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最近下载：2025-05-16
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文档列表

12. The Final Run/1. The Whole Implementation.mp4 286.9 MB
1. Introduction/2. Introduction + Course Structure + Demo.mp4 204.8 MB
3. Step 2 - Convolutional Neural Network/8. Step 4 - Full Connection.mp4 203.7 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
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
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
1. Introduction/4. Your Three Best Resources.mp4 141.0 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
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.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
5. Step 4 - Variational AutoEncoder/2. Introduction to the VAE.mp4 76.3 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 60.2 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 52.7 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
12. The Final Run/5. THANK YOU bonus video.mp4 30.6 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
1. Introduction/1. Updates on Udemy Reviews.mp4 23.1 MB
3. Step 2 - Convolutional Neural Network/2. Plan of Attack.mp4 22.9 MB
4. Step 3 - AutoEncoder/9. Contractive AutoEncoders.mp4 21.5 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
2. Step 1 - Artificial Neural Network/2. Plan of Attack.mp4 16.6 MB
4. Step 3 - AutoEncoder/2. Plan of Attack.mp4 16.6 MB
4. Step 3 - AutoEncoder/11. Deep AutoEncoders.mp4 12.5 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.srt 29.2 kB
12. The Final Run/1. The Whole Implementation.srt 29.0 kB
7. Step 6 - Recurrent Neural Network/5. LSTMs.srt 28.9 kB
10. Step 9 - Reinforcement Learning/3. A Pseudo Implementation of Reinforcement Learning for the Full World Model.srt 27.6 kB
6. Step 5 - Implementing the CNN-VAE/4. Building the Encoder part of the VAE.srt 26.8 kB
3. Step 2 - Convolutional Neural Network/10. Softmax & Cross-Entropy.srt 25.9 kB
3. Step 2 - Convolutional Neural Network/8. Step 4 - Full Connection.vtt 25.6 kB
12. The Final Run/1. The Whole Implementation.vtt 25.5 kB
2. Step 1 - Artificial Neural Network/3. The Neuron.srt 25.2 kB
7. Step 6 - Recurrent Neural Network/5. LSTMs.vtt 25.2 kB
7. Step 6 - Recurrent Neural Network/3. What are Recurrent Neural Networks.srt 24.4 kB
10. Step 9 - Reinforcement Learning/3. A Pseudo Implementation of Reinforcement Learning for the Full World Model.vtt 24.1 kB
6. Step 5 - Implementing the CNN-VAE/7. Implementing the Training operations.srt 24.0 kB
3. Step 2 - Convolutional Neural Network/4. Step 1 - The Convolution Operation.srt 23.8 kB
6. Step 5 - Implementing the CNN-VAE/4. Building the Encoder part of the VAE.vtt 23.3 kB
3. Step 2 - Convolutional Neural Network/3. What are Convolutional Neural Networks.srt 22.7 kB
3. Step 2 - Convolutional Neural Network/10. Softmax & Cross-Entropy.vtt 22.7 kB
1. Introduction/2. Introduction + Course Structure + Demo.srt 22.5 kB
9. Step 8 - Implementing the MDN-RNN/4. Building the RNN - Creating an LSTM cell with Dropout.srt 22.4 kB
2. Step 1 - Artificial Neural Network/3. The Neuron.vtt 22.1 kB
3. Step 2 - Convolutional Neural Network/6. Step 2 - Pooling.srt 21.5 kB
