[FreeTutorials.Us] Udemy - Artificial Intelligence Masterclass

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  • 12. The Final Run/1. The Whole Implementation.mp4 273.65 MB
    1. Introduction/2. Introduction + Course Structure + Demo.mp4 195.34 MB
    3. Step 2 - Convolutional Neural Network/8. Step 4 - Full Connection.mp4 194.26 MB
    7. Step 6 - Recurrent Neural Network/6. LSTM Practical Intuition.mp4 187.41 MB
    6. Step 5 - Implementing the CNN-VAE/7. Implementing the Training operations.mp4 186.98 MB
    9. Step 8 - Implementing the MDN-RNN/9. Implementing the Training operations (Part 1).mp4 177.44 MB
    9. Step 8 - Implementing the MDN-RNN/10. Implementing the Training operations (Part 2).mp4 162.89 MB
    12. The Final Run/3. Installing the required packages.mp4 158.71 MB
    10. Step 9 - Reinforcement Learning/3. A Pseudo Implementation of Reinforcement Learning for the Full World Model.mp4 154.25 MB
    11. Step 10 - Deep NeuroEvolution/4. Genetic Algorithms.mp4 149.11 MB
    9. Step 8 - Implementing the MDN-RNN/7. Building the MDN - Getting the Input, Hidden Layer and Output of the MDN.mp4 146.98 MB
    11. Step 10 - Deep NeuroEvolution/5. Covariance-Matrix Adaptation Evolution Strategy (CMA-ES).mp4 144.06 MB
    11. Step 10 - Deep NeuroEvolution/6. Parameter-Exploring Policy Gradients (PEPG).mp4 143.9 MB
    3. Step 2 - Convolutional Neural Network/6. Step 2 - Pooling.mp4 140.17 MB
    7. Step 6 - Recurrent Neural Network/5. LSTMs.mp4 136.52 MB
    1. Introduction/4. Your Three Best Resources.mp4 134.49 MB
    6. Step 5 - Implementing the CNN-VAE/4. Building the Encoder part of the VAE.mp4 133.64 MB
    9. Step 8 - Implementing the MDN-RNN/5. Building the RNN - Setting up the Input, Target, and Output of the RNN.mp4 131.12 MB
    9. Step 8 - Implementing the MDN-RNN/4. Building the RNN - Creating an LSTM cell with Dropout.mp4 127.16 MB
    9. Step 8 - Implementing the MDN-RNN/6. Building the RNN - Getting the Deterministic Output of the RNN.mp4 125.49 MB
    12. The Final Run/4. The Final Race Human Intelligence vs. Artificial Intelligence.mp4 125.09 MB
    7. Step 6 - Recurrent Neural Network/3. What are Recurrent Neural Networks.mp4 121.09 MB
    11. Step 10 - Deep NeuroEvolution/3. Evolution Strategies.mp4 119.44 MB
    3. Step 2 - Convolutional Neural Network/10. Softmax & Cross-Entropy.mp4 117.97 MB
    2. Step 1 - Artificial Neural Network/6. How do Neural Networks learn.mp4 112.11 MB
    7. Step 6 - Recurrent Neural Network/4. The Vanishing Gradient Problem.mp4 111.17 MB
    9. Step 8 - Implementing the MDN-RNN/8. Building the MDN - Getting the MDN parameters.mp4 109.44 MB
    11. Step 10 - Deep NeuroEvolution/2. Deep NeuroEvolution.mp4 108.84 MB
    11. Step 10 - Deep NeuroEvolution/7. OpenAI Evolution Strategy.mp4 108.09 MB
    3. Step 2 - Convolutional Neural Network/3. What are Convolutional Neural Networks.mp4 107.97 MB
    9. Step 8 - Implementing the MDN-RNN/2. Initializing all the parameters and variables of the MDN-RNN class.mp4 99.49 MB
    2. Step 1 - Artificial Neural Network/3. The Neuron.mp4 98.79 MB
