[FreeCourseSite.com] Udemy - Artificial Intelligence Masterclass

Creation Time	Nov. 1, 2023, 10 a.m.
Last Access Time	March 30, 2025, 3:20 a.m.
File Size	6.2 GB
Keywords	FreeCourseSite Intelligence - Artificial Udemy Masterclass com
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Total Files	158
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3. Step 2 - Convolutional Neural Network/8. Step 4 - Full Connection.mp4 194.3 MB
12. The Final Run/1. The Whole Implementation.mp4 191.6 MB
7. Step 6 - Recurrent Neural Network/6. LSTM Practical Intuition.mp4 187.4 MB
6. Step 5 - Implementing the CNN-VAE/7. Implementing the Training operations.mp4 187.0 MB
9. Step 8 - Implementing the MDN-RNN/9. Implementing the Training operations (Part 1).mp4 177.5 MB
9. Step 8 - Implementing the MDN-RNN/10. Implementing the Training operations (Part 2).mp4 162.9 MB
12. The Final Run/3. Installing the required packages.mp4 158.7 MB
1. Introduction/2. Introduction + Course Structure + Demo.mp4 156.8 MB
10. Step 9 - Reinforcement Learning/3. A Pseudo Implementation of Reinforcement Learning for the Full World Model.mp4 154.3 MB
11. Step 10 - Deep NeuroEvolution/4. Genetic Algorithms.mp4 149.1 MB
9. Step 8 - Implementing the MDN-RNN/7. Building the MDN - Getting the Input, Hidden Layer and Output of the MDN.mp4 147.0 MB
11. Step 10 - Deep NeuroEvolution/5. Covariance-Matrix Adaptation Evolution Strategy (CMA-ES).mp4 144.1 MB
11. Step 10 - Deep NeuroEvolution/6. Parameter-Exploring Policy Gradients (PEPG).mp4 143.9 MB
1. Introduction/3. Your Three Best Resources.mp4 143.3 MB
3. Step 2 - Convolutional Neural Network/6. Step 2 - Pooling.mp4 140.2 MB
7. Step 6 - Recurrent Neural Network/5. LSTMs.mp4 136.5 MB
6. Step 5 - Implementing the CNN-VAE/4. Building the Encoder part of the VAE.mp4 133.7 MB
9. Step 8 - Implementing the MDN-RNN/5. Building the RNN - Setting up the Input, Target, and Output of the RNN.mp4 131.1 MB
9. Step 8 - Implementing the MDN-RNN/4. Building the RNN - Creating an LSTM cell with Dropout.mp4 127.2 MB
9. Step 8 - Implementing the MDN-RNN/6. Building the RNN - Getting the Deterministic Output of the RNN.mp4 125.5 MB
12. The Final Run/4. The Final Race Human Intelligence vs. Artificial Intelligence.mp4 125.1 MB
7. Step 6 - Recurrent Neural Network/3. What are Recurrent Neural Networks.mp4 121.1 MB
11. Step 10 - Deep NeuroEvolution/3. Evolution Strategies.mp4 119.4 MB
3. Step 2 - Convolutional Neural Network/10. Softmax & Cross-Entropy.mp4 118.0 MB
2. Step 1 - Artificial Neural Network/6. How do Neural Networks learn.mp4 112.1 MB
7. Step 6 - Recurrent Neural Network/4. The Vanishing Gradient Problem.mp4 111.2 MB
9. Step 8 - Implementing the MDN-RNN/8. Building the MDN - Getting the MDN parameters.mp4 109.5 MB
11. Step 10 - Deep NeuroEvolution/2. Deep NeuroEvolution.mp4 108.8 MB
11. Step 10 - Deep NeuroEvolution/7. OpenAI Evolution Strategy.mp4 108.1 MB
3. Step 2 - Convolutional Neural Network/3. What are Convolutional Neural Networks.mp4 108.0 MB
5. Step 4 - Variational AutoEncoder/2. Introduction to the VAE.mp4 103.7 MB
9. Step 8 - Implementing the MDN-RNN/2. Initializing all the parameters and variables of the MDN-RNN class.mp4 99.5 MB
2. Step 1 - Artificial Neural Network/3. The Neuron.mp4 98.8 MB
