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[FreeCourseSite com] Udemy - Artificial Intelligence Masterclass
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Torrent Files List
1. Introduction (Size: 6.17 GB) (Files: 158)
1. Introduction
1. Updates on Udemy Reviews.mp4
1. Updates on Udemy Reviews.vtt
2. Introduction + Course Structure + Demo.mp4
2. Introduction + Course Structure + Demo.vtt
3. Your Three Best Resources.mp4
3. Your Three Best Resources.vtt
4. Download the Resources here.html
5. Meet your instructors!.html
10. Step 9 - Reinforcement Learning
1. Welcome to Step 9 - Reinforcement Learning.html
2. What is Reinforcement Learning.mp4
2. What is Reinforcement Learning.vtt
3. A Pseudo Implementation of Reinforcement Learning for the Full World Model.mp4
3. A Pseudo Implementation of Reinforcement Learning for the Full World Model.vtt
4. Full Code Section.html
11. Step 10 - Deep NeuroEvolution
1. Welcome to Step 10 - Deep NeuroEvolution.html
2. Deep NeuroEvolution.mp4
2. Deep NeuroEvolution.vtt
3. Evolution Strategies.mp4
3. Evolution Strategies.vtt
4. Genetic Algorithms.mp4
4. Genetic Algorithms.vtt
5. Covariance-Matrix Adaptation Evolution Strategy (CMA-ES).mp4
5. Covariance-Matrix Adaptation Evolution Strategy (CMA-ES).vtt
6. Parameter-Exploring Policy Gradients (PEPG).mp4
6. Parameter-Exploring Policy Gradients (PEPG).vtt
7. OpenAI Evolution Strategy.mp4
7. OpenAI Evolution Strategy.vtt
12. The Final Run
1. The Whole Implementation.mp4
1. The Whole Implementation.vtt
2. Download the whole AI Masterclass folder here.html
2.1 AI_Masterclass.zip.zip
3. Installing the required packages.mp4
3. Installing the required packages.vtt
4. The Final Race Human Intelligence vs. Artificial Intelligence.mp4
4. The Final Race Human Intelligence vs. Artificial Intelligence.vtt
2. Step 1 - Artificial Neural Network
1. Welcome to Step 1 - Artificial Neural Network.html
2. Plan of Attack.mp4
2. Plan of Attack.vtt
3. The Neuron.mp4
3. The Neuron.vtt
4. The Activation Function.mp4
4. The Activation Function.vtt
5. How do Neural Networks work.mp4
5. How do Neural Networks work.vtt
6. How do Neural Networks learn.mp4
6. How do Neural Networks learn.vtt
7. Gradient Descent.mp4
7. Gradient Descent.vtt
8. Stochastic Gradient Descent.mp4
8. Stochastic Gradient Descent.vtt
9. Backpropagation.mp4
9. Backpropagation.vtt
3. Step 2 - Convolutional Neural Network
1. Welcome to Step 2 - Convolutional Neural Network.html
10. Softmax & Cross-Entropy.mp4
10. Softmax & Cross-Entropy.vtt
2. Plan of Attack.mp4
2. Plan of Attack.vtt
3. What are Convolutional Neural Networks.mp4
3. What are Convolutional Neural Networks.vtt
4. Step 1 - The Convolution Operation.mp4
4. Step 1 - The Convolution Operation.vtt
5. Step 1 Bis - The ReLU Layer.mp4
5. Step 1 Bis - The ReLU Layer.vtt
6. Step 2 - Pooling.mp4
6. Step 2 - Pooling.vtt
7. Step 3 - Flattening.mp4
7. Step 3 - Flattening.vtt
8. Step 4 - Full Connection.mp4
8. Step 4 - Full Connection.vtt
9. Summary.mp4
9. Summary.vtt
4. Step 3 - AutoEncoder
1. Welcome to Step 3 - AutoEncoder.html
10. Stacked AutoEncoders.mp4
10. Stacked AutoEncoders.vtt
11. Deep AutoEncoders.mp4
11. Deep AutoEncoders.vtt
2. Plan of Attack.mp4
2. Plan of Attack.vtt
3. What are AutoEncoders.mp4
3. What are AutoEncoders.vtt
4. A Note on Biases.mp4
4. A Note on Biases.vtt