    3. Step 2 - Convolutional Neural Network/4. Step 1 - The Convolution Operation.mp4 97.93 MB
    4. Step 3 - AutoEncoder/3. What are AutoEncoders.mp4 94.61 MB
    6. Step 5 - Implementing the CNN-VAE/6. Building the Decoder part of the VAE.mp4 92.89 MB
    8. Step 7 - Mixture Density Network/2. Introduction to the MDN-RNN.mp4 83.39 MB
    2. Step 1 - Artificial Neural Network/5. How do Neural Networks work.mp4 81.94 MB
    6. Step 5 - Implementing the CNN-VAE/5. Building the V part of the VAE.mp4 80.33 MB
    9. Step 8 - Implementing the MDN-RNN/3. Building the RNN - Gathering the parameters.mp4 76.58 MB
    5. Step 4 - Variational AutoEncoder/2. Introduction to the VAE.mp4 72.81 MB
    6. Step 5 - Implementing the CNN-VAE/3. Initializing all the parameters and variables of the CNN-VAE class.mp4 71.72 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 67.29 MB
    8. Step 7 - Mixture Density Network/3. Mixture Density Networks.mp4 65.35 MB
    2. Step 1 - Artificial Neural Network/7. Gradient Descent.mp4 60.62 MB
    6. Step 5 - Implementing the CNN-VAE/2. Introduction to Step 5.mp4 58.85 MB
    4. Step 3 - AutoEncoder/7. Sparse AutoEncoders.mp4 57.45 MB
    3. Step 2 - Convolutional Neural Network/5. Step 1 Bis - The ReLU Layer.mp4 53.44 MB
    4. Step 3 - AutoEncoder/5. Training an AutoEncoder.mp4 50.3 MB
    2. Step 1 - Artificial Neural Network/4. The Activation Function.mp4 45.36 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 43.14 MB
    3. Step 2 - Convolutional Neural Network/9. Summary.mp4 30.33 MB
    12. The Final Run/5. THANK YOU bonus video.mp4 29.21 MB
    4. Step 3 - AutoEncoder/6. Overcomplete Hidden Layers.mp4 28.06 MB
    5. Step 4 - Variational AutoEncoder/4. Reparameterization Trick.mp4 26.41 MB
    5. Step 4 - Variational AutoEncoder/3. Variational AutoEncoders.mp4 26.31 MB
    4. Step 3 - AutoEncoder/8. Denoising AutoEncoders.mp4 24.1 MB
    1. Introduction/1. Updates on Udemy Reviews.mp4 22.03 MB
    3. Step 2 - Convolutional Neural Network/2. Plan of Attack.mp4 21.81 MB
    4. Step 3 - AutoEncoder/9. Contractive AutoEncoders.mp4 20.55 MB
    7. Step 6 - Recurrent Neural Network/7. LSTM Variations.mp4 20.12 MB
    12. The Final Run/2.1 AI Masterclass.zip.zip 17.05 MB
    4. Step 3 - AutoEncoder/10. Stacked AutoEncoders.mp4 16.44 MB
    2. Step 1 - Artificial Neural Network/2. Plan of Attack.mp4 15.85 MB
    4. Step 3 - AutoEncoder/2. Plan of Attack.mp4 15.85 MB
    4. Step 3 - AutoEncoder/11. Deep AutoEncoders.mp4 11.97 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 8.61 MB
    3. Step 2 - Convolutional Neural Network/7. Step 3 - Flattening.mp4 7.94 MB
    3. Step 2 - Convolutional Neural Network/8. Step 4 - Full Connection.srt 28.47 KB
    12. The Final Run/1. The Whole Implementation.srt 28.35 KB
    7. Step 6 - Recurrent Neural Network/5. LSTMs.srt 28.24 KB
    10. Step 9 - Reinforcement Learning/3. A Pseudo Implementation of Reinforcement Learning for the Full World Model.srt 26.98 KB
    6. Step 5 - Implementing the CNN-VAE/4. Building the Encoder part of the VAE.srt 26.15 KB
    3. Step 2 - Convolutional Neural Network/10. Softmax & Cross-Entropy.srt 25.29 KB
