Udemy - Artificial Intelligence Masterclass

  • Category Other
  • Type Tutorials
  • Language English
  • Total size 4.5 GB
  • Uploaded By CourseClub
  • Downloads 629
  • Last checked 2 hours ago
  • Date uploaded 4 days ago
  • Seeders 21
  • Leechers 17

Infohash : 63DDA960206C06F8E234022D2B8161D58E8602D1

Artificial Intelligence Masterclass

Enter the new era of Hybrid AI Models optimized by Deep NeuroEvolution, with a complete toolkit of ML, DL & AI models

For More Courses Visit: http://desirecourse.com

Files:

[DesireCourse.Com] Udemy - Artificial Intelligence Masterclass 1. Introduction
  • 1. Introduction + Course Structure + Demo.mp4 (156.8 MB)
  • 1. Introduction + Course Structure + Demo.vtt (19.2 KB)
  • 2. Your Three Best Resources.mp4 (143.3 MB)
  • 2. Your Three Best Resources.vtt (11.8 KB)
  • 3. Download the Resources here.html (3.0 KB)
  • 4. Meet your instructors!.html (0.7 KB)
10. Step 9 - Reinforcement Learning
  • 1. Welcome to Step 9 - Reinforcement Learning.html (0.4 KB)
  • 2. What is Reinforcement Learning.mp4 (68.6 MB)
  • 2. What is Reinforcement Learning.vtt (16.0 KB)
  • 3. A Pseudo Implementation of Reinforcement Learning for the Full World Model.mp4 (154.3 MB)
  • 3. A Pseudo Implementation of Reinforcement Learning for the Full World Model.vtt (23.5 KB)
  • 4. Full Code Section.html (0.4 KB)
11. Step 10 - Deep NeuroEvolution
  • 1. Welcome to Step 10 - Deep NeuroEvolution.html (1.1 KB)
2. Step 1 - Artificial Neural Network
  • 1. Welcome to Step 1 - Artificial Neural Network.html (0.6 KB)
  • 2. Plan of Attack.mp4 (11.9 MB)
  • 2. Plan of Attack.vtt (3.5 KB)
  • 3. The Neuron.mp4 (98.8 MB)
  • 3. The Neuron.vtt (21.6 KB)
  • 4. The Activation Function.mp4 (45.4 MB)
  • 4. The Activation Function.vtt (10.4 KB)
  • 5. How do Neural Networks work.mp4 (81.9 MB)
  • 5. How do Neural Networks work.vtt (16.8 KB)
  • 6. How do Neural Networks learn.mp4 (112.1 MB)
  • 6. How do Neural Networks learn.vtt (16.5 KB)
  • 7. Gradient Descent.mp4 (60.6 MB)
  • 7. Gradient Descent.vtt (12.3 KB)
  • 8. Stochastic Gradient Descent.mp4 (67.3 MB)
  • 8. Stochastic Gradient Descent.vtt (10.8 KB)
  • 9. Backpropagation.mp4 (43.1 MB)
  • 9. Backpropagation.vtt (6.4 KB)
3. Step 2 - Convolutional Neural Network
  • 1. Welcome to Step 2 - Convolutional Neural Network.html (0.4 KB)
  • 10. Softmax & Cross-Entropy.mp4 (118.0 MB)
  • 10. Softmax & Cross-Entropy.vtt (22.1 KB)
  • 2. Plan of Attack.mp4 (15.8 MB)
  • 2. Plan of Attack.vtt (4.7 KB)
  • 3. What are Convolutional Neural Networks.mp4 (108.0 MB)
  • 3. What are Convolutional Neural Networks.vtt (19.4 KB)
  • 4. Step 1 - The Convolution Operation.mp4 (97.9 MB)
  • 4. Step 1 - The Convolution Operation.vtt (20.4 KB)
  • 5. Step 1 Bis - The ReLU Layer.mp4 (53.4 MB)
  • 5. Step 1 Bis - The ReLU Layer.vtt (8.2 KB)
