Udemy - Master Deep Learning with TensorFlow in Python [DC]

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[DesireCourse.Net] Udemy - Master Deep Learning with TensorFlow in Python 1. Welcome! Course introduction
  • 1. Meet your instructors and why you should study machine learning.mp4 (105.8 MB)
  • 1. Meet your instructors and why you should study machine learning.vtt (8.8 KB)
  • 2. What does the course cover.mp4 (16.4 MB)
  • 2. What does the course cover.vtt (5.5 KB)
  • 3. What does the course cover - Quiz.html (0.2 KB)
10. Gradient descent and learning rates
  • 1. Stochastic gradient descent.mp4 (9.4 MB)
  • 1. Stochastic gradient descent.vtt (4.2 KB)
  • 2. Gradient descent pitfalls.mp4 (4.3 MB)
  • 2. Gradient descent pitfalls.vtt (2.5 KB)
  • 3. Momentum.mp4 (6.1 MB)
  • 3. Momentum.vtt (3.1 KB)
  • 4. Learning rate schedules.mp4 (10.3 MB)
  • 4. Learning rate schedules.vtt (5.3 KB)
  • 5. Learning rate schedules. A picture.mp4 (3.1 MB)
  • 5. Learning rate schedules. A picture.vtt (1.9 KB)
  • 6. Adaptive learning rate schedules.mp4 (8.9 MB)
  • 6. Adaptive learning rate schedules.vtt (4.6 KB)
  • 7. Adaptive moment estimation.mp4 (7.8 MB)
  • 7. Adaptive moment estimation.vtt (2.9 KB)
11. Preprocessing
  • 1. Preprocessing introduction.mp4 (8.4 MB)
  • 1. Preprocessing introduction.vtt (3.4 KB)
  • 2. Basic preprocessing.mp4 (3.7 MB)
  • 2. Basic preprocessing.vtt (1.5 KB)
  • 3. Standardization.mp4 (8.3 MB)
  • 3. Standardization.vtt (5.3 KB)
  • 4. Dealing with categorical data.mp4 (6.1 MB)
  • 4. Dealing with categorical data.vtt (2.4 KB)
  • 5. One-hot and binary encoding.mp4 (6.2 MB)
  • 5. One-hot and binary encoding.vtt (4.2 KB)
12. The MNIST example
  • 1. The dataset.mp4 (7.4 MB)
  • 1. The dataset.vtt (3.1 KB)
  • 10. MNIST - exercises.html (2.3 KB)
  • 10.1 MNIST_Exercises_All.html (0.1 KB)
  • 11. MNIST - solutions.html (2.2 KB)
  • 11.1 MNIST_Depth_Solution.html (0.1 KB)
  • 11.10 MNIST_Learning_rate_Part_1_Solution.html (0.2 KB)
  • 11.11 TensorFlow_MNIST_Activation_functions_Part_1_Solution.html (0.2 KB)
  • 11.2 MNIST_take_note_of_time_Solution.html (0.2 KB)
  • 11.3 Width_and_Depth_Solution.html (0.2 KB)
  • 11.4 MNIST_Learning_rate_Part_2_Solution.html (0.2 KB)
  • 11.5 MNIST_around_98_percent_accuracy_solution.html (0.2 KB)
  • 11.6 MNIST_Batch_size_Part_2_Solution.html (0.2 KB)
  • 11.7 MNIST_Width_Solution.html (0.1 KB)
  • 11.8 MNIST_Batch_size_Part_1_Solution.html (0.2 KB)
  • 11.9 MNIST_Activation_functions_Part_2_Solution.html (0.2 KB)
  • 2. How to tackle the MNIST.mp4 (7.3 MB)
  • 2. How to tackle the MNIST.vtt (3.2 KB)
  • 3. Importing the relevant packages.mp4 (5.5 MB)
  • 3. Importing the relevant packages.vtt (1.9 KB)
  • 3.1 TensorFlow_MNIST_with_comments_Part_1.html (0.2 KB)
  • 4. Outlining the model.mp4 (18.4 MB)
  • 4. Outlining the model.vtt (7.8 KB)
  • 4.1 TensorFlow_MNIST_with_comments_Part_2.html (0.2 KB)
  • 5. Declaring the loss and the optimization algorithm.mp4 (7.1 MB)
  • 5. Declaring the loss and the optimization algorithm.vtt (3.1 KB)
  • 5.1 TensorFlow_MNIST_with_comments_Part_3.html (0.2 KB)