    3. Step 2 - Convolutional Neural Network/8. Step 4 - Full Connection.vtt 25.03 KB
    12. The Final Run/1. The Whole Implementation.vtt 24.9 KB
    2. Step 1 - Artificial Neural Network/3. The Neuron.srt 24.65 KB
    7. Step 6 - Recurrent Neural Network/5. LSTMs.vtt 24.65 KB
    7. Step 6 - Recurrent Neural Network/3. What are Recurrent Neural Networks.srt 23.84 KB
    10. Step 9 - Reinforcement Learning/3. A Pseudo Implementation of Reinforcement Learning for the Full World Model.vtt 23.52 KB
    6. Step 5 - Implementing the CNN-VAE/7. Implementing the Training operations.srt 23.44 KB
    3. Step 2 - Convolutional Neural Network/4. Step 1 - The Convolution Operation.srt 23.26 KB
    6. Step 5 - Implementing the CNN-VAE/4. Building the Encoder part of the VAE.vtt 22.76 KB
    3. Step 2 - Convolutional Neural Network/3. What are Convolutional Neural Networks.srt 22.18 KB
    3. Step 2 - Convolutional Neural Network/10. Softmax & Cross-Entropy.vtt 22.15 KB
    1. Introduction/2. Introduction + Course Structure + Demo.srt 21.94 KB
    9. Step 8 - Implementing the MDN-RNN/4. Building the RNN - Creating an LSTM cell with Dropout.srt 21.85 KB
    2. Step 1 - Artificial Neural Network/3. The Neuron.vtt 21.63 KB
    3. Step 2 - Convolutional Neural Network/6. Step 2 - Pooling.srt 21 KB
    7. Step 6 - Recurrent Neural Network/6. LSTM Practical Intuition.srt 20.98 KB
    7. Step 6 - Recurrent Neural Network/3. What are Recurrent Neural Networks.vtt 20.83 KB
    7. Step 6 - Recurrent Neural Network/4. The Vanishing Gradient Problem.srt 20.79 KB
    9. Step 8 - Implementing the MDN-RNN/9. Implementing the Training operations (Part 1).srt 20.46 KB
    3. Step 2 - Convolutional Neural Network/4. Step 1 - The Convolution Operation.vtt 20.42 KB
    6. Step 5 - Implementing the CNN-VAE/7. Implementing the Training operations.vtt 20.37 KB
    9. Step 8 - Implementing the MDN-RNN/5. Building the RNN - Setting up the Input, Target, and Output of the RNN.srt 20.02 KB
    3. Step 2 - Convolutional Neural Network/3. What are Convolutional Neural Networks.vtt 19.42 KB
    9. Step 8 - Implementing the MDN-RNN/4. Building the RNN - Creating an LSTM cell with Dropout.vtt 19.19 KB
    1. Introduction/2. Introduction + Course Structure + Demo.vtt 19.18 KB
    2. Step 1 - Artificial Neural Network/5. How do Neural Networks work.srt 19.05 KB
    2. Step 1 - Artificial Neural Network/6. How do Neural Networks learn.srt 18.96 KB
    9. Step 8 - Implementing the MDN-RNN/10. Implementing the Training operations (Part 2).srt 18.9 KB
    3. Step 2 - Convolutional Neural Network/6. Step 2 - Pooling.vtt 18.39 KB
    7. Step 6 - Recurrent Neural Network/6. LSTM Practical Intuition.vtt 18.36 KB
    7. Step 6 - Recurrent Neural Network/4. The Vanishing Gradient Problem.vtt 18.26 KB
    10. Step 9 - Reinforcement Learning/2. What is Reinforcement Learning.srt 18.12 KB
    9. Step 8 - Implementing the MDN-RNN/2. Initializing all the parameters and variables of the MDN-RNN class.srt 17.99 KB
    9. Step 8 - Implementing the MDN-RNN/9. Implementing the Training operations (Part 1).vtt 17.79 KB
