vtt 4.5 KB
4. Neural Network Theory/21. Applications of neural networks I - character recognition.vtt 4.4 KB
2. Backtracking/9. Knight tour introduction.vtt 4.4 KB
4. Neural Network Theory/18. Gradient calculation II - hidden layer.vtt 4.1 KB
2. Backtracking/12. Maze problem introduction.vtt 4.1 KB
5. Neural Network Implementation/6. Neural network implementation - mean squared error.vtt 3.9 KB
3. Dynamic Programming/1. Dynamic programming introduction.vtt 3.8 KB
5. Neural Network Implementation/2. Neural network implementation - helper methods.vtt 3.8 KB
7. BONUS/1. DISCOUNT FOR OTHER COURSES!.html 3.7 KB
4. Neural Network Theory/4. Learning paradigms.vtt 3.1 KB
4. Neural Network Theory/9. Applications of neural networks.vtt 2.4 KB
1. Introduction/1. Introduction.vtt 2.1 KB
4. Neural Network Theory/20. Backpropagation II.vtt 2.0 KB
2. Backtracking/11. UPDATE Knight's Tour.html 716 bytes
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6. Course Materials (DOWNLOADS)/1. Course material.html 157 bytes
4. Neural Network Theory/10. ---------- BACKPROPAGATION ----------.html 42 bytes
4. Neural Network Theory/1. ---------- NEURAL NETWORKS INTRODUCTION ----------.html 35 bytes

[FTUForum.com] [UDEMY] Algorithmic Problems in Python [FTU]

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