  • 6. Accuracy of prediction.mp4 (12.4 MB)
  • 6. Accuracy of prediction.vtt (4.6 KB)
  • 6.1 TensorFlow_MNIST_with_comments_Part_4.html (0.2 KB)
  • 7. Batching and early stopping.mp4 (4.6 MB)
  • 7. Batching and early stopping.vtt (2.5 KB)
  • 7.1 TensorFlow_MNIST_with_comments_Part_5.html (0.2 KB)
  • 8. Learning.mp4 (15.9 MB)
  • 8. Learning.vtt (8.9 KB)
  • 8.1 TensorFlow_MNIST_with_comments_Part_6.html (0.2 KB)
  • 9. Discuss the results and test.mp4 (22.0 MB)
  • 9. Discuss the results and test.vtt (7.2 KB)
  • 9.1 TensorFlow_MNIST_with_comments.html (0.1 KB)
13. Business case
  • 1. Exploring the dataset and identifying predictors.mp4 (23.3 MB)
  • 1. Exploring the dataset and identifying predictors.vtt (9.4 KB)
  • 1.1 Audiobooks_data.csv.csv (710.8 KB)
  • 10. Testing the model.mp4 (4.3 MB)
  • 10. Testing the model.vtt (2.3 KB)
  • 11. A comment on the homework.mp4 (13.0 MB)
  • 11. A comment on the homework.vtt (4.6 KB)
  • 11.1 Homework exercise.html (0.1 KB)
  • 12. Final exercise.html (0.4 KB)
  • 12.1 Homework exercise.html (0.1 KB)
  • 2. Outlining the business case solution.mp4 (3.8 MB)
  • 2. Outlining the business case solution.vtt (2.2 KB)
  • 3. Balancing the dataset.mp4 (13.8 MB)
  • 3. Balancing the dataset.vtt (3.9 KB)
  • 4. Preprocessing the data.mp4 (34.3 MB)
  • 4. Preprocessing the data.vtt (11.8 KB)
  • 4.1 Preprocessing.html (0.1 KB)
  • 5. Preprocessing exercise.html (0.4 KB)
  • 5.1 Preprocessing exercise.html (0.1 KB)
  • 6. Create a class for batching.mp4 (27.6 MB)
  • 6. Create a class for batching.vtt (6.9 KB)
  • 6.1 Class.html (0.1 KB)
  • 7. Outlining the model.mp4 (19.5 MB)
  • 7. Outlining the model.vtt (6.1 KB)
  • 7.1 Outlining the model.html (0.1 KB)
  • 8. Optimizing the algorithm.mp4 (12.2 MB)
  • 8. Optimizing the algorithm.vtt (5.7 KB)
  • 8.1 Optimizing the algorithm.html (0.1 KB)
  • 9. Interpreting the result.mp4 (5.4 MB)
  • 9. Interpreting the result.vtt (2.6 KB)
  • 9.1 Interpreting the result.html (0.1 KB)
14. Appendix Linear Algebra Fundamentals
  • 1. What is a Matrix.mp4 (33.6 MB)
  • 1. What is a Matrix.vtt (3.8 KB)
  • 10. Dot Product of Matrices.mp4 (49.4 MB)
  • 10. Dot Product of Matrices.vtt (8.2 KB)
  • 10.1 Dot Product of Matrices Python Notebook.html (0.2 KB)
  • 11. Why is Linear Algebra Useful.mp4 (144.3 MB)
  • 11. Why is Linear Algebra Useful.vtt (10.3 KB)
  • 2. Scalars and Vectors.mp4 (33.8 MB)

  • Description

    Master Deep Learning with TensorFlow in Python

    Build Deep Learning Algorithms with TensorFlow, Dive into Neural Networks and Master the #1 Skill of the Data Scientist

    For More Courses Visit: https://desirecourse.net

    For More Courses Visit: https://courseclub.me



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Udemy - Master Deep Learning with TensorFlow in Python [DC]

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1.4 GB
seeders:5
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Udemy - Master Deep Learning with TensorFlow in Python [DC]

Torrent hash: B32FA4DC9596273110ECC8A0055A9502488D916B