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[DesireCourse.Net] Udemy - Algorithmic Problems & Neural Networks in Python
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[DesireCourse.Net] Udemy - Algorithmic Problems & Neural Networks in Python
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最近下载:
2025-08-31
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文件列表
3. Dynamic Programming/4. Knapsack problem introduction.mp4
32.7 MB
3. Dynamic Programming/5. Knapsack problem example.mp4
31.5 MB
2. Backtracking/3. N-queens problem implementation.mp4
30.4 MB
4. Neural Network Theory/12. Optimization - cost function.mp4
27.1 MB
2. Backtracking/6. Hamiltonian cycle implementation.mp4
26.0 MB
5. Neural Network Implementation/5. Neural network implementation - backpropagation.mp4
25.6 MB
2. Backtracking/2. N-queens problem introduction.mp4
23.7 MB
3. Dynamic Programming/7. Coin change problem introduction.mp4
23.4 MB
2. Backtracking/7. Coloring problem introduction.mp4
23.1 MB
2. Backtracking/4. Hamiltonian cycle introduction.mp4
22.8 MB
2. Backtracking/10. Knight tour implementation.mp4
22.1 MB
2. Backtracking/13. Maze problem implementation.mp4
21.7 MB
4. Neural Network Theory/17. Gradient calculation I - output layer.mp4
21.3 MB
4. Neural Network Theory/13. Simplified feedforward network.mp4
20.4 MB
4. Neural Network Theory/2. Axons and neurons in the human brain.mp4
19.9 MB
3. Dynamic Programming/11. Rod cutting problem example.mp4
19.9 MB
3. Dynamic Programming/6. Knapsack problem implementation.mp4
19.6 MB
4. Neural Network Theory/11. Feedforward neural networks.mp4
19.3 MB
3. Dynamic Programming/9. Coin change problem implementation.mp4
18.6 MB
2. Backtracking/8. Coloring problem implementation.mp4
18.3 MB
5. Neural Network Implementation/7. Neural network implementation - testing.mp4
18.3 MB
4. Neural Network Theory/5. Artificial neurons - the model.mp4
17.1 MB
4. Neural Network Theory/3. Modeling human brain.mp4
16.8 MB
5. Neural Network Implementation/3. Neural network implementation - initialize.mp4
15.6 MB
4. Neural Network Theory/14. Feedforward neural network topology.mp4
15.4 MB
3. Dynamic Programming/12. Rod cutting problem implementation.mp4
15.3 MB
2. Backtracking/1. Backtracking introduction.mp4
15.1 MB
4. Neural Network Theory/6. Artificial neurons - activation functions.mp4
14.7 MB
4. Neural Network Theory/16. Error calculation.mp4
14.4 MB
3. Dynamic Programming/8. Coin change problem example.mp4
14.2 MB
5. Neural Network Implementation/4. Neural network implementation - feedforward.mp4
14.1 MB
3. Dynamic Programming/2. Fibonacci numbers introduction.mp4
14.1 MB
4. Neural Network Theory/15. The learning algorithm.mp4
13.9 MB
2. Backtracking/5. Hamiltonian cycle illustration.mp4
13.8 MB
3. Dynamic Programming/10. Rod cutting problem introduction.mp4
13.4 MB
4. Neural Network Theory/19. Backpropagation.mp4
13.3 MB
3. Dynamic Programming/3. Fibonacci numbers implementation.mp4
12.6 MB
4. Neural Network Theory/7. Artificial neurons - an example.mp4
11.7 MB
4. Neural Network Theory/8. Neural networks - the big picture.mp4
11.1 MB
5. Neural Network Implementation/1. Neural network implementation - representations.mp4
10.6 MB
4. Neural Network Theory/22. Applications of neural networks II - stock market forecast.mp4
10.0 MB
4. Neural Network Theory/23. Deep learning.mp4
9.9 MB
4. Neural Network Theory/18. Gradient calculation II - hidden layer.mp4
9.6 MB
2. Backtracking/9. Knight tour introduction.mp4
9.5 MB
2. Backtracking/14. NP-complete problems.mp4
9.4 MB
5. Neural Network Implementation/6. Neural network implementation - mean squared error.mp4
9.3 MB
4. Neural Network Theory/21. Applications of neural networks I - character recognition.mp4
9.2 MB
3. Dynamic Programming/1. Dynamic programming introduction.mp4
9.0 MB
5. Neural Network Implementation/2. Neural network implementation - helper methods.mp4
8.5 MB
2. Backtracking/12. Maze problem introduction.mp4
7.2 MB
4. Neural Network Theory/4. Learning paradigms.mp4
6.7 MB
4. Neural Network Theory/9. Applications of neural networks.mp4
5.4 MB
4. Neural Network Theory/20. Backpropagation II.mp4
4.9 MB
