Udemy - Generative Adversarial Networks (GANs) in Practice

  • CategoryOther
  • TypeTutorials
  • LanguageEnglish
  • Total size1.5 GB
  • Uploaded Byfreecoursewb
  • Downloads54
  • Last checkedOct. 20th '21
  • Date uploadedOct. 18th '21
  • Seeders 12
  • Leechers9
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Infohash : 7DAC8C8DF08C9E606C7D9E5F9C17D83075714A28

Generative Adversarial Networks (GANs) in Practice



MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch
Genre: eLearning | Language: English + srt | Duration: 42 lectures (4h 1m) | Size: 1.41 GB
With Introductory Review on Artificial Neural Networks and Deep Learning Algorithms and Models
What you'll learn:
The fundamentals of Artificial Neural Networks (ANNs) and reviews state-of-the-art DL examples.
The fundamental of Deep learning and the most popular algorithms.
The most popular GAN algorithms features and requirements .
How to implement a GAN model in PRACTICE.
Several examples and applications of GAN.

Requirements
Probability,
Calculus,
Basic of Python, Tensor Flow, Keras, and Numpy.

Description
Deep learning is one of the most recent and advanced topics in machine learning, with several applications in many fields. It shows promising results in many areas, from computer vision to drug discovery and stock market prediction. There are many books and articles in deep learning that discuss its algorithms, theories, and applications. Also, because of its capabilities and potentials in solving different problems by deploying different data types, many researchers and people who are not in computer science or related fields are interested in learning and using deep learning architectures in their projects.

This course gives you some fundamentals of artificial neural networks and deep learning and then has focused on Generative Adversarial Network and its applications with some coding examples to understand the concepts better. The course is suitable for people who are new in the machine learning field and deep learning and would like to learn how to implement deep learning algorithms (especially GAN algorithms) using python, TensorFlow, and Keras.

https://FreeCourseWeb.com

Files:

