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文档列表

3. Step 2 - Convolutional Neural Network/8. Step 4 - Full Connection.mp4 203.7 MB
12. The Final Run/1. The Whole Implementation.mp4 200.9 MB
7. Step 6 - Recurrent Neural Network/6. LSTM Practical Intuition.mp4 196.5 MB
6. Step 5 - Implementing the CNN-VAE/7. Implementing the Training operations.mp4 196.1 MB
9. Step 8 - Implementing the MDN-RNN/9. Implementing the Training operations (Part 1).mp4 186.1 MB
9. Step 8 - Implementing the MDN-RNN/10. Implementing the Training operations (Part 2).mp4 170.8 MB
12. The Final Run/3. Installing the required packages.mp4 166.4 MB
1. Introduction/2. Introduction + Course Structure + Demo.mp4 164.4 MB
10. Step 9 - Reinforcement Learning/3. A Pseudo Implementation of Reinforcement Learning for the Full World Model.mp4 161.7 MB
11. Step 10 - Deep NeuroEvolution/4. Genetic Algorithms.mp4 156.4 MB
9. Step 8 - Implementing the MDN-RNN/7. Building the MDN - Getting the Input, Hidden Layer and Output of the MDN.mp4 154.1 MB
11. Step 10 - Deep NeuroEvolution/5. Covariance-Matrix Adaptation Evolution Strategy (CMA-ES).mp4 151.1 MB
11. Step 10 - Deep NeuroEvolution/6. Parameter-Exploring Policy Gradients (PEPG).mp4 150.9 MB
1. Introduction/3. Your Three Best Resources.mp4 150.2 MB
3. Step 2 - Convolutional Neural Network/6. Step 2 - Pooling.mp4 147.0 MB
7. Step 6 - Recurrent Neural Network/5. LSTMs.mp4 143.2 MB
6. Step 5 - Implementing the CNN-VAE/4. Building the Encoder part of the VAE.mp4 140.1 MB
9. Step 8 - Implementing the MDN-RNN/5. Building the RNN - Setting up the Input, Target, and Output of the RNN.mp4 137.5 MB
9. Step 8 - Implementing the MDN-RNN/4. Building the RNN - Creating an LSTM cell with Dropout.mp4 133.3 MB
9. Step 8 - Implementing the MDN-RNN/6. Building the RNN - Getting the Deterministic Output of the RNN.mp4 131.6 MB
12. The Final Run/4. The Final Race Human Intelligence vs. Artificial Intelligence.mp4 131.2 MB
7. Step 6 - Recurrent Neural Network/3. What are Recurrent Neural Networks.mp4 127.0 MB
11. Step 10 - Deep NeuroEvolution/3. Evolution Strategies.mp4 125.2 MB
3. Step 2 - Convolutional Neural Network/10. Softmax & Cross-Entropy.mp4 123.7 MB
2. Step 1 - Artificial Neural Network/6. How do Neural Networks learn.mp4 117.6 MB
7. Step 6 - Recurrent Neural Network/4. The Vanishing Gradient Problem.mp4 116.6 MB
9. Step 8 - Implementing the MDN-RNN/8. Building the MDN - Getting the MDN parameters.mp4 114.8 MB
11. Step 10 - Deep NeuroEvolution/2. Deep NeuroEvolution.mp4 114.1 MB
11. Step 10 - Deep NeuroEvolution/7. OpenAI Evolution Strategy.mp4 113.3 MB
3. Step 2 - Convolutional Neural Network/3. What are Convolutional Neural Networks.mp4 113.2 MB
5. Step 4 - Variational AutoEncoder/2. Introduction to the VAE.mp4 108.7 MB
9. Step 8 - Implementing the MDN-RNN/2. Initializing all the parameters and variables of the MDN-RNN class.mp4 104.3 MB