1. Introduction/1. Introduction.mp4
4.4 MB
2. Backtracking/3. N-queens problem implementation.vtt
14.8 kB
3. Dynamic Programming/5. Knapsack problem example.vtt
14.3 kB
3. Dynamic Programming/4. Knapsack problem introduction.vtt
14.2 kB
2. Backtracking/2. N-queens problem introduction.vtt
12.4 kB
4. Neural Network Theory/12. Optimization - cost function.vtt
12.1 kB
2. Backtracking/6. Hamiltonian cycle implementation.vtt
11.4 kB
2. Backtracking/7. Coloring problem introduction.vtt
11.1 kB
3. Dynamic Programming/7. Coin change problem introduction.vtt
10.4 kB
2. Backtracking/4. Hamiltonian cycle introduction.vtt
10.1 kB
2. Backtracking/10. Knight tour implementation.vtt
10.0 kB
4. Neural Network Theory/2. Axons and neurons in the human brain.vtt
9.6 kB
3. Dynamic Programming/11. Rod cutting problem example.vtt
9.5 kB
4. Neural Network Theory/17. Gradient calculation I - output layer.vtt
9.5 kB
2. Backtracking/13. Maze problem implementation.vtt
9.3 kB
4. Neural Network Theory/13. Simplified feedforward network.vtt
9.2 kB
4. Neural Network Theory/11. Feedforward neural networks.vtt
9.1 kB
5. Neural Network Implementation/5. Neural network implementation - backpropagation.vtt
9.0 kB
3. Dynamic Programming/9. Coin change problem implementation.vtt
8.7 kB
4. Neural Network Theory/3. Modeling human brain.vtt
8.6 kB
2. Backtracking/8. Coloring problem implementation.vtt
8.1 kB
3. Dynamic Programming/6. Knapsack problem implementation.vtt
8.1 kB
4. Neural Network Theory/5. Artificial neurons - the model.vtt
7.4 kB
2. Backtracking/1. Backtracking introduction.vtt
6.9 kB
3. Dynamic Programming/12. Rod cutting problem implementation.vtt
6.9 kB
2. Backtracking/5. Hamiltonian cycle illustration.vtt
6.7 kB
4. Neural Network Theory/6. Artificial neurons - activation functions.vtt
6.7 kB
3. Dynamic Programming/8. Coin change problem example.vtt
6.7 kB
4. Neural Network Theory/14. Feedforward neural network topology.vtt
6.7 kB
4. Neural Network Theory/16. Error calculation.vtt
6.7 kB
3. Dynamic Programming/2. Fibonacci numbers introduction.vtt
6.5 kB
4. Neural Network Theory/15. The learning algorithm.vtt
6.2 kB
3. Dynamic Programming/10. Rod cutting problem introduction.vtt
6.2 kB
5. Neural Network Implementation/7. Neural network implementation - testing.vtt
6.0 kB
5. Neural Network Implementation/3. Neural network implementation - initialize.vtt
6.0 kB
4. Neural Network Theory/19. Backpropagation.vtt
5.9 kB
3. Dynamic Programming/3. Fibonacci numbers implementation.vtt
5.5 kB
5. Neural Network Implementation/4. Neural network implementation - feedforward.vtt
5.5 kB
4. Neural Network Theory/8. Neural networks - the big picture.vtt
5.0 kB
5. Neural Network Implementation/1. Neural network implementation - representations.vtt
5.0 kB
4. Neural Network Theory/22. Applications of neural networks II - stock market forecast.vtt
4.8 kB
4. Neural Network Theory/7. Artificial neurons - an example.vtt
4.7 kB
4. Neural Network Theory/23. Deep learning.vtt
4.7 kB
2. Backtracking/14. NP-complete problems.vtt
4.6 kB
4. Neural Network Theory/21. Applications of neural networks I - character recognition.vtt
4.5 kB
2. Backtracking/9. Knight tour introduction.vtt
4.5 kB
4. Neural Network Theory/18. Gradient calculation II - hidden layer.vtt
4.2 kB
2. Backtracking/12. Maze problem introduction.vtt
4.2 kB
5. Neural Network Implementation/6. Neural network implementation - mean squared error.vtt
4.0 kB
3. Dynamic Programming/1. Dynamic programming introduction.vtt
3.9 kB
5. Neural Network Implementation/2. Neural network implementation - helper methods.vtt
3.9 kB
7. BONUS/1. DISCOUNT FOR OTHER COURSES!.html
3.8 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
2.2 kB
4. Neural Network Theory/20. Backpropagation II.vtt
2.1 kB
2. Backtracking/11. UPDATE Knight's Tour.html
716 Bytes
6. Course Materials (DOWNLOADS)/1. Course material.html
157 Bytes
[DesireCourse.Net].url
51 Bytes
[CourseClub.Me].url
48 Bytes
4. Neural Network Theory/10. ---------- BACKPROPAGATION ----------.html
42 Bytes
4. Neural Network Theory/1. ---------- NEURAL NETWORKS INTRODUCTION ----------.html
35 Bytes
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