[ FreeCourseWeb.com ] Udemy - Generative Adversarial Networks (GANs) in Practice
  • Get Bonus Downloads Here.url (0.2 KB)
    • ~Get Your Files Here ! 1. Introduction
    • 1. Introduction.mp4 (65.0 MB)
    • 1. Introduction.srt (5.4 KB)
    • 1.1 Introduction.pdf (2.5 MB)
    2. Machine Learning
    • 1. Machine Learning.mp4 (84.5 MB)
    • 1. Machine Learning.srt (7.3 KB)
    • 1.1 Lecture 2.pdf (1.0 MB)
    • 1.2 Probabilistic Machine Learning Advanced Topics.html (0.1 KB)
    • 10. Semi-Supervised Learning.html (0.2 KB)
    • 11. Learning Methods Comparison.mp4 (29.9 MB)
    • 11. Learning Methods Comparison.srt (2.2 KB)
    • 11.1 SuperVize Me What’s the Difference Between Supervised, Unsupervised, Semi-Supervised and Reinforcement Learning.html (0.1 KB)
    • 12. Learning Methods Comparison.html (0.2 KB)
    • 13. Reinforcement Learning.mp4 (39.7 MB)
    • 13. Reinforcement Learning.srt (3.4 KB)
    • 13.1 Reinforcement Learning A Survey.html (0.1 KB)
    • 14. Reinforcement Learning.html (0.2 KB)
    • 15. Learning Example.mp4 (31.9 MB)
    • 15. Learning Example.srt (2.6 KB)
    • 15.1 Machine Learning 6 Real-World Examples.html (0.1 KB)
    • 16. Learning Example.html (0.2 KB)
    • 17. Design a Learning System.mp4 (50.0 MB)
    • 17. Design a Learning System.srt (4.3 KB)
    • 17.1 Machine Learning.html (0.1 KB)
    • 18. Design a Learning System.html (0.2 KB)
    • 19. Design a Learning System.html (0.2 KB)
    • 2. Machine Learning.html (0.2 KB)
    • 20. Leaning Error Types.mp4 (20.1 MB)
    • 20. Leaning Error Types.srt (1.6 KB)
    • 20.1 Probabilistic Machine Learning Advanced Topics.html (0.1 KB)
    • 21. Leaning Error Types.html (0.2 KB)
    • 22. Underfitting and Overfitting.mp4 (13.6 MB)
    • 22. Underfitting and Overfitting.srt (1.3 KB)
    • 22.1 Underfitting and Overfitting in Machine Learning.html (0.2 KB)
    • 23. Underfitting and Overfitting.html (0.2 KB)
    • 24. Underfitting and Overfitting.html (0.2 KB)
    • 25. Linear Regression.html (0.2 KB)
    • 26. SVM.html (0.2 KB)
    • 27. Classification model Benchmark.html (0.2 KB)
    • 3. Machine Learning.html (0.2 KB)
    • 4. Machine Learning.html (0.2 KB)
    • 5. Supervised Learning.mp4 (41.0 MB)
    • 5. Supervised Learning.srt (4.1 KB)
    • 5.1 Artificial Intelligence A Modern Approach.html (0.1 KB)
    • 6. Supervised Learning.html (0.2 KB)
    • 7. Unsupervised Learning.mp4 (34.3 MB)
    • 7. Unsupervised Learning.srt (3.6 KB)
    • 7.1 Unsupervised Learning Foundations of Neural Computation.html (0.1 KB)
    • 8. Unsupervised Learning.html (0.2 KB)
    • 9. Semi-Supervised Learning.mp4 (23.0 MB)
    • 9. Semi-Supervised Learning.srt (3.0 KB)
    • 9.1 Semi-Supervised Learning Literature Survey.html (0.1 KB)
    3. Artificial Neural Networks
    • 1. Neural Networks.mp4 (85.6 MB)
    • 1. Neural Networks.srt (7.2 KB)
    • 1.1 Neural Networks and Learning Machines.html (0.1 KB)
    • 10. Single-Layer Neural Networks (Perceptron).html (0.2 KB)
    • 11. Multi-Layer Neural Networks.mp4 (39.3 MB)
    • 11. Multi-Layer Neural Networks.srt (2.9 KB)
    • 11.1 Multilayer Perceptron.html (0.1 KB)
    • 12. Multi-Layer Neural Networks.html (0.2 KB)
    • 13. Activation Functions.mp4 (22.5 MB)
    • 13. Activation Functions.srt (2.1 KB)
    • 13.1 What is an Activation Function.html (0.1 KB)
    • 14. Activation Functions.html (0.2 KB)
    • 15. Neural Network Example with Number.mp4 (23.5 MB)
    • 15. Neural Network Example with Number.srt (1.7 KB)
    • 16. Neural Networks Example with Number.html (0.2 KB)
    • 17. Classification.html (0.2 KB)
    • 2. Neural Networks.html (0.2 KB)
    • 3. Neural Networks.html (0.2 KB)
    • 4. Neural Networks.html (0.2 KB)
    • 5. Neural Networks.html (0.2 KB)
    • 6. Neural Networks.html (0.2 KB)
    • 7. Neural Networks.html (0.2 KB)
    • 8. Applications of Artificial Neural Networks.mp4 (9.2 MB)
    • 8. Applications of Artificial Neural Networks.srt (2.2 KB)
    • 8.1 Applications of neural networks.html (0.2 KB)
    • 9. Single-Layer Neural Networks (Perceptron).mp4 (19.2 MB)
    • 9. Single-Layer Neural Networks (Perceptron).srt (1.6 KB)
    • 9.1 What is a Perceptron.html (0.1 KB)
    4. Deep Learning
    • 1. What is Deep Learning.mp4 (40.4 MB)
    • 1. What is Deep Learning.srt (2.7 KB)
    • 1.1 Deep Learning.html (0.2 KB)
    • 10. Convolutional Neural Networks (CNNs).html (0.2 KB)
    • 11. Convolutional Neural Networks (CNNs).html (0.2 KB)
    • 12. Recurrent Neural Networks(RNNs).mp4 (57.5 MB)
    • 12. Recurrent Neural Networks(RNNs).srt (5.8 KB)
    • 12.1 Recurrent Neural Networks.html (0.1 KB)
    • 13. Recurrent Neural Networks(RNNs).html (0.2 KB)
    • 14. Recurrent Neural Networks(RNNs).html (0.2 KB)
    • 15. Recurrent Neural Networks(RNNs).html (0.2 KB)
    • 16. Long Short-Term Memory (LSTM).mp4 (28.5 MB)
    • 16. Long Short-Term Memory (LSTM).srt (4.1 KB)
    • 16.1 Understanding LSTM Networks.html (0.2 KB)
    • 17. Long Short-Term Memory (LSTM).html (0.2 KB)
    • 18. Long Short-Term Memory (LSTM).html (0.2 KB)
    • 19. Long Short-Term Memory (LSTM).html (0.2 KB)
    • 2. What is Deep Learning.html (0.2 KB)
    • 20. Residual Neural Network Learning (ResNets).mp4 (24.5 MB)
    • 20. Residual Neural Network Learning (ResNets).srt (2.5 KB)
    • 21. Residual Neural Network Learning (ResNets).html (0.2 KB)
    • 22. Classification flowers.html (0.2 KB)
    • 23. CNN.html (0.2 KB)
    • 24. Text Classification RNN.html (0.2 KB)
    • 3. What is Deep Learning.html (0.2 KB)
    • 4. Deep Learning Applications.mp4 (42.9 MB)
    • 4. Deep Learning Applications.srt (2.6 KB)
    • 4.1 Deep Learning Methods and Applications.html (0.1 KB)
    • 5. Deep Learning Applications.html (0.2 KB)
    • 6. Deep Learning Algorithms and Architectures.

Code:

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