2. Step 1 - Artificial Neural Network/3. The Neuron.mp4 103.6 MB
3. Step 2 - Convolutional Neural Network/4. Step 1 - The Convolution Operation.mp4 102.7 MB
4. Step 3 - AutoEncoder/3. What are AutoEncoders.mp4 99.2 MB
6. Step 5 - Implementing the CNN-VAE/6. Building the Decoder part of the VAE.mp4 97.4 MB
8. Step 7 - Mixture Density Network/2. Introduction to the MDN-RNN.vtt 87.5 MB
8. Step 7 - Mixture Density Network/2. Introduction to the MDN-RNN.mp4 87.4 MB
2. Step 1 - Artificial Neural Network/5. How do Neural Networks work.mp4 85.9 MB
6. Step 5 - Implementing the CNN-VAE/5. Building the V part of the VAE.mp4 84.2 MB
9. Step 8 - Implementing the MDN-RNN/3. Building the RNN - Gathering the parameters.mp4 80.3 MB
6. Step 5 - Implementing the CNN-VAE/3. Initializing all the parameters and variables of the CNN-VAE class.mp4 75.2 MB
10. Step 9 - Reinforcement Learning/2. What is Reinforcement Learning.mp4 71.9 MB
2. Step 1 - Artificial Neural Network/8. Stochastic Gradient Descent.mp4 70.6 MB
8. Step 7 - Mixture Density Network/3. Mixture Density Networks.mp4 68.5 MB
2. Step 1 - Artificial Neural Network/7. Gradient Descent.mp4 63.6 MB
6. Step 5 - Implementing the CNN-VAE/2. Introduction to Step 5.mp4 61.7 MB
4. Step 3 - AutoEncoder/7. Sparse AutoEncoders.mp4 60.3 MB
9. Step 8 - Implementing the MDN-RNN/3. Building the RNN - Gathering the parameters.vtt 58.0 MB
3. Step 2 - Convolutional Neural Network/5. Step 1 Bis - The ReLU Layer.mp4 56.0 MB
4. Step 3 - AutoEncoder/5. Training an AutoEncoder.mp4 52.8 MB
1. Introduction/1. Updates on Udemy Reviews.mp4 48.3 MB
2. Step 1 - Artificial Neural Network/4. The Activation Function.mp4 47.6 MB
8. Step 7 - Mixture Density Network/4. VAE + MDN-RNN Visualization.mp4 47.5 MB
2. Step 1 - Artificial Neural Network/9. Backpropagation.mp4 45.2 MB
3. Step 2 - Convolutional Neural Network/9. Summary.mp4 31.8 MB
4. Step 3 - AutoEncoder/6. Overcomplete Hidden Layers.mp4 29.4 MB
5. Step 4 - Variational AutoEncoder/4. Reparameterization Trick.mp4 27.7 MB
5. Step 4 - Variational AutoEncoder/3. Variational AutoEncoders.mp4 27.6 MB
4. Step 3 - AutoEncoder/8. Denoising AutoEncoders.mp4 25.3 MB
4. Step 3 - AutoEncoder/9. Contractive AutoEncoders.mp4 21.6 MB
7. Step 6 - Recurrent Neural Network/7. LSTM Variations.mp4 21.1 MB
12. The Final Run/2.1 AI_Masterclass.zip.zip 17.9 MB
4. Step 3 - AutoEncoder/10. Stacked AutoEncoders.mp4 17.2 MB
3. Step 2 - Convolutional Neural Network/2. Plan of Attack.mp4 16.6 MB
4. Step 3 - AutoEncoder/10. Stacked AutoEncoders.vtt 15.1 MB
4. Step 3 - AutoEncoder/11. Deep AutoEncoders.mp4 12.5 MB
2. Step 1 - Artificial Neural Network/2. Plan of Attack.mp4 12.4 MB
4. Step 3 - AutoEncoder/2. Plan of Attack.mp4 12.4 MB
7. Step 6 - Recurrent Neural Network/2. Plan of Attack.mp4 11.0 MB
4. Step 3 - AutoEncoder/4. A Note on Biases.mp4 9.0 MB