    9. Step 8 - Implementing the MDN-RNN/5. Building the RNN - Setting up the Input, Target, and Output of the RNN.vtt 17.7 KB
    11. Step 10 - Deep NeuroEvolution/4. Genetic Algorithms.srt 17.66 KB
    12. The Final Run/3. Installing the required packages.srt 17.53 KB
    11. Step 10 - Deep NeuroEvolution/5. Covariance-Matrix Adaptation Evolution Strategy (CMA-ES).srt 17.19 KB
    6. Step 5 - Implementing the CNN-VAE/3. Initializing all the parameters and variables of the CNN-VAE class.srt 16.95 KB
    2. Step 1 - Artificial Neural Network/5. How do Neural Networks work.vtt 16.81 KB
    9. Step 8 - Implementing the MDN-RNN/7. Building the MDN - Getting the Input, Hidden Layer and Output of the MDN.srt 16.55 KB
    2. Step 1 - Artificial Neural Network/6. How do Neural Networks learn.vtt 16.52 KB
    9. Step 8 - Implementing the MDN-RNN/10. Implementing the Training operations (Part 2).vtt 16.42 KB
    11. Step 10 - Deep NeuroEvolution/6. Parameter-Exploring Policy Gradients (PEPG).srt 16.39 KB
    9. Step 8 - Implementing the MDN-RNN/6. Building the RNN - Getting the Deterministic Output of the RNN.srt 16.37 KB
    4. Step 3 - AutoEncoder/3. What are AutoEncoders.srt 16.3 KB
    10. Step 9 - Reinforcement Learning/2. What is Reinforcement Learning.vtt 15.98 KB
    12. The Final Run/4. The Final Race Human Intelligence vs. Artificial Intelligence.srt 15.83 KB
    9. Step 8 - Implementing the MDN-RNN/2. Initializing all the parameters and variables of the MDN-RNN class.vtt 15.81 KB
    11. Step 10 - Deep NeuroEvolution/4. Genetic Algorithms.vtt 15.47 KB
    11. Step 10 - Deep NeuroEvolution/5. Covariance-Matrix Adaptation Evolution Strategy (CMA-ES).vtt 15.17 KB
    11. Step 10 - Deep NeuroEvolution/2. Deep NeuroEvolution.srt 15.08 KB
    12. The Final Run/3. Installing the required packages.vtt 14.97 KB
    6. Step 5 - Implementing the CNN-VAE/3. Initializing all the parameters and variables of the CNN-VAE class.vtt 14.87 KB
    9. Step 8 - Implementing the MDN-RNN/7. Building the MDN - Getting the Input, Hidden Layer and Output of the MDN.vtt 14.65 KB
    11. Step 10 - Deep NeuroEvolution/6. Parameter-Exploring Policy Gradients (PEPG).vtt 14.53 KB
    9. Step 8 - Implementing the MDN-RNN/8. Building the MDN - Getting the MDN parameters.srt 14.51 KB
    9. Step 8 - Implementing the MDN-RNN/6. Building the RNN - Getting the Deterministic Output of the RNN.vtt 14.37 KB
    4. Step 3 - AutoEncoder/3. What are AutoEncoders.vtt 14.31 KB
    2. Step 1 - Artificial Neural Network/7. Gradient Descent.srt 14.17 KB
    8. Step 7 - Mixture Density Network/3. Mixture Density Networks.srt 13.54 KB
    12. The Final Run/4. The Final Race Human Intelligence vs. Artificial Intelligence.vtt 13.48 KB
    6. Step 5 - Implementing the CNN-VAE/5. Building the V part of the VAE.srt 13.46 KB
    11. Step 10 - Deep NeuroEvolution/2. Deep NeuroEvolution.vtt 13.35 KB
    1. Introduction/4. Your Three Best Resources.srt 13.32 KB
    6. Step 5 - Implementing the CNN-VAE/6. Building the Decoder part of the VAE.srt 13.04 KB
    11. Step 10 - Deep NeuroEvolution/3. Evolution Strategies.srt 12.97 KB