3. Step 2 - Convolutional Neural Network/4. Step 1 - The Convolution Operation.mp4 97.9 MB
4. Step 3 - AutoEncoder/3. What are AutoEncoders.mp4 94.6 MB
6. Step 5 - Implementing the CNN-VAE/6. Building the Decoder part of the VAE.mp4 92.9 MB
8. Step 7 - Mixture Density Network/2. Introduction to the MDN-RNN.vtt 83.4 MB
8. Step 7 - Mixture Density Network/2. Introduction to the MDN-RNN.mp4 83.4 MB
2. Step 1 - Artificial Neural Network/5. How do Neural Networks work.mp4 81.9 MB
6. Step 5 - Implementing the CNN-VAE/5. Building the V part of the VAE.mp4 80.3 MB
9. Step 8 - Implementing the MDN-RNN/3. Building the RNN - Gathering the parameters.mp4 76.6 MB
6. Step 5 - Implementing the CNN-VAE/3. Initializing all the parameters and variables of the CNN-VAE class.mp4 71.7 MB
10. Step 9 - Reinforcement Learning/2. What is Reinforcement Learning.mp4 68.6 MB
2. Step 1 - Artificial Neural Network/8. Stochastic Gradient Descent.mp4 67.3 MB
8. Step 7 - Mixture Density Network/3. Mixture Density Networks.mp4 65.4 MB
2. Step 1 - Artificial Neural Network/7. Gradient Descent.mp4 60.6 MB
6. Step 5 - Implementing the CNN-VAE/2. Introduction to Step 5.mp4 58.9 MB
4. Step 3 - AutoEncoder/7. Sparse AutoEncoders.mp4 57.5 MB
9. Step 8 - Implementing the MDN-RNN/3. Building the RNN - Gathering the parameters.vtt 55.3 MB
3. Step 2 - Convolutional Neural Network/5. Step 1 Bis - The ReLU Layer.mp4 53.4 MB
4. Step 3 - AutoEncoder/5. Training an AutoEncoder.mp4 50.3 MB
1. Introduction/1. Updates on Udemy Reviews.mp4 46.0 MB
2. Step 1 - Artificial Neural Network/4. The Activation Function.mp4 45.4 MB
8. Step 7 - Mixture Density Network/4. VAE + MDN-RNN Visualization.mp4 45.3 MB
2. Step 1 - Artificial Neural Network/9. Backpropagation.mp4 43.1 MB
3. Step 2 - Convolutional Neural Network/9. Summary.mp4 30.3 MB
4. Step 3 - AutoEncoder/6. Overcomplete Hidden Layers.mp4 28.1 MB
5. Step 4 - Variational AutoEncoder/4. Reparameterization Trick.mp4 26.4 MB
5. Step 4 - Variational AutoEncoder/3. Variational AutoEncoders.mp4 26.3 MB
4. Step 3 - AutoEncoder/8. Denoising AutoEncoders.mp4 24.1 MB
4. Step 3 - AutoEncoder/9. Contractive AutoEncoders.mp4 20.6 MB
7. Step 6 - Recurrent Neural Network/7. LSTM Variations.mp4 20.1 MB
12. The Final Run/2.1 AI_Masterclass.zip.zip 17.1 MB
4. Step 3 - AutoEncoder/10. Stacked AutoEncoders.mp4 16.4 MB
3. Step 2 - Convolutional Neural Network/2. Plan of Attack.mp4 15.8 MB
4. Step 3 - AutoEncoder/10. Stacked AutoEncoders.vtt 14.4 MB
4. Step 3 - AutoEncoder/11. Deep AutoEncoders.mp4 12.0 MB
2. Step 1 - Artificial Neural Network/2. Plan of Attack.mp4 11.9 MB
4. Step 3 - AutoEncoder/2. Plan of Attack.mp4 11.8 MB
7. Step 6 - Recurrent Neural Network/2. Plan of Attack.mp4 10.5 MB
4. Step 3 - AutoEncoder/4. A Note on Biases.mp4 8.6 MB
3. Step 2 - Convolutional Neural Network/7. Step 3 - Flattening.mp4 7.9 MB
3. Step 2 - Convolutional Neural Network/8. Step 4 - Full Connection.vtt 26.1 KB
12. The Final Run/1. The Whole Implementation.vtt 25.7 KB
7. Step 6 - Recurrent Neural Network/5. LSTMs.vtt 25.1 KB
10. Step 9 - Reinforcement Learning/3. A Pseudo Implementation of Reinforcement Learning for the Full World Model.vtt 24.5 KB
6. Step 5 - Implementing the CNN-VAE/4. Building the Encoder part of the VAE.vtt 23.6 KB
