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种子名称： [FreeTutorials.Us] Udemy - Artificial Intelligence Masterclass
文件类型： 视频
文件数目： 69个文件
文件大小： 6.1 GB
收录时间： 2019-7-16 05:48
已经下载： 3次
资源热度： 117
最近下载： 2024-4-26 19:49

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[FreeTutorials.Us] Udemy - Artificial Intelligence Masterclass.torrent

1. Introduction/1. Updates on Udemy Reviews.mp422.03MB

1. Introduction/2. Introduction + Course Structure + Demo.mp4195.34MB

1. Introduction/4. Your Three Best Resources.mp4134.49MB

10. Step 9 - Reinforcement Learning/2. What is Reinforcement Learning.mp468.6MB

10. Step 9 - Reinforcement Learning/3. A Pseudo Implementation of Reinforcement Learning for the Full World Model.mp4154.25MB

11. Step 10 - Deep NeuroEvolution/2. Deep NeuroEvolution.mp4108.84MB

11. Step 10 - Deep NeuroEvolution/3. Evolution Strategies.mp4119.44MB

11. Step 10 - Deep NeuroEvolution/4. Genetic Algorithms.mp4149.11MB

11. Step 10 - Deep NeuroEvolution/5. Covariance-Matrix Adaptation Evolution Strategy (CMA-ES).mp4144.06MB

11. Step 10 - Deep NeuroEvolution/6. Parameter-Exploring Policy Gradients (PEPG).mp4143.9MB

11. Step 10 - Deep NeuroEvolution/7. OpenAI Evolution Strategy.mp4108.09MB

12. The Final Run/1. The Whole Implementation.mp4273.65MB

12. The Final Run/3. Installing the required packages.mp4158.71MB

12. The Final Run/4. The Final Race Human Intelligence vs. Artificial Intelligence.mp4125.09MB

12. The Final Run/5. THANK YOU bonus video.mp429.21MB

2. Step 1 - Artificial Neural Network/2. Plan of Attack.mp415.85MB

2. Step 1 - Artificial Neural Network/3. The Neuron.mp498.79MB

2. Step 1 - Artificial Neural Network/4. The Activation Function.mp445.36MB

2. Step 1 - Artificial Neural Network/5. How do Neural Networks work.mp481.94MB

2. Step 1 - Artificial Neural Network/6. How do Neural Networks learn.mp4112.11MB

2. Step 1 - Artificial Neural Network/7. Gradient Descent.mp460.62MB

2. Step 1 - Artificial Neural Network/8. Stochastic Gradient Descent.mp467.29MB

2. Step 1 - Artificial Neural Network/9. Backpropagation.mp443.14MB

3. Step 2 - Convolutional Neural Network/10. Softmax & Cross-Entropy.mp4117.97MB

3. Step 2 - Convolutional Neural Network/2. Plan of Attack.mp421.81MB

3. Step 2 - Convolutional Neural Network/3. What are Convolutional Neural Networks.mp4107.97MB

3. Step 2 - Convolutional Neural Network/4. Step 1 - The Convolution Operation.mp497.93MB

3. Step 2 - Convolutional Neural Network/5. Step 1 Bis - The ReLU Layer.mp453.44MB

3. Step 2 - Convolutional Neural Network/6. Step 2 - Pooling.mp4140.17MB

3. Step 2 - Convolutional Neural Network/7. Step 3 - Flattening.mp47.94MB

3. Step 2 - Convolutional Neural Network/8. Step 4 - Full Connection.mp4194.26MB

3. Step 2 - Convolutional Neural Network/9. Summary.mp430.33MB

4. Step 3 - AutoEncoder/10. Stacked AutoEncoders.mp416.44MB

4. Step 3 - AutoEncoder/11. Deep AutoEncoders.mp411.97MB

4. Step 3 - AutoEncoder/2. Plan of Attack.mp415.85MB

4. Step 3 - AutoEncoder/3. What are AutoEncoders.mp494.61MB

4. Step 3 - AutoEncoder/4. A Note on Biases.mp48.61MB

4. Step 3 - AutoEncoder/5. Training an AutoEncoder.mp450.3MB

4. Step 3 - AutoEncoder/6. Overcomplete Hidden Layers.mp428.06MB

4. Step 3 - AutoEncoder/7. Sparse AutoEncoders.mp457.45MB

4. Step 3 - AutoEncoder/8. Denoising AutoEncoders.mp424.1MB

4. Step 3 - AutoEncoder/9. Contractive AutoEncoders.mp420.55MB

5. Step 4 - Variational AutoEncoder/2. Introduction to the VAE.mp472.81MB

5. Step 4 - Variational AutoEncoder/3. Variational AutoEncoders.mp426.31MB

5. Step 4 - Variational AutoEncoder/4. Reparameterization Trick.mp426.41MB

6. Step 5 - Implementing the CNN-VAE/2. Introduction to Step 5.mp458.85MB

6. Step 5 - Implementing the CNN-VAE/3. Initializing all the parameters and variables of the CNN-VAE class.mp471.72MB

6. Step 5 - Implementing the CNN-VAE/4. Building the Encoder part of the VAE.mp4133.64MB

6. Step 5 - Implementing the CNN-VAE/5. Building the V part of the VAE.mp480.33MB

6. Step 5 - Implementing the CNN-VAE/6. Building the Decoder part of the VAE.mp492.89MB

6. Step 5 - Implementing the CNN-VAE/7. Implementing the Training operations.mp4186.98MB

7. Step 6 - Recurrent Neural Network/2. Plan of Attack.mp410.5MB

7. Step 6 - Recurrent Neural Network/3. What are Recurrent Neural Networks.mp4121.09MB

7. Step 6 - Recurrent Neural Network/4. The Vanishing Gradient Problem.mp4111.17MB

7. Step 6 - Recurrent Neural Network/5. LSTMs.mp4136.52MB

7. Step 6 - Recurrent Neural Network/6. LSTM Practical Intuition.mp4187.41MB

7. Step 6 - Recurrent Neural Network/7. LSTM Variations.mp420.12MB

8. Step 7 - Mixture Density Network/2. Introduction to the MDN-RNN.mp483.39MB

8. Step 7 - Mixture Density Network/3. Mixture Density Networks.mp465.35MB

8. Step 7 - Mixture Density Network/4. VAE + MDN-RNN Visualization.mp445.3MB

9. Step 8 - Implementing the MDN-RNN/10. Implementing the Training operations (Part 2).mp4162.89MB

9. Step 8 - Implementing the MDN-RNN/2. Initializing all the parameters and variables of the MDN-RNN class.mp499.49MB

9. Step 8 - Implementing the MDN-RNN/3. Building the RNN - Gathering the parameters.mp476.58MB

9. Step 8 - Implementing the MDN-RNN/4. Building the RNN - Creating an LSTM cell with Dropout.mp4127.16MB

9. Step 8 - Implementing the MDN-RNN/5. Building the RNN - Setting up the Input, Target, and Output of the RNN.mp4131.12MB

9. Step 8 - Implementing the MDN-RNN/6. Building the RNN - Getting the Deterministic Output of the RNN.mp4125.49MB

9. Step 8 - Implementing the MDN-RNN/7. Building the MDN - Getting the Input, Hidden Layer and Output of the MDN.mp4146.98MB

9. Step 8 - Implementing the MDN-RNN/8. Building the MDN - Getting the MDN parameters.mp4109.44MB

9. Step 8 - Implementing the MDN-RNN/9. Implementing the Training operations (Part 1).mp4177.44MB