    9. Step 8 - Implementing the MDN-RNN/3. Building the RNN - Gathering the parameters.srt 12.89 KB
    9. Step 8 - Implementing the MDN-RNN/8. Building the MDN - Getting the MDN parameters.vtt 12.8 KB
    8. Step 7 - Mixture Density Network/2. Introduction to the MDN-RNN.srt 12.69 KB
    2. Step 1 - Artificial Neural Network/7. Gradient Descent.vtt 12.35 KB
    2. Step 1 - Artificial Neural Network/8. Stochastic Gradient Descent.srt 12.19 KB
    8. Step 7 - Mixture Density Network/3. Mixture Density Networks.vtt 11.95 KB
    1. Introduction/4. Your Three Best Resources.vtt 11.81 KB
    2. Step 1 - Artificial Neural Network/4. The Activation Function.srt 11.8 KB
    6. Step 5 - Implementing the CNN-VAE/5. Building the V part of the VAE.vtt 11.79 KB
    6. Step 5 - Implementing the CNN-VAE/6. Building the Decoder part of the VAE.vtt 11.43 KB
    11. Step 10 - Deep NeuroEvolution/3. Evolution Strategies.vtt 11.38 KB
    9. Step 8 - Implementing the MDN-RNN/3. Building the RNN - Gathering the parameters.vtt 11.33 KB
    8. Step 7 - Mixture Density Network/2. Introduction to the MDN-RNN.vtt 11.19 KB
    5. Step 4 - Variational AutoEncoder/2. Introduction to the VAE.srt 11 KB
    9. Step 8 - Implementing the MDN-RNN/11. Full Code Section.html 10.8 KB
    2. Step 1 - Artificial Neural Network/8. Stochastic Gradient Descent.vtt 10.77 KB
    6. Step 5 - Implementing the CNN-VAE/2. Introduction to Step 5.srt 10.75 KB
    2. Step 1 - Artificial Neural Network/4. The Activation Function.vtt 10.41 KB
    11. Step 10 - Deep NeuroEvolution/7. OpenAI Evolution Strategy.srt 10.36 KB
    5. Step 4 - Variational AutoEncoder/2. Introduction to the VAE.vtt 9.67 KB
    4. Step 3 - AutoEncoder/5. Training an AutoEncoder.srt 9.54 KB
    6. Step 5 - Implementing the CNN-VAE/2. Introduction to Step 5.vtt 9.42 KB
    3. Step 2 - Convolutional Neural Network/5. Step 1 Bis - The ReLU Layer.srt 9.31 KB
    11. Step 10 - Deep NeuroEvolution/7. OpenAI Evolution Strategy.vtt 9.16 KB
    4. Step 3 - AutoEncoder/7. Sparse AutoEncoders.srt 8.78 KB
    4. Step 3 - AutoEncoder/5. Training an AutoEncoder.vtt 8.37 KB
    3. Step 2 - Convolutional Neural Network/5. Step 1 Bis - The ReLU Layer.vtt 8.16 KB
    4. Step 3 - AutoEncoder/7. Sparse AutoEncoders.vtt 7.81 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.srt 7.55 KB
    2. Step 1 - Artificial Neural Network/9. Backpropagation.srt 7.29 KB
    8. Step 7 - Mixture Density Network/4. VAE + MDN-RNN Visualization.vtt 6.65 KB
    5. Step 4 - Variational AutoEncoder/4. Reparameterization Trick.srt 6.58 KB
    2. Step 1 - Artificial Neural Network/9. Backpropagation.vtt 6.44 KB
    5. Step 4 - Variational AutoEncoder/3. Variational AutoEncoders.srt 6.14 KB
    3. Step 2 - Convolutional Neural Network/9. Summary.srt 6.08 KB
    5. Step 4 - Variational AutoEncoder/4. Reparameterization Trick.vtt 5.8 KB
    4. Step 3 - AutoEncoder/6. Overcomplete Hidden Layers.srt 5.66 KB
    5. Step 4 - Variational AutoEncoder/3. Variational AutoEncoders.vtt 5.43 KB
    3. Step 2 - Convolutional Neural Network/9. Summary.vtt 5.37 KB
    3. Step 2 - Convolutional Neural Network/2. Plan of Attack.srt 5.34 KB