3. Step 2 - Convolutional Neural Network/10. Softmax & Cross-Entropy.vtt 23.0 KB
2. Step 1 - Artificial Neural Network/3. The Neuron.vtt 22.0 KB
3. Step 2 - Convolutional Neural Network/4. Step 1 - The Convolution Operation.vtt 21.5 KB
7. Step 6 - Recurrent Neural Network/3. What are Recurrent Neural Networks.vtt 21.4 KB
6. Step 5 - Implementing the CNN-VAE/7. Implementing the Training operations.vtt 21.2 KB
1. Introduction/2. Introduction + Course Structure + Demo.vtt 19.9 KB
3. Step 2 - Convolutional Neural Network/3. What are Convolutional Neural Networks.vtt 19.9 KB
9. Step 8 - Implementing the MDN-RNN/4. Building the RNN - Creating an LSTM cell with Dropout.vtt 19.8 KB
3. Step 2 - Convolutional Neural Network/6. Step 2 - Pooling.vtt 19.1 KB
7. Step 6 - Recurrent Neural Network/6. LSTM Practical Intuition.vtt 18.6 KB
7. Step 6 - Recurrent Neural Network/4. The Vanishing Gradient Problem.vtt 18.6 KB
9. Step 8 - Implementing the MDN-RNN/9. Implementing the Training operations (Part 1).vtt 18.3 KB
9. Step 8 - Implementing the MDN-RNN/5. Building the RNN - Setting up the Input, Target, and Output of the RNN.vtt 17.9 KB
2. Step 1 - Artificial Neural Network/5. How do Neural Networks work.vtt 17.3 KB
9. Step 8 - Implementing the MDN-RNN/10. Implementing the Training operations (Part 2).vtt 17.2 KB
2. Step 1 - Artificial Neural Network/6. How do Neural Networks learn.vtt 17.0 KB
9. Step 8 - Implementing the MDN-RNN/2. Initializing all the parameters and variables of the MDN-RNN class.vtt 16.4 KB
10. Step 9 - Reinforcement Learning/2. What is Reinforcement Learning.vtt 16.1 KB
11. Step 10 - Deep NeuroEvolution/5. Covariance-Matrix Adaptation Evolution Strategy (CMA-ES).vtt 15.8 KB
6. Step 5 - Implementing the CNN-VAE/3. Initializing all the parameters and variables of the CNN-VAE class.vtt 15.5 KB
11. Step 10 - Deep NeuroEvolution/4. Genetic Algorithms.vtt 15.4 KB
12. The Final Run/3. Installing the required packages.vtt 15.1 KB
11. Step 10 - Deep NeuroEvolution/6. Parameter-Exploring Policy Gradients (PEPG).vtt 15.1 KB
9. Step 8 - Implementing the MDN-RNN/6. Building the RNN - Getting the Deterministic Output of the RNN.vtt 15.0 KB
9. Step 8 - Implementing the MDN-RNN/7. Building the MDN - Getting the Input, Hidden Layer and Output of the MDN.vtt 15.0 KB
4. Step 3 - AutoEncoder/3. What are AutoEncoders.vtt 14.6 KB
12. The Final Run/4. The Final Race Human Intelligence vs. Artificial Intelligence.vtt 14.0 KB
11. Step 10 - Deep NeuroEvolution/2. Deep NeuroEvolution.vtt 13.8 KB
9. Step 8 - Implementing the MDN-RNN/8. Building the MDN - Getting the MDN parameters.vtt 13.2 KB
2. Step 1 - Artificial Neural Network/7. Gradient Descent.vtt 12.7 KB
6. Step 5 - Implementing the CNN-VAE/5. Building the V part of the VAE.vtt 12.4 KB
8. Step 7 - Mixture Density Network/3. Mixture Density Networks.vtt 12.4 KB
1. Introduction/3. Your Three Best Resources.vtt 12.2 KB
6. Step 5 - Implementing the CNN-VAE/6. Building the Decoder part of the VAE.vtt 11.9 KB
11. Step 10 - Deep NeuroEvolution/3. Evolution Strategies.vtt 11.9 KB
2. Step 1 - Artificial Neural Network/8. Stochastic Gradient Descent.vtt 11.2 KB
9. Step 8 - Implementing the MDN-RNN/11. Full Code Section.html 10.8 KB