  • 6. Step 2 - Pooling.mp4 (140.2 MB)
  • 6. Step 2 - Pooling.vtt (18.4 KB)
  • 7. Step 3 - Flattening.mp4 (7.9 MB)
  • 7. Step 3 - Flattening.vtt (2.3 KB)
  • 8. Step 4 - Full Connection.mp4 (194.3 MB)
  • 8. Step 4 - Full Connection.vtt (25.0 KB)
  • 9. Summary.mp4 (30.3 MB)
  • 9. Summary.vtt (5.4 KB)
4. Step 3 - AutoEncoder
  • 1. Welcome to Step 3 - AutoEncoder.html (0.4 KB)
  • 10. Stacked AutoEncoders.mp4 (16.4 MB)
  • 10. Stacked AutoEncoders.vtt (2.1 KB)
  • 11. Deep AutoEncoders.mp4 (12.0 MB)
  • 11. Deep AutoEncoders.vtt (2.4 KB)
  • 2. Plan of Attack.mp4 (11.8 MB)
  • 2. Plan of Attack.vtt (2.9 KB)
  • 3. What are AutoEncoders.mp4 (94.6 MB)
  • 3. What are AutoEncoders.vtt (14.3 KB)
  • 4. A Note on Biases.mp4 (8.6 MB)
  • 4. A Note on Biases.vtt (1.8 KB)
  • 5. Training an AutoEncoder.mp4 (50.3 MB)
  • 5. Training an AutoEncoder.vtt (8.4 KB)
  • 6. Overcomplete Hidden Layers.mp4 (28.1 MB)
  • 6. Overcomplete Hidden Layers.vtt (5.0 KB)
  • 7. Sparse AutoEncoders.mp4 (57.5 MB)
  • 7. Sparse AutoEncoders.vtt (7.8 KB)
  • 8. Denoising AutoEncoders.mp4 (24.1 MB)
  • 8. Denoising AutoEncoders.vtt (3.2 KB)
  • 9. Contractive AutoEncoders.mp4 (20.6 MB)
  • 9. Contractive AutoEncoders.vtt (3.1 KB)
5. Step 4 - Variational AutoEncoder
  • 1. Welcome to Step 4 - Variational AutoEncoder.html (0.4 KB)
  • 2. Introduction to the VAE.mp4 (103.7 MB)
  • 2. Introduction to the VAE.vtt (9.7 KB)
  • 3. Variational AutoEncoders.mp4 (26.3 MB)
  • 3. Variational AutoEncoders.vtt (5.4 KB)
  • 4. Reparameterization Trick.mp4 (26.4 MB)
  • 4. Reparameterization Trick.vtt (5.8 KB)
6. Step 5 - Implementing the CNN-VAE
  • 1. Welcome to Step 5 - Implementing the CNN-VAE.html (2.3 KB)
  • 2. Introduction to Step 5.mp4 (58.9 MB)
  • 2. Introduction to Step 5.vtt (9.4 KB)
  • 3. Initializing all the parameters and variables of the CNN-VAE class.mp4 (71.7 MB)
  • 3. Initializing all the parameters and variables of the CNN-VAE class.vtt (14.9 KB)
  • 4. Building the Encoder part of the VAE.mp4 (133.7 MB)
  • 4. Building the Encoder part of the VAE.vtt (22.8 KB)
  • 5. Building the V part of the VAE.mp4 (80.3 MB)
  • 5. Building the V part of the VAE.vtt (11.8 KB)
  • 6. Building the Decoder part of the VAE.mp4 (92.9 MB)
  • 6. Building the Decoder part of the VAE.vtt (11.4 KB)
  • 7. Implementing the Training operations.mp4 (187.0 MB)
  • 7. Implementing the Training operations.vtt (20.4 KB)
  • 8. Full Code Section.html (4.0 KB)
7. Step 6 - Recurrent Neural Network
  • 1. Welcome to Step 6 - Recurrent Neural Network.html (0.5 KB)
  • 2. Plan of Attack.mp4 (10.5 MB)
  • 2. Plan of Attack.vtt (3.1 KB)
  • 3. What are Recurrent Neural Networks.mp4 (121.1 MB)
  • 3. What are Recurrent Neural Networks.vtt (20.8 KB)
  • 4. The Vanishing Gradient Problem.mp4 (111.2 MB)