7. Step 6 - Recurrent Neural Network/6. LSTM Practical Intuition.srt 21.5 kB
7. Step 6 - Recurrent Neural Network/3. What are Recurrent Neural Networks.vtt 21.3 kB
7. Step 6 - Recurrent Neural Network/4. The Vanishing Gradient Problem.srt 21.3 kB
9. Step 8 - Implementing the MDN-RNN/9. Implementing the Training operations (Part 1).srt 20.9 kB
3. Step 2 - Convolutional Neural Network/4. Step 1 - The Convolution Operation.vtt 20.9 kB
6. Step 5 - Implementing the CNN-VAE/7. Implementing the Training operations.vtt 20.9 kB
9. Step 8 - Implementing the MDN-RNN/5. Building the RNN - Setting up the Input, Target, and Output of the RNN.srt 20.5 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 19.7 kB
1. Introduction/2. Introduction + Course Structure + Demo.vtt 19.6 kB
2. Step 1 - Artificial Neural Network/5. How do Neural Networks work.srt 19.5 kB
2. Step 1 - Artificial Neural Network/6. How do Neural Networks learn.srt 19.4 kB
9. Step 8 - Implementing the MDN-RNN/10. Implementing the Training operations (Part 2).srt 19.4 kB
3. Step 2 - Convolutional Neural Network/6. Step 2 - Pooling.vtt 18.8 kB
7. Step 6 - Recurrent Neural Network/6. LSTM Practical Intuition.vtt 18.8 kB
7. Step 6 - Recurrent Neural Network/4. The Vanishing Gradient Problem.vtt 18.7 kB
10. Step 9 - Reinforcement Learning/2. What is Reinforcement Learning.srt 18.6 kB
9. Step 8 - Implementing the MDN-RNN/2. Initializing all the parameters and variables of the MDN-RNN class.srt 18.4 kB
9. Step 8 - Implementing the MDN-RNN/9. Implementing the Training operations (Part 1).vtt 18.2 kB
9. Step 8 - Implementing the MDN-RNN/5. Building the RNN - Setting up the Input, Target, and Output of the RNN.vtt 18.1 kB
11. Step 10 - Deep NeuroEvolution/4. Genetic Algorithms.srt 18.1 kB
12. The Final Run/3. Installing the required packages.srt 17.9 kB
11. Step 10 - Deep NeuroEvolution/5. Covariance-Matrix Adaptation Evolution Strategy (CMA-ES).srt 17.6 kB
6. Step 5 - Implementing the CNN-VAE/3. Initializing all the parameters and variables of the CNN-VAE class.srt 17.4 kB
2. Step 1 - Artificial Neural Network/5. How do Neural Networks work.vtt 17.2 kB
9. Step 8 - Implementing the MDN-RNN/7. Building the MDN - Getting the Input, Hidden Layer and Output of the MDN.srt 16.9 kB
2. Step 1 - Artificial Neural Network/6. How do Neural Networks learn.vtt 16.9 kB
9. Step 8 - Implementing the MDN-RNN/10. Implementing the Training operations (Part 2).vtt 16.8 kB
11. Step 10 - Deep NeuroEvolution/6. Parameter-Exploring Policy Gradients (PEPG).srt 16.8 kB
9. Step 8 - Implementing the MDN-RNN/6. Building the RNN - Getting the Deterministic Output of the RNN.srt 16.8 kB
4. Step 3 - AutoEncoder/3. What are AutoEncoders.srt 16.7 kB
10. Step 9 - Reinforcement Learning/2. What is Reinforcement Learning.vtt 16.4 kB
12. The Final Run/4. The Final Race Human Intelligence vs. Artificial Intelligence.srt 16.2 kB
9. Step 8 - Implementing the MDN-RNN/2. Initializing all the parameters and variables of the MDN-RNN class.vtt 16.2 kB
11. Step 10 - Deep NeuroEvolution/4. Genetic Algorithms.vtt 15.8 kB
11. Step 10 - Deep NeuroEvolution/5. Covariance-Matrix Adaptation Evolution Strategy (CMA-ES).vtt 15.5 kB