5. Training an AutoEncoder.mp4
5. Training an AutoEncoder.vtt
6. Overcomplete Hidden Layers.mp4
6. Overcomplete Hidden Layers.vtt
7. Sparse AutoEncoders.mp4
7. Sparse AutoEncoders.vtt
8. Denoising AutoEncoders.mp4
8. Denoising AutoEncoders.vtt
9. Contractive AutoEncoders.mp4
9. Contractive AutoEncoders.vtt
5. Step 4 - Variational AutoEncoder
1. Welcome to Step 4 - Variational AutoEncoder.html
2. Introduction to the VAE.mp4
2. Introduction to the VAE.vtt
3. Variational AutoEncoders.mp4
3. Variational AutoEncoders.vtt
4. Reparameterization Trick.mp4
4. Reparameterization Trick.vtt
6. Step 5 - Implementing the CNN-VAE
1. Welcome to Step 5 - Implementing the CNN-VAE.html
2. Introduction to Step 5.mp4
2. Introduction to Step 5.vtt
3. Initializing all the parameters and variables of the CNN-VAE class.mp4
3. Initializing all the parameters and variables of the CNN-VAE class.vtt
4. Building the Encoder part of the VAE.mp4
4. Building the Encoder part of the VAE.vtt
5. Building the V part of the VAE.mp4
5. Building the V part of the VAE.vtt
6. Building the Decoder part of the VAE.mp4
6. Building the Decoder part of the VAE.vtt
7. Implementing the Training operations.mp4
7. Implementing the Training operations.vtt
8. Full Code Section.html
9. The Keras Implementation.html
7. Step 6 - Recurrent Neural Network
1. Welcome to Step 6 - Recurrent Neural Network.html
2. Plan of Attack.mp4
2. Plan of Attack.vtt
3. What are Recurrent Neural Networks.mp4
3. What are Recurrent Neural Networks.vtt
4. The Vanishing Gradient Problem.mp4
4. The Vanishing Gradient Problem.vtt
5. LSTMs.mp4
5. LSTMs.vtt
6. LSTM Practical Intuition.mp4
6. LSTM Practical Intuition.vtt
7. LSTM Variations.mp4
7. LSTM Variations.vtt
8. Step 7 - Mixture Density Network
1. Welcome to Step 7 - Mixture Density Network.html
2. Introduction to the MDN-RNN.mp4
2. Introduction to the MDN-RNN.vtt
3. Mixture Density Networks.mp4
3. Mixture Density Networks.vtt
4. VAE + MDN-RNN Visualization.mp4
4. VAE + MDN-RNN Visualization.vtt
9. Step 8 - Implementing the MDN-RNN
1. Welcome to Step 8 - Implementing the MDN-RNN.html
10. Implementing the Training operations (Part 2).mp4
10. Implementing the Training operations (Part 2).vtt
11. Full Code Section.html
12. The Keras Implementation.html
2. Initializing all the parameters and variables of the MDN-RNN class.mp4
2. Initializing all the parameters and variables of the MDN-RNN class.vtt
3. Building the RNN - Gathering the parameters.mp4
3. Building the RNN - Gathering the parameters.vtt
4. Building the RNN - Creating an LSTM cell with Dropout.mp4
4. Building the RNN - Creating an LSTM cell with Dropout.vtt
5. Building the RNN - Setting up the Input, Target, and Output of the RNN.mp4
5. Building the RNN - Setting up the Input, Target, and Output of the RNN.vtt
6. Building the RNN - Getting the Deterministic Output of the RNN.mp4
6. Building the RNN - Getting the Deterministic Output of the RNN.vtt
7. Building the MDN - Getting the Input, Hidden Layer and Output of the MDN.mp4
7. Building the MDN - Getting the Input, Hidden Layer and Output of the MDN.vtt
8. Building the MDN - Getting the MDN parameters.mp4
8. Building the MDN - Getting the MDN parameters.vtt
9. Implementing the Training operations (Part 1).mp4
9. Implementing the Training operations (Part 1).vtt
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