  • 4. The Vanishing Gradient Problem.vtt (18.3 KB)
  • 5. LSTMs.mp4 (136.5 MB)
  • 5. LSTMs.vtt (24.6 KB)
  • 6. LSTM Practical Intuition.mp4 (187.4 MB)
  • 6. LSTM Practical Intuition.vtt (18.4 KB)
  • 7. LSTM Variations.mp4 (20.1 MB)
  • 7. LSTM Variations.vtt (4.3 KB)
8. Step 7 - Mixture Density Network
  • 1. Welcome to Step 7 - Mixture Density Network.html (0.5 KB)
9. Step 8 - Implementing the MDN-RNN
  • 1. Welcome to Step 8 - Implementing the MDN-RNN.html (2.8 KB)
  • 10. Implementing the Training operations (Part 2).mp4 (162.9 MB)
  • 10. Implementing the Training operations (Part 2).vtt (16.4 KB)
  • 11. Full Code Section.html (10.8 KB)
  • 2. Initializing all the parameters and variables of the MDN-RNN class.mp4 (99.5 MB)
  • 2. Initializing all the parameters and variables of the MDN-RNN class.vtt (15.8 KB)
  • 3. Building the RNN - Gathering the parameters.mp4 (76.6 MB)
  • 3. Building the RNN - Gathering the parameters.vtt (11.3 KB)
  • 4. Building the RNN - Creating an LSTM cell with Dropout.mp4 (127.2 MB)
  • 4. Building the RNN - Creating an LSTM cell with Dropout.vtt (19.2 KB)
  • 5. Building the RNN - Setting up the Input, Target, and Output of the RNN.mp4 (131.1 MB)
  • 5. Building the RNN - Setting up the Input, Target, and Output of the RNN.vtt (17.7 KB)
  • 6. Building the RNN - Getting the Deterministic Output of the RNN.mp4 (125.5 MB)
  • 6. Building the RNN - Getting the Deterministic Output of the RNN.vtt (14.4 KB)
  • 7. Building the MDN - Getting the Input, Hidden Layer and Output of the MDN.mp4 (147.0 MB)
  • 7. Building the MDN - Getting the Input, Hidden Layer and Output of the MDN.vtt (14.7 KB)
  • 8. Building the MDN - Getting the MDN parameters.mp4 (109.5 MB)
  • 8. Building the MDN - Getting the MDN parameters.vtt (12.8 KB)
  • 9. Implementing the Training operations (Part 1).mp4 (177.5 MB)
  • 9. Implementing the Training operations (Part 1).vtt (17.8 KB)
  • [DesireCourse.Com].txt (0.7 KB)
  • [DesireCourse.Com].url (0.0 KB)

There are currently no comments. Feel free to leave one :)

Code:

  • udp://62.138.0.158:6969/announce
  • udp://87.233.192.220:6969/announce
  • udp://88.198.231.1:1337/announce
  • udp://151.80.120.113:2710/announce
  • udp://111.6.78.96:6969/announce
  • udp://90.179.64.91:1337/announce
  • udp://51.15.4.13:1337/announce
  • udp://191.96.249.23:6969/announce
  • udp://35.187.36.248:1337/announce
  • udp://123.249.16.65:2710/announce
  • udp://127.0.0.1:6969/announce
  • udp://210.244.71.25:6969/announce
  • udp://78.142.19.42:1337/announce
  • udp://173.254.219.72:6969/announce
  • udp://51.15.76.199:6969/announce
  • udp://91.212.150.191:3418/announce
  • udp://103.224.212.222:6969/announce
  • udp://92.241.171.245:6969/announce
  • udp://51.15.40.114:80/announce
  • udp://37.19.5.139:6969/announce