    9. Step 8 - Implementing the MDN-RNN/12. The Keras Implementation.html 5.26 KB
    4. Step 3 - AutoEncoder/6. Overcomplete Hidden Layers.vtt 4.99 KB
    7. Step 6 - Recurrent Neural Network/7. LSTM Variations.srt 4.87 KB
    3. Step 2 - Convolutional Neural Network/2. Plan of Attack.vtt 4.69 KB
    7. Step 6 - Recurrent Neural Network/7. LSTM Variations.vtt 4.28 KB
    6. Step 5 - Implementing the CNN-VAE/8. Full Code Section.html 3.96 KB
    2. Step 1 - Artificial Neural Network/2. Plan of Attack.srt 3.94 KB
    4. Step 3 - AutoEncoder/8. Denoising AutoEncoders.srt 3.64 KB
    4. Step 3 - AutoEncoder/9. Contractive AutoEncoders.srt 3.57 KB
    2. Step 1 - Artificial Neural Network/2. Plan of Attack.vtt 3.5 KB
    1. Introduction/1. Updates on Udemy Reviews.srt 3.48 KB
    7. Step 6 - Recurrent Neural Network/2. Plan of Attack.srt 3.43 KB
    4. Step 3 - AutoEncoder/8. Denoising AutoEncoders.vtt 3.22 KB
    4. Step 3 - AutoEncoder/2. Plan of Attack.srt 3.22 KB
    4. Step 3 - AutoEncoder/9. Contractive AutoEncoders.vtt 3.14 KB
    7. Step 6 - Recurrent Neural Network/2. Plan of Attack.vtt 3.07 KB
    1. Introduction/1. Updates on Udemy Reviews.vtt 3.05 KB
    4. Step 3 - AutoEncoder/2. Plan of Attack.vtt 2.87 KB
    9. Step 8 - Implementing the MDN-RNN/1. Welcome to Step 8 - Implementing the MDN-RNN.html 2.81 KB
    4. Step 3 - AutoEncoder/11. Deep AutoEncoders.srt 2.72 KB
    3. Step 2 - Convolutional Neural Network/7. Step 3 - Flattening.srt 2.57 KB
    4. Step 3 - AutoEncoder/10. Stacked AutoEncoders.srt 2.41 KB
    4. Step 3 - AutoEncoder/11. Deep AutoEncoders.vtt 2.39 KB
    1. Introduction/3. BONUS Learning Paths.html 2.37 KB
    12. The Final Run/5. THANK YOU bonus video.srt 2.32 KB
    6. Step 5 - Implementing the CNN-VAE/1. Welcome to Step 5 - Implementing the CNN-VAE.html 2.3 KB
    3. Step 2 - Convolutional Neural Network/7. Step 3 - Flattening.vtt 2.29 KB
    4. Step 3 - AutoEncoder/10. Stacked AutoEncoders.vtt 2.14 KB
    4. Step 3 - AutoEncoder/4. A Note on Biases.srt 2.09 KB
    12. The Final Run/5. THANK YOU bonus video.vtt 2.04 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 1.22 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 1.02 KB
    1. Introduction/5. Download the Resources here.html 790 B
    1. Introduction/6. Meet your instructors!.html 723 B
    2. Step 1 - Artificial Neural Network/1. Welcome to Step 1 - Artificial Neural Network.html 605 B
    8. Step 7 - Mixture Density Network/1. Welcome to Step 7 - Mixture Density Network.html 517 B
    7. Step 6 - Recurrent Neural Network/1. Welcome to Step 6 - Recurrent Neural Network.html 507 B
    3. Step 2 - Convolutional Neural Network/1. Welcome to Step 2 - Convolutional Neural Network.html 430 B
    10. Step 9 - Reinforcement Learning/1. Welcome to Step 9 - Reinforcement Learning.html 424 B
    5. Step 4 - Variational AutoEncoder/1. Welcome to Step 4 - Variational AutoEncoder.html 423 B
    4. Step 3 - AutoEncoder/1. Welcome to Step 3 - AutoEncoder.html 418 B
    10. Step 9 - Reinforcement Learning/4. Full Code Section.html 393 B
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