Deep Learning- Deep Learning using Python for Beginners
Focused View
6:13:01
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1. Introduction to Course.mp4
03:36
2. Request for Your Honest Review.mp4
01:18
3. Links for the Courses Materials and Codes.html
1. Links for the Courses Materials and Codes.html
2. Problem to Solve Part 1.mp4
02:00
3. Problem to Solve Part 2.mp4
02:26
4. Problem to Solve Part 3.mp4
01:42
5. Linear Equation.mp4
03:18
6. Linear Equation Vectorized.mp4
03:00
7. 3D Feature Space.mp4
03:46
8. N Dimensional Space.mp4
02:30
9. Theory of Perceptron.mp4
01:46
10. Implementing Basic Perceptron.mp4
05:37
11. Logical Gates for Perceptrons.mp4
02:46
12. Perceptron Training Part 1.mp4
01:39
13. Perceptron Training Part 2.mp4
03:40
14. Learning Rate.mp4
03:14
15. Perceptron Training Part 3.mp4
03:31
16. Perceptron Algorithm.mp4
01:00
17. Coading Perceptron Algo (Data Reading & Visualization).mp4
05:51
18. Coading Perceptron Algo (Perceptron Step).mp4
07:22
19. Coading Perceptron Algo (Training Perceptron).mp4
06:42
20. Coading Perceptron Algo (Visualizing the Results).mp4
03:53
21. Problem with Linear Solutions.mp4
02:32
22. Solution to Problem.mp4
01:03
23. Error Functions.mp4
02:21
24. Discrete vs Continuous Error Function.mp4
02:25
25. Sigmoid Function.mp4
03:01
26. Multi-Class Problem.mp4
01:17
27. Problem of Negative Scores.mp4
03:02
28. Need of Softmax.mp4
01:22
29. Coding Softmax.mp4
04:06
30. One Hot Encoding.mp4
02:40
31. Maximum Likelihood Part 1.mp4
05:30
32. Maximum Likelihood Part 2.mp4
03:47
33. Cross Entropy.mp4
04:06
34. Cross Entropy Formulation.mp4
07:38
35. Multi Class Cross Entropy.mp4
03:51
36. Cross Entropy Implementation.mp4
04:14
37. Sigmoid Function Implementation.mp4
00:57
38. Output Function Implementation.mp4
02:10
1. Links for the Courses Materials and Codes.html
2. Introduction to Gradient Decent.mp4
05:21
3. Convex Functions.mp4
02:31
4. Use of Derivatives.mp4
03:12
5. How Gradient Decent Works.mp4
03:34
6. Gradient Step.mp4
01:54
7. Logistic Regression Algorithm.mp4
01:37
8. Data Visualization and Reading.mp4
06:10
9. Updating Weights in Python.mp4
04:14
10. Implementing Logistic Regression.mp4
12:44
11. Visualization and Results.mp4
08:43
12. Gradient Decent vs Perceptron.mp4
04:35
13. Linear to Non Linear Boundaries.mp4
04:42
14. Combining Probabilities.mp4
02:07
15. Weighted Sums.mp4
03:02
16. Neural Network Architecture.mp4
12:09
17. Layers and DEEP Networks.mp4
04:44
18. Multi Class Classification.mp4
02:48
19. Basics of Feed Forward.mp4
07:51
20. Feed Forward for DEEP Net.mp4
04:57
21. Deep Learning Algo Overview.mp4
01:57
22. Basics of Back Propagation.mp4
06:32
23. Updating Weights.mp4
02:46
24. Chain Rule for BackPropagation.mp4
05:53
25. Sigma Prime.mp4
02:23
26. Data Analysis NN Implementation.mp4
05:25
27. One Hot Encoding (NN Implementation).mp4
03:11
28. Scaling the Data (NN Implementation).mp4
01:48
29. Splitting the Data (NN Implementation).mp4
04:55
30. Helper Functions (NN Implementation).mp4
02:18
31. Training (NN Implementation).mp4
12:25
32. Testing (NN Implementation).mp4
03:21
1. Links for the Courses Materials and Codes.html
2. Underfitting vs Overfitting.mp4
05:19
3. Early Stopping.mp4
03:51
4. Quiz.mp4
00:58
5. Solution & Regularization.mp4
05:59
6. L1 & L2 Regularization.mp4
03:12
7. Dropout.mp4
02:59
8. Local Minima Problem.mp4
02:55
9. Random Restart Solution.mp4
04:27
10. Vanishing Gradient Problem.mp4
04:16
11. Other Activation Functions.mp4
03:19
1. Links for the Courses Materials and Codes.html
2. Final Project Part 1.mp4
11:19
3. Final Project Part 2.mp4
13:16
4. Final Project Part 3.mp4
12:58
5. Final Project Part 4.mp4
12:19
6. Final Project Part 5.mp4
08:06
7. THANK YOU Bonus Video.mp4
01:20
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View courses UdemyStudents take courses primarily to improve job-related skills.Some courses generate credit toward technical certification. Udemy has made a special effort to attract corporate trainers seeking to create coursework for employees of their company.
- language english
- Training sessions 86
- duration 6:13:01
- Release Date 2024/05/19