3. Step 2 - Convolutional Neural Network/7. Step 3 - Flattening.mp4 8.3 MB
3. Step 2 - Convolutional Neural Network/8. Step 4 - Full Connection.vtt 26.7 kB
12. The Final Run/1. The Whole Implementation.vtt 26.3 kB
7. Step 6 - Recurrent Neural Network/5. LSTMs.vtt 25.7 kB
10. Step 9 - Reinforcement Learning/3. A Pseudo Implementation of Reinforcement Learning for the Full World Model.vtt 25.1 kB
6. Step 5 - Implementing the CNN-VAE/4. Building the Encoder part of the VAE.vtt 24.2 kB
3. Step 2 - Convolutional Neural Network/10. Softmax & Cross-Entropy.vtt 23.6 kB
2. Step 1 - Artificial Neural Network/3. The Neuron.vtt 22.5 kB
3. Step 2 - Convolutional Neural Network/4. Step 1 - The Convolution Operation.vtt 22.0 kB
7. Step 6 - Recurrent Neural Network/3. What are Recurrent Neural Networks.vtt 21.9 kB
6. Step 5 - Implementing the CNN-VAE/7. Implementing the Training operations.vtt 21.7 kB
1. Introduction/2. Introduction + Course Structure + Demo.vtt 20.4 kB
3. Step 2 - Convolutional Neural Network/3. What are Convolutional Neural Networks.vtt 20.3 kB
9. Step 8 - Implementing the MDN-RNN/4. Building the RNN - Creating an LSTM cell with Dropout.vtt 20.2 kB
3. Step 2 - Convolutional Neural Network/6. Step 2 - Pooling.vtt 19.6 kB
7. Step 6 - Recurrent Neural Network/6. LSTM Practical Intuition.vtt 19.0 kB
7. Step 6 - Recurrent Neural Network/4. The Vanishing Gradient Problem.vtt 19.0 kB
9. Step 8 - Implementing the MDN-RNN/9. Implementing the Training operations (Part 1).vtt 18.7 kB
9. Step 8 - Implementing the MDN-RNN/5. Building the RNN - Setting up the Input, Target, and Output of the RNN.vtt 18.3 kB
2. Step 1 - Artificial Neural Network/5. How do Neural Networks work.vtt 17.7 kB
9. Step 8 - Implementing the MDN-RNN/10. Implementing the Training operations (Part 2).vtt 17.6 kB
2. Step 1 - Artificial Neural Network/6. How do Neural Networks learn.vtt 17.4 kB
9. Step 8 - Implementing the MDN-RNN/2. Initializing all the parameters and variables of the MDN-RNN class.vtt 16.8 kB
10. Step 9 - Reinforcement Learning/2. What is Reinforcement Learning.vtt 16.5 kB
11. Step 10 - Deep NeuroEvolution/5. Covariance-Matrix Adaptation Evolution Strategy (CMA-ES).vtt 16.1 kB
6. Step 5 - Implementing the CNN-VAE/3. Initializing all the parameters and variables of the CNN-VAE class.vtt 15.8 kB
11. Step 10 - Deep NeuroEvolution/4. Genetic Algorithms.vtt 15.8 kB
12. The Final Run/3. Installing the required packages.vtt 15.4 kB
11. Step 10 - Deep NeuroEvolution/6. Parameter-Exploring Policy Gradients (PEPG).vtt 15.4 kB
9. Step 8 - Implementing the MDN-RNN/6. Building the RNN - Getting the Deterministic Output of the RNN.vtt 15.4 kB
9. Step 8 - Implementing the MDN-RNN/7. Building the MDN - Getting the Input, Hidden Layer and Output of the MDN.vtt 15.4 kB
4. Step 3 - AutoEncoder/3. What are AutoEncoders.vtt 14.9 kB
12. The Final Run/4. The Final Race Human Intelligence vs. Artificial Intelligence.vtt 14.4 kB