11. Step 10 - Deep NeuroEvolution/2. Deep NeuroEvolution.srt 15.4 kB
12. The Final Run/3. Installing the required packages.vtt 15.3 kB
6. Step 5 - Implementing the CNN-VAE/3. Initializing all the parameters and variables of the CNN-VAE class.vtt 15.2 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
11. Step 10 - Deep NeuroEvolution/6. Parameter-Exploring Policy Gradients (PEPG).vtt 14.9 kB
9. Step 8 - Implementing the MDN-RNN/8. Building the MDN - Getting the MDN parameters.srt 14.9 kB
9. Step 8 - Implementing the MDN-RNN/6. Building the RNN - Getting the Deterministic Output of the RNN.vtt 14.7 kB
4. Step 3 - AutoEncoder/3. What are AutoEncoders.vtt 14.7 kB
2. Step 1 - Artificial Neural Network/7. Gradient Descent.srt 14.5 kB
8. Step 7 - Mixture Density Network/3. Mixture Density Networks.srt 13.9 kB
12. The Final Run/4. The Final Race Human Intelligence vs. Artificial Intelligence.vtt 13.8 kB
6. Step 5 - Implementing the CNN-VAE/5. Building the V part of the VAE.srt 13.8 kB
11. Step 10 - Deep NeuroEvolution/2. Deep NeuroEvolution.vtt 13.7 kB
1. Introduction/4. Your Three Best Resources.srt 13.6 kB
6. Step 5 - Implementing the CNN-VAE/6. Building the Decoder part of the VAE.srt 13.4 kB
11. Step 10 - Deep NeuroEvolution/3. Evolution Strategies.srt 13.3 kB
9. Step 8 - Implementing the MDN-RNN/3. Building the RNN - Gathering the parameters.srt 13.2 kB
9. Step 8 - Implementing the MDN-RNN/8. Building the MDN - Getting the MDN parameters.vtt 13.1 kB
8. Step 7 - Mixture Density Network/2. Introduction to the MDN-RNN.srt 13.0 kB
2. Step 1 - Artificial Neural Network/7. Gradient Descent.vtt 12.6 kB
2. Step 1 - Artificial Neural Network/8. Stochastic Gradient Descent.srt 12.5 kB
8. Step 7 - Mixture Density Network/3. Mixture Density Networks.vtt 12.2 kB
1. Introduction/4. Your Three Best Resources.vtt 12.1 kB
2. Step 1 - Artificial Neural Network/4. The Activation Function.srt 12.1 kB
6. Step 5 - Implementing the CNN-VAE/5. Building the V part of the VAE.vtt 12.1 kB
6. Step 5 - Implementing the CNN-VAE/6. Building the Decoder part of the VAE.vtt 11.7 kB
11. Step 10 - Deep NeuroEvolution/3. Evolution Strategies.vtt 11.7 kB
9. Step 8 - Implementing the MDN-RNN/3. Building the RNN - Gathering the parameters.vtt 11.6 kB
8. Step 7 - Mixture Density Network/2. Introduction to the MDN-RNN.vtt 11.5 kB
5. Step 4 - Variational AutoEncoder/2. Introduction to the VAE.srt 11.3 kB
9. Step 8 - Implementing the MDN-RNN/11. Full Code Section.html 11.1 kB
2. Step 1 - Artificial Neural Network/8. Stochastic Gradient Descent.vtt 11.0 kB
6. Step 5 - Implementing the CNN-VAE/2. Introduction to Step 5.srt 11.0 kB
2. Step 1 - Artificial Neural Network/4. The Activation Function.vtt 10.7 kB
11. Step 10 - Deep NeuroEvolution/7. OpenAI Evolution Strategy.srt 10.6 kB
5. Step 4 - Variational AutoEncoder/2. Introduction to the VAE.vtt 9.9 kB
4. Step 3 - AutoEncoder/5. Training an AutoEncoder.srt 9.8 kB
6. Step 5 - Implementing the CNN-VAE/2. Introduction to Step 5.vtt 9.6 kB
3. Step 2 - Convolutional Neural Network/5. Step 1 Bis - The ReLU Layer.srt 9.5 kB
11. Step 10 - Deep NeuroEvolution/7. OpenAI Evolution Strategy.vtt 9.4 kB
