[FreeTutorials.Us] Udemy - Artificial Intelligence Masterclass

磁力链接/BT种子名称

[FreeTutorials.Us] Udemy - Artificial Intelligence Masterclass

磁力链接/BT种子简介

种子哈希：0cbfbe4bdd220e48976639ff1b095c571a8a26c6
文件大小： 6.12G
已经下载：2013次
下载速度：极快
收录时间：2021-05-02
最近下载：2025-08-04

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文件列表

  • 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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