11. Step 10 - Deep NeuroEvolution/2. Deep NeuroEvolution.vtt 14.2 kB
9. Step 8 - Implementing the MDN-RNN/8. Building the MDN - Getting the MDN parameters.vtt 13.5 kB
2. Step 1 - Artificial Neural Network/7. Gradient Descent.vtt 13.0 kB
6. Step 5 - Implementing the CNN-VAE/5. Building the V part of the VAE.vtt 12.7 kB
8. Step 7 - Mixture Density Network/3. Mixture Density Networks.vtt 12.7 kB
1. Introduction/3. Your Three Best Resources.vtt 12.5 kB
6. Step 5 - Implementing the CNN-VAE/6. Building the Decoder part of the VAE.vtt 12.2 kB
11. Step 10 - Deep NeuroEvolution/3. Evolution Strategies.vtt 12.2 kB
2. Step 1 - Artificial Neural Network/8. Stochastic Gradient Descent.vtt 11.5 kB
9. Step 8 - Implementing the MDN-RNN/11. Full Code Section.html 11.1 kB
2. Step 1 - Artificial Neural Network/4. The Activation Function.vtt 10.8 kB
5. Step 4 - Variational AutoEncoder/2. Introduction to the VAE.vtt 10.2 kB
6. Step 5 - Implementing the CNN-VAE/2. Introduction to Step 5.vtt 10.0 kB
11. Step 10 - Deep NeuroEvolution/7. OpenAI Evolution Strategy.vtt 10.0 kB
4. Step 3 - AutoEncoder/5. Training an AutoEncoder.vtt 8.9 kB
3. Step 2 - Convolutional Neural Network/5. Step 1 Bis - The ReLU Layer.vtt 8.7 kB
4. Step 3 - AutoEncoder/7. Sparse AutoEncoders.vtt 8.4 kB
6. Step 5 - Implementing the CNN-VAE/9. The Keras Implementation.html 7.9 kB
8. Step 7 - Mixture Density Network/4. VAE + MDN-RNN Visualization.vtt 7.1 kB
2. Step 1 - Artificial Neural Network/9. Backpropagation.vtt 6.9 kB
5. Step 4 - Variational AutoEncoder/4. Reparameterization Trick.vtt 6.3 kB
5. Step 4 - Variational AutoEncoder/3. Variational AutoEncoders.vtt 5.9 kB
3. Step 2 - Convolutional Neural Network/9. Summary.vtt 5.8 kB
9. Step 8 - Implementing the MDN-RNN/12. The Keras Implementation.html 5.4 kB
4. Step 3 - AutoEncoder/6. Overcomplete Hidden Layers.vtt 5.1 kB
3. Step 2 - Convolutional Neural Network/2. Plan of Attack.vtt 4.9 kB
7. Step 6 - Recurrent Neural Network/7. LSTM Variations.vtt 4.5 kB
6. Step 5 - Implementing the CNN-VAE/8. Full Code Section.html 4.1 kB
2. Step 1 - Artificial Neural Network/2. Plan of Attack.vtt 3.6 kB
4. Step 3 - AutoEncoder/9. Contractive AutoEncoders.vtt 3.4 kB
4. Step 3 - AutoEncoder/8. Denoising AutoEncoders.vtt 3.3 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 3.1 kB
4. Step 3 - AutoEncoder/2. Plan of Attack.vtt 2.9 kB
9. Step 8 - Implementing the MDN-RNN/1. Welcome to Step 8 - Implementing the MDN-RNN.html 2.9 kB
4. Step 3 - AutoEncoder/11. Deep AutoEncoders.vtt 2.5 kB
3. Step 2 - Convolutional Neural Network/7. Step 3 - Flattening.vtt 2.4 kB
6. Step 5 - Implementing the CNN-VAE/1. Welcome to Step 5 - Implementing the CNN-VAE.html 2.4 kB
4. Step 3 - AutoEncoder/4. A Note on Biases.vtt 2.0 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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