4. Step 3 - AutoEncoder/7. Sparse AutoEncoders.srt 9.0 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 8.4 kB
4. Step 3 - AutoEncoder/7. Sparse AutoEncoders.vtt 8.0 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.srt 7.7 kB
2. Step 1 - Artificial Neural Network/9. Backpropagation.srt 7.5 kB
8. Step 7 - Mixture Density Network/4. VAE + MDN-RNN Visualization.vtt 6.8 kB
5. Step 4 - Variational AutoEncoder/4. Reparameterization Trick.srt 6.7 kB
2. Step 1 - Artificial Neural Network/9. Backpropagation.vtt 6.6 kB
5. Step 4 - Variational AutoEncoder/3. Variational AutoEncoders.srt 6.3 kB
3. Step 2 - Convolutional Neural Network/9. Summary.srt 6.2 kB
5. Step 4 - Variational AutoEncoder/4. Reparameterization Trick.vtt 5.9 kB
4. Step 3 - AutoEncoder/6. Overcomplete Hidden Layers.srt 5.8 kB
5. Step 4 - Variational AutoEncoder/3. Variational AutoEncoders.vtt 5.6 kB
3. Step 2 - Convolutional Neural Network/9. Summary.vtt 5.5 kB
3. Step 2 - Convolutional Neural Network/2. Plan of Attack.srt 5.5 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
7. Step 6 - Recurrent Neural Network/7. LSTM Variations.srt 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 4.1 kB
2. Step 1 - Artificial Neural Network/2. Plan of Attack.srt 4.0 kB
4. Step 3 - AutoEncoder/8. Denoising AutoEncoders.srt 3.7 kB
4. Step 3 - AutoEncoder/9. Contractive AutoEncoders.srt 3.7 kB
2. Step 1 - Artificial Neural Network/2. Plan of Attack.vtt 3.6 kB
1. Introduction/1. Updates on Udemy Reviews.srt 3.6 kB
7. Step 6 - Recurrent Neural Network/2. Plan of Attack.srt 3.5 kB
4. Step 3 - AutoEncoder/8. Denoising AutoEncoders.vtt 3.3 kB
4. Step 3 - AutoEncoder/2. Plan of Attack.srt 3.3 kB
4. Step 3 - AutoEncoder/9. Contractive 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
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.srt 2.8 kB
3. Step 2 - Convolutional Neural Network/7. Step 3 - Flattening.srt 2.6 kB
4. Step 3 - AutoEncoder/10. Stacked AutoEncoders.srt 2.5 kB
4. Step 3 - AutoEncoder/11. Deep AutoEncoders.vtt 2.5 kB
1. Introduction/3. BONUS Learning Paths.html 2.4 kB
12. The Final Run/5. THANK YOU bonus video.srt 2.4 kB
6. Step 5 - Implementing the CNN-VAE/1. Welcome to Step 5 - Implementing the CNN-VAE.html 2.4 kB
3. Step 2 - Convolutional Neural Network/7. Step 3 - Flattening.vtt 2.3 kB
4. Step 3 - AutoEncoder/10. Stacked AutoEncoders.vtt 2.2 kB
4. Step 3 - AutoEncoder/4. A Note on Biases.srt 2.1 kB
12. The Final Run/5. THANK YOU bonus video.vtt 2.1 kB
4. Step 3 - AutoEncoder/4. A Note on Biases.vtt 1.8 kB
11. Step 10 - Deep NeuroEvolution/1. Welcome to Step 10 - Deep NeuroEvolution.html 1.2 kB
13. Bonus Lectures/1. YOUR SPECIAL BONUS.html 1.1 kB
12. The Final Run/2. Download the whole AI Masterclass folder here.html 1.0 kB
1. Introduction/5. Download the Resources here.html 790 Bytes
1. Introduction/6. 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
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北川ゆい Akira COCOLO Saiko あいだももあさのくるみあまいれもんいしかわ愛里いとうしいなうさみ恭香うちだまひろかぐやひめかとりこのみかないかほくすのき琴美クミコグレースくらもとまい（葉月ありさ）さとみ中村あみしいな純菜しのざきさとみ（三沢亜也）牧本千幸（つかもと友希）眞木ありさデヴィテラパトリックドミニカともさかまいともさか愛なごみもえひなこ

分享BT种子/磁力链接

亲，你知道吗？下载的人越多速度越快，赶快把本页面分享给好友一起下载吧^_^

磁力管家