2. Step 1 - Artificial Neural Network/4. The Activation Function.vtt 10.5 KB
5. Step 4 - Variational AutoEncoder/2. Introduction to the VAE.vtt 10.0 KB
6. Step 5 - Implementing the CNN-VAE/2. Introduction to Step 5.vtt 9.8 KB
11. Step 10 - Deep NeuroEvolution/7. OpenAI Evolution Strategy.vtt 9.7 KB
4. Step 3 - AutoEncoder/5. Training an AutoEncoder.vtt 8.6 KB
3. Step 2 - Convolutional Neural Network/5. Step 1 Bis - The ReLU Layer.vtt 8.5 KB
4. Step 3 - AutoEncoder/7. Sparse AutoEncoders.vtt 8.2 KB
6. Step 5 - Implementing the CNN-VAE/9. The Keras Implementation.html 7.7 KB
8. Step 7 - Mixture Density Network/4. VAE + MDN-RNN Visualization.vtt 6.9 KB
2. Step 1 - Artificial Neural Network/9. Backpropagation.vtt 6.7 KB
5. Step 4 - Variational AutoEncoder/4. Reparameterization Trick.vtt 6.2 KB
5. Step 4 - Variational AutoEncoder/3. Variational AutoEncoders.vtt 5.7 KB
3. Step 2 - Convolutional Neural Network/9. Summary.vtt 5.7 KB
9. Step 8 - Implementing the MDN-RNN/12. The Keras Implementation.html 5.3 KB
4. Step 3 - AutoEncoder/6. Overcomplete Hidden Layers.vtt 5.0 KB
3. Step 2 - Convolutional Neural Network/2. Plan of Attack.vtt 4.8 KB
7. Step 6 - Recurrent Neural Network/7. LSTM Variations.vtt 4.4 KB
6. Step 5 - Implementing the CNN-VAE/8. Full Code Section.html 4.0 KB
2. Step 1 - Artificial Neural Network/2. Plan of Attack.vtt 3.5 KB
4. Step 3 - AutoEncoder/9. Contractive AutoEncoders.vtt 3.3 KB
4. Step 3 - AutoEncoder/8. Denoising AutoEncoders.vtt 3.2 KB
7. Step 6 - Recurrent Neural Network/2. Plan of Attack.vtt 3.1 KB
1. Introduction/1. Updates on Udemy Reviews.vtt 3.1 KB
1. Introduction/4. Download the Resources here.html 3.0 KB
4. Step 3 - AutoEncoder/2. Plan of Attack.vtt 2.8 KB
9. Step 8 - Implementing the MDN-RNN/1. Welcome to Step 8 - Implementing the MDN-RNN.html 2.8 KB
4. Step 3 - AutoEncoder/11. Deep AutoEncoders.vtt 2.5 KB
3. Step 2 - Convolutional Neural Network/7. Step 3 - Flattening.vtt 2.3 KB
6. Step 5 - Implementing the CNN-VAE/1. Welcome to Step 5 - Implementing the CNN-VAE.html 2.3 KB
4. Step 3 - AutoEncoder/4. A Note on Biases.vtt 1.9 KB
11. Step 10 - Deep NeuroEvolution/1. Welcome to Step 10 - Deep NeuroEvolution.html 1.2 KB
12. The Final Run/2. Download the whole AI Masterclass folder here.html 1.0 KB
1. Introduction/5. Meet your instructors!.html 723 bytes
2. Step 1 - Artificial Neural Network/1. Welcome to Step 1 - Artificial Neural Network.html 605 bytes
8. Step 7 - Mixture Density Network/1. Welcome to Step 7 - Mixture Density Network.html 517 bytes
7. Step 6 - Recurrent Neural Network/1. Welcome to Step 6 - Recurrent Neural Network.html 507 bytes
3. Step 2 - Convolutional Neural Network/1. Welcome to Step 2 - Convolutional Neural Network.html 430 bytes
10. Step 9 - Reinforcement Learning/1. Welcome to Step 9 - Reinforcement Learning.html 424 bytes
5. Step 4 - Variational AutoEncoder/1. Welcome to Step 4 - Variational AutoEncoder.html 423 bytes
4. Step 3 - AutoEncoder/1. Welcome to Step 3 - AutoEncoder.html 418 bytes
10. Step 9 - Reinforcement Learning/4. Full Code Section.html 393 bytes
[FCS Forum].url 133 bytes
[FreeCourseSite.com].url 127 bytes
[CourseClub.NET].